Nothing
R/family.actuary.R
In VGAM: Vector Generalized Linear and Additive Models
Defines functions
inv.paralogistic
paralogistic
inv.lomax
fisk
lomax
betaII
dagum
sinmad
dinv.paralogistic
dinv.lomax
ddagum
dparalogistic
dfisk
dlomax
dsinmad
dbetaII
pinv.paralogistic
pinv.lomax
pdagum
pparalogistic
pfisk
plomax
psinmad
qinv.paralogistic
qinv.lomax
qdagum
qparalogistic
qfisk
qlomax
qsinmad
rinv.paralogistic
rinv.lomax
rdagum
rparalogistic
rfisk
rlomax
rsinmad
dgenbetaII
genbetaII
genbetaII.Loglikfun4
exponential.mo
exponential.mo.control
gompertz
gompertz.control
rgompertz
qgompertz
pgompertz
dgompertz
makeham
makeham.control
rmakeham
qmakeham
pmakeham
dmakeham
perks
perks.control
rperks
qperks
pperks
dperks
qbeard
dbeard
dbeard
pmperks
dmperks
pmbeard
dmbeard
gumbelII
rgumbelII
qgumbelII
pgumbelII
dgumbelII
Documented in
betaII
dagum
dbetaII
ddagum
dfisk
dgenbetaII
dgompertz
dgumbelII
dinv.lomax
dinv.paralogistic
dlomax
dmakeham
dparalogistic
dperks
dsinmad
fisk
genbetaII
genbetaII.Loglikfun4
gompertz
gumbelII
inv.lomax
inv.paralogistic
lomax
makeham
paralogistic
pdagum
perks
pfisk
pgompertz
pgumbelII
pinv.lomax
pinv.paralogistic
plomax
pmakeham
pparalogistic
pperks
psinmad
qdagum
qfisk
qgompertz
qgumbelII
qinv.lomax
qinv.paralogistic
qlomax
qmakeham
qparalogistic
qperks
qsinmad
rdagum
rfisk
rgompertz
rgumbelII
rinv.lomax
rinv.paralogistic
rlomax
rmakeham
rparalogistic
rperks
rsinmad
sinmad
# These functions are
# Copyright (C) 1998-2023 T.W. Yee, University of Auckland.
# All rights reserved.
dgumbelII <- function(x, scale = 1, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
ans <- x
index0 <- (x < 0) & is.finite(x) & !is.na(x)
ans[!index0] <- log(shape[!index0] / scale[!index0]) +
(shape[!index0] + 1) * log(scale[!index0] / x[!index0]) -
(x[!index0] / scale[!index0])^(-shape[!index0])
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
}
pgumbelII <- function(q, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
# 20150121 KaiH
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
# 20150121 KaiH
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
# 20150121 KaiH
if (lower.tail) {
if (log.p) {
ans <- -(q / scale)^(-shape)
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- exp(-(q / scale)^(-shape))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- log(-expm1(-(q / scale)^(-shape)))
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- -expm1(-(q / scale)^(-shape))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
}
qgumbelII <- function(p, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- scale * (-ln.p)^(-1 / shape)
ans[ln.p > 0] <- NaN
} else { # Default
ans <- scale * (-log(p))^(-1 / shape)
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- scale * (-log(-expm1(ln.p)))^(-1 / shape)
ans[ln.p > 0] <- NaN
} else {
ans <- scale * (-log1p(-p))^(-1 / shape)
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
}
rgumbelII <- function(n, scale = 1, shape) {
qgumbelII(runif(n), shape = shape, scale = scale)
}
gumbelII <-
function(lscale = "loglink", lshape = "loglink",
iscale = NULL, ishape = NULL,
probs.y = c(0.2, 0.5, 0.8),
perc.out = NULL, # 50,
imethod = 1, zero = "shape", nowarning = FALSE) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 2)
stop("argument 'imethod' must be 1 or 2")
if (!is.Numeric(probs.y, positive = TRUE) ||
length(probs.y) < 2 ||
max(probs.y) >= 1)
stop("bad input for argument 'probs.y'")
if (length(perc.out))
if (!is.Numeric(perc.out, positive = TRUE) ||
max(probs.y) >= 100)
stop("bad input for argument 'perc.out'")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
new("vglmff",
blurb = c("Gumbel Type II distribution\n\n",
"Links: ",
namesof("scale", lscale, escale), ", ",
namesof("shape", lshape, eshape), "\n",
"Mean: scale^(1/shape) * gamma(1 - 1 / shape)\n",
"Variance: scale^(2/shape) * (gamma(1 - 2/shape) - ",
"gamma(1 + 1/shape)^2)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "gumbelII",
parameters.names = c("scale", "shape"),
perc.out = .perc.out ,
zero = .zero )
}, list( .zero = zero,
.perc.out = perc.out
))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
Shape.init <- matrix(if (length( .ishape ))
.ishape else 0 + NA,
n, ncoly, byrow = TRUE)
Scale.init <- matrix(if (length( .iscale ))
.iscale else 0 + NA,
n, ncoly, byrow = TRUE)
if (!length(etastart)) {
if (!length( .ishape ) ||
!length( .iscale )) {
for (ilocal in 1:ncoly) {
anyc <- FALSE # extra$leftcensored|extra$rightcensored
i11 <- if ( .imethod == 1)
anyc else FALSE # can be all data
probs.y <- .probs.y
xvec <- log(-log(probs.y))
fit0 <- lsfit(y = xvec,
x = log(quantile(y[!i11, ilocal],
probs = probs.y )))
if (!is.Numeric(Shape.init[, ilocal]))
Shape.init[, ilocal] <- -fit0$coef["X"]
if (!is.Numeric(Scale.init[, ilocal]))
Scale.init[, ilocal] <-
exp(fit0$coef["Intercept"] / Shape.init[, ilocal])
} # ilocal
etastart <-
cbind(theta2eta(Scale.init, .lscale , .escale ),
theta2eta(Shape.init, .lshape , .eshape ))[,
interleave.VGAM(M, M1 = M1)]
}
}
}), list(
.lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.iscale = iscale, .ishape = ishape,
.probs.y = probs.y,
.imethod = imethod ) )),
linkinv = eval(substitute(function(eta, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
Shape <- as.matrix(Shape)
if (length( .perc.out ) > 1 && ncol(Shape) > 1)
stop("argument 'perc.out' should be of length one since ",
"there are multiple responses")
if (!length( .perc.out )) {
return(Scale * gamma(1 - 1 / Shape))
}
ans <- if (length( .perc.out ) > 1) {
qgumbelII(p = matrix( .perc.out / 100, length(Shape),
length( .perc.out ), byrow = TRUE),
shape = Shape, scale = Scale)
} else {
qgumbelII(p = .perc.out / 100, Shape, scale = Scale)
}
colnames(ans) <- paste0(as.character( .perc.out ), "%")
ans
}, list(
.lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.perc.out = perc.out ) )),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[
interleave.VGAM(M, M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape
}
misc$M1 <- M1
misc$imethod <- .imethod
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$perc.out <- .perc.out
misc$true.mu <- FALSE # @fitted is not a true mu
}), list(
.lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.perc.out = perc.out,
.imethod = imethod ) )),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dgumbelII(x = y, shape = Shape,
scale = Scale, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape
) )),
vfamily = c("gumbelII"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(Shape)) && all(0 < Shape)
okay1
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape) )),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
rgumbelII(nsim * length(Scale), shape = Shape, scale = Scale)
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape
) )),
deriv = eval(substitute(expression({
M1 <- 2
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
dl.dshape <- 1 / Shape + log(Scale / y) -
log(Scale / y) * (Scale / y)^Shape
dl.dscale <- Shape / Scale -
(Shape / y) * (Scale / y)^(Shape - 1)
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
dshape.deta <- dtheta.deta(Shape, .lshape , .eshape )
myderiv <- c(w) * cbind(dl.dscale, dl.dshape) *
cbind(dscale.deta, dshape.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape
) )),
weight = eval(substitute(expression({
EulerM <- -digamma(1.0)
ned2l.dshape2 <- (1 + trigamma(2) + digamma(2)^2) / Shape^2
ned2l.dscale2 <- (Shape / Scale)^2
ned2l.dshapescale <- digamma(2) / Scale
wz <- array(c(c(w) * ned2l.dscale2 * dscale.deta^2,
c(w) * ned2l.dshape2 * dshape.deta^2,
c(w) * ned2l.dshapescale * dscale.deta *
dshape.deta),
dim = c(n, M / M1, 3))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale, .lshape = lshape ))))
}
dmbeard <-
function(x, shape, scale = 1, rho, epsilon, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(rho), length(epsilon))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(rho) != LLL) rho <- rep_len(rho, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
index0 <- (x < 0)
ans <- log(epsilon * exp(-x * scale) + shape) +
(-epsilon * x -
((rho * epsilon - 1) / (rho * scale)) *
(log1p(rho * shape) -
log(exp(-x * scale) + rho * shape) - scale * x)) -
log(exp(-x * scale) + shape * rho)
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | rho <= 0 | epsilon <= 0] <- NaN
ans
}
pmbeard <- function(q, shape, scale = 1, rho, epsilon) {
LLL <- max(length(q), length(shape), length(scale),
length(rho), length(epsilon))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(rho) != LLL) rho <- rep_len(rho, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
ans <- -expm1(-epsilon * q -
((rho * epsilon - 1) / (rho * scale)) *
(log1p(rho * shape) -
log(exp(-scale * q) + rho * shape) - scale * q))
ans[(q <= 0)] <- 0
ans[shape <= 0 | scale <= 0 | rho <= 0 | epsilon <= 0] <- NaN
ans[q == Inf] <- 1
ans
}
dmperks <-
function(x, scale = 1, shape, epsilon, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(epsilon))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
index0 <- (x < 0)
ans <- log(epsilon * exp(-x * scale) + shape) +
(-epsilon * x -
((epsilon - 1) / scale) *
(log1p(shape) -
log(shape + exp(-x * scale)) -x * scale)) -
log(exp(-x * scale) + shape)
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | epsilon <= 0] <- NaN
ans
} # dmperks
pmperks <- function(q, scale = 1, shape, epsilon) {
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
ans <- -expm1(-epsilon * q -
((epsilon - 1) / scale) *
(log1p(shape) -
log(shape + exp(-q * scale)) - q * scale))
ans[(q <= 0)] <- 0
ans[shape <= 0 | scale <= 0] <- NaN
ans[q == Inf] <- 1
ans
}
dbeard <- function(x, shape, scale = 1, rho, log = FALSE) {
warning("does not integrate to unity")
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(rho))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(rho) != LLL) rho <- rep_len(rho, LLL)
index0 <- (x < 0)
ans <- log(shape) - x * scale * (rho^(-1 / scale)) +
log(rho) + log(scale) +
(rho^(-1 / scale)) * log1p(shape * rho) -
(1 + rho^(-1 / scale)) *
log(shape * rho + exp(-x * scale))
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | rho <= 0] <- NaN
ans
}
dbeard <- function(x, shape, scale = 1, rho, log = FALSE) {
alpha <- shape
beta <- scale
warning("does not integrate to unity")
ret <- ifelse(x <= 0 | beta <= 0, NaN,
exp(alpha+beta*x)*
(1+exp(alpha+rho))**(exp(-rho/beta)) /
(1+exp(alpha+rho+beta*x))**(1+exp(-rho/beta)))
ret
}
qbeard <- function(x, u = 0.5, alpha = 1, beta = 1,rho = 1) {
ret <-
ifelse(x <= 0 | u <= 0 | u >= 1 |
length(x) != length(u) | beta <= 0,
NaN,
(1/beta) * (log((u**(-beta*exp(rho))) *
(1+exp(alpha+rho+beta*x))-1)-alpha-rho)-x)
return(ret)
}
dperks <- function(x, scale = 1, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
index0 <- (x < 0)
ans <- log(shape) - x +
log1p(shape) / scale -
(1 + 1 / scale) * log(shape + exp(-x * scale))
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # dperks
pperks <- function(q, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
logS <- -q + (log1p(shape) -
log(shape + exp(-q * scale))) / scale
if (lower.tail) {
if (log.p) {
ans <- log(-expm1(logS))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1(logS)
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- logS
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp(logS)
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # pperks
qperks <-
function(p, scale = 1, shape, lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
tmp <- scale * log(-expm1(ln.p))
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[ln.p > 0] <- NaN
} else {
tmp <- scale * log1p(-p)
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
tmp <- scale * ln.p
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[ln.p > 0] <- NaN
} else {
tmp <- scale * log(p)
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # qperks
rperks <- function(n, scale = 1, shape) {
qperks(runif(n), scale = scale, shape = shape)
}
perks.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
perks <-
function(lscale = "loglink", lshape = "loglink",
iscale = NULL, ishape = NULL,
gscale = exp(-5:5), gshape = exp(-5:5),
nsimEIM = 500,
oim.mean = FALSE,
zero = NULL, nowarning = FALSE) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
if (!is.logical(oim.mean) || length(oim.mean) != 1)
stop("bad input for argument 'oim.mean'")
new("vglmff",
blurb = c("Perks' distribution\n\n",
"Links: ",
namesof("scale", lscale, escale), ", ",
namesof("shape", lshape, eshape), "\n",
"Median: qperks(p = 0.5, scale = scale, shape = shape)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "perks",
nsimEIM = .nsimEIM ,
parameters.names = c("scale", "shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matH <- matrix(if (length( .ishape )) .ishape else 0 + NA,
n, ncoly, byrow = TRUE)
matC <- matrix(if (length( .iscale )) .iscale else 0 + NA,
n, ncoly, byrow = TRUE)
shape.grid <- .gshape
scale.grid <- .gscale
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
perks.Loglikfun2 <- function(scaleval, shapeval,
y, x, w, extraargs) {
sum(c(w) * dperks(x = y, shape = shapeval,
scale = scaleval, log = TRUE))
}
try.this <-
grid.search2(scale.grid, shape.grid,
objfun = perks.Loglikfun2,
y = yvec, w = wvec,
ret.objfun = TRUE) # Last value is \ell
if (!length( .iscale ))
matC[, spp.] <- try.this["Value1"]
if (!length( .ishape ))
matH[, spp.] <- try.this["Value2"]
} # spp.
etastart <-
cbind(theta2eta(matC, .lscale , .escale ),
theta2eta(matH, .lshape , .eshape ))[,
interleave.VGAM(M, M1 = M1)]
} # End of !length(etastart)
}), list( .lscale = lscale, .lshape = lshape,
.eshape = eshape, .escale = escale,
.gshape = gshape, .gscale = gscale,
.ishape = ishape, .iscale = iscale
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
qperks(p = 0.5, shape = Shape, scale = Scale)
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
last = eval(substitute(expression({
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M,
M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape
}
misc$M1 <- M1
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dperks(x = y, shape = Shape,
scale = Scale, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
vfamily = c("perks"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(Shape)) && all(0 < Shape)
okay1
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape) )),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
rperks(nsim * length(Scale), shape = Shape, scale = Scale)
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
deriv = eval(substitute(expression({
M1 <- 2
scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
temp2 <- exp(y * scale)
temp3 <- 1 + shape * temp2
dl.dshape <- 1 / shape + 1 / (scale * (1 + shape)) -
(1 + 1 / scale) * temp2 / temp3
dl.dscale <- y - log1p(shape) / scale^2 +
log1p(shape * temp2) / scale^2 -
(1 + 1 / scale) * shape * y * temp2 / temp3
dshape.deta <- dtheta.deta(shape, .lshape , .eshape )
dscale.deta <- dtheta.deta(scale, .lscale , .escale )
dthetas.detas <- cbind(dscale.deta, dshape.deta)
myderiv <- c(w) * cbind(dl.dscale, dl.dshape) * dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1) # wz is 'tridiagonal'
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE)
for (spp. in 1:NOS) {
run.varcov <- 0
Scale <- scale[, spp.]
Shape <- shape[, spp.]
if (FALSE && intercept.only && .oim.mean ) {
stop("this is wrong")
temp8 <- (1 + Shape * exp(Scale * y[, spp.]))^2
nd2l.dadb <- 2 * y[, spp.] * exp(Scale * y[, spp.]) / temp8
nd2l.dada <- 1 / Shape^2 + 1 / (1 + Shape)^2 -
2 * exp(2 * Scale * y[, spp.]) / temp8
nd2l.dbdb <- 2 * Shape * y[, spp.]^2 *
exp(Scale * y[, spp.]) / temp8
ave.oim11 <- weighted.mean(nd2l.dada, w[, spp.])
ave.oim12 <- weighted.mean(nd2l.dadb, w[, spp.])
ave.oim22 <- weighted.mean(nd2l.dbdb, w[, spp.])
run.varcov <- cbind(ave.oim11, ave.oim22, ave.oim12)
} else {
for (ii in 1:( .nsimEIM )) {
ysim <- rperks(n = n, shape = Shape, scale = Scale)
if (ii < 3) {
}
temp2 <- exp(ysim * Scale)
temp3 <- 1 + Shape * temp2
dl.dshape <- 1 / Shape + 1 / (Scale * (1 + Shape)) -
(1 + 1 / Scale) * temp2 / temp3
dl.dscale <- ysim - log1p(Shape) / Scale^2 +
log1p(Shape * temp2) / Scale^2 -
(1 + 1 / Scale) * Shape * ysim * temp2 / temp3
temp7 <- cbind(dl.dscale, dl.dshape)
if (ii < 3) {
}
run.varcov <- run.varcov +
temp7[, ind1$row.index] *
temp7[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM )
}
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov), n, ncol(run.varcov),
byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) {
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay,
M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}), list( .lscale = lscale,
.escale = escale,
.nsimEIM = nsimEIM, .oim.mean = oim.mean ))))
} # perks()
dmakeham <-
function(x, scale = 1, shape, epsilon = 0, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(epsilon))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
index0 <- (x < 0)
ans <- log(epsilon * exp(-x * scale) + shape) +
x * (scale - epsilon) -
(shape / scale) * expm1(x * scale)
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | epsilon < 0] <- NaN
ans
} # dmakeham
pmakeham <- function(q, scale = 1, shape, epsilon = 0,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale),
length(epsilon))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
if (lower.tail) {
if (log.p) {
ans <- log(-expm1(-q * epsilon - (shape / scale) *
expm1(scale * q)))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1(-q * epsilon - (shape / scale) *
expm1(scale * q))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- -q * epsilon - (shape / scale) * expm1(scale * q)
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp(-q * epsilon - (shape / scale) * expm1(scale * q))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0 | epsilon < 0] <- NaN
ans
} # pmakeham
qmakeham <- function(p, scale = 1, shape, epsilon = 0,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale),
length(epsilon))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- shape / (scale * epsilon) -
log(-expm1(ln.p)) / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
exp(log(-expm1(ln.p)) * (-scale / epsilon))) / scale
ans[ln.p == 0] <- Inf
ans[ln.p > 0] <- NaN
} else {
ans <- shape / (scale * epsilon) - log1p(-p) / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
exp( (-scale / epsilon) * log1p(-p) )) / scale
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- shape / (scale * epsilon) - ln.p / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
exp(ln.p * (-scale / epsilon))) / scale
ans[ln.p == -Inf] <- Inf
ans[ln.p > 0] <- NaN
} else {
ans <- shape / (scale * epsilon) - log(p) / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
p^(-scale / epsilon)) / scale
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[epsilon == 0] <-
qgompertz(p = p[epsilon == 0],
shape = shape[epsilon == 0],
scale = scale[epsilon == 0],
lower.tail = lower.tail,
log.p = log.p)
ans[shape <= 0 | scale <= 0 | epsilon < 0] <- NaN
ans
} # qmakeham
rmakeham <- function(n, scale = 1, shape, epsilon = 0) {
qmakeham(runif(n), scale = scale, shape, epsilon = epsilon)
}
makeham.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
makeham <-
function(lscale = "loglink", lshape = "loglink",
lepsilon = "loglink",
iscale = NULL, ishape = NULL,
iepsilon = NULL, # 0.3,
gscale = exp(-5:5),
gshape = exp(-5:5),
gepsilon = exp(-4:1),
nsimEIM = 500,
oim.mean = TRUE,
zero = NULL, nowarning = FALSE) {
lepsil <- lepsilon
iepsil <- iepsilon
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lepsil <- as.list(substitute(lepsil))
eepsil <- link2list(lepsil)
lepsil <- attr(eepsil, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
if (length(iepsil))
if (!is.Numeric(iepsil, positive = TRUE))
stop("argument 'iepsil' values must be positive")
if (!is.logical(oim.mean) || length(oim.mean) != 1)
stop("bad input for argument 'oim.mean'")
new("vglmff",
blurb = c("Makeham distribution\n\n",
"Links: ",
namesof("scale", lscale, escale), ", ",
namesof("shape", lshape, eshape), ", ",
namesof("epsilon", lepsil, eepsil), "\n",
"Median: qmakeham(p = 0.5, scale, shape, epsilon)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "makeham",
nsimEIM = .nsimEIM,
parameters.names = c("scale", "shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 3
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
mynames3 <- param.names("epsilon", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE),
namesof(mynames3, .lepsil , .eepsil , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matC <- matrix(if (length( .iscale )) .iscale else 0 + NA,
n, ncoly, byrow = TRUE)
matH <- matrix(if (length( .ishape )) .ishape else 0 + NA,
n, ncoly, byrow = TRUE)
matE <- matrix(if (length( .iepsil )) .iepsil else 0.3,
n, ncoly, byrow = TRUE)
shape.grid <- unique(sort(c( .gshape )))
scale.grid <- unique(sort(c( .gscale )))
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
makeham.Loglikfun2 <- function(scaleval, shapeval,
y, x, w, extraargs) {
sum(c(w) * dmakeham(x = y, shape = shapeval,
epsilon = extraargs$Epsil,
scale = scaleval, log = TRUE))
}
try.this <-
grid.search2(scale.grid, shape.grid,
objfun = makeham.Loglikfun2,
y = yvec, w = wvec,
extraargs = list(Epsilon = matE[1, spp.]),
ret.objfun = TRUE) # Last value is \ell
if (!length( .iscale ))
matC[, spp.] <- try.this["Value1"]
if (!length( .ishape ))
matH[, spp.] <- try.this["Value2"]
} # spp.
epsil.grid <- c( .gepsil )
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
makeham.Loglikfun2 <-
function(epsilval, y, x, w, extraargs) {
ans <-
sum(c(w) * dmakeham(x = y, shape = extraargs$Shape,
epsilon = epsilval, log = TRUE,
scale = extraargs$Scale))
ans
}
Init.epsil <-
grid.search(epsil.grid,
objfun = makeham.Loglikfun2,
y = yvec, x = x, w = wvec,
extraargs = list(Shape = matH[1, spp.],
Scale = matC[1, spp.]))
matE[, spp.] <- Init.epsil
} # spp.
etastart <- cbind(theta2eta(matC, .lscale , .escale ),
theta2eta(matH, .lshape , .eshape ),
theta2eta(matE, .lepsil , .eepsil ))[,
interleave.VGAM(M, M1 = M1)]
} # End of !length(etastart)
}),
list(
.lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil,
.gshape = gshape, .gscale = gscale, .gepsil = gepsilon,
.ishape = ishape, .iscale = iscale, .iepsil = iepsil
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE)],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE)],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE)],
.lepsil , .eepsil )
qmakeham(p = 0.5, scale = scale, shape, epsil = epsil)
},
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly),
rep_len( .lepsil , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2, mynames3)[
interleave.VGAM(M, M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape
misc$earg[[M1*ii ]] <- .eepsil
}
misc$M1 <- M1
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}),
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE)],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE)],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE)],
.lepsil , .eepsil )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dmakeham(y, scale = scale, shape,
epsil = epsil, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
},
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
vfamily = c("makeham"),
# ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE), drop = FALSE],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE), drop = FALSE],
.lepsil , .eepsil )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(shape)) && all(0 < shape) &&
all(is.finite(epsil)) && all(0 < epsil)
okay1
},
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE), drop = FALSE],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE), drop = FALSE],
.lepsil , .eepsil )
rmakeham(nsim * length(Scale),
scale = c(Scale), shape = c(shape), epsilon = c(epsil))
}, list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
deriv = eval(substitute(expression({
scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE), drop = FALSE],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE), drop = FALSE],
.lepsil , .eepsil )
temp2 <- exp(y * scale)
temp3 <- epsil + shape * temp2
dl.dshape <- temp2 / temp3 - expm1(y * scale) / scale
dl.dscale <- shape * y * temp2 / temp3 +
shape * expm1(y * scale) / scale^2 -
shape * y * temp2 / scale
dl.depsil <- 1 / temp3 - y
dshape.deta <- dtheta.deta(shape, .lshape , .eshape )
dscale.deta <- dtheta.deta(scale, .lscale , .escale )
depsil.deta <- dtheta.deta(epsil, .lepsil , .eepsil )
dthetas.detas <- cbind(dscale.deta, dshape.deta, depsil.deta)
myderiv <- c(w) * cbind(dl.dscale,
dl.dshape,
dl.depsil) * dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}),
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1 + M - 2) # wz has half-bandwidth 3
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE) # Use SFS
for (spp. in 1:NOS) {
run.varcov <- 0
Shape <- shape[, spp.]
Scale <- scale[, spp.]
Epsil <- epsil[, spp.]
for (ii in 1:( .nsimEIM )) {
ysim <- rmakeham(n, scale = Scale, Shape, epsil = Epsil)
temp2 <- exp(ysim * Scale)
temp3 <- Epsil + Shape * temp2
dl.dshape <- temp2 / temp3 - expm1(ysim * Scale) / Scale
dl.dscale <- Shape * ysim * temp2 / temp3 +
Shape * expm1(ysim * Scale) / Scale^2 -
Shape * ysim * temp2 / Scale
dl.depsil <- 1 / temp3 - ysim
temp7 <- cbind(dl.dscale, dl.dshape, dl.depsil)
run.varcov <- run.varcov + temp7[, ind1$row.index] *
temp7[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM )
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov, na.rm = TRUE),
n, ncol(run.varcov), byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) { # Now copy wz1 into wz
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay, M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}),
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil,
.nsimEIM = nsimEIM, .oim.mean = oim.mean ))))
} # makeham()
dgompertz <- function(x, scale = 1, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
index0 <- (x < 0)
index1 <- abs(x * scale) < 0.1 & is.finite(x * scale)
ans <- log(shape) + x * scale -
(shape / scale) * (exp(x * scale) - 1)
ans[index1] <- log(shape[index1]) + x[index1] * scale[index1] -
(shape[index1] / scale[index1]) *
expm1(x[index1] * scale[index1])
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # dgompertz
pgompertz <- function(q, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ans <- log1p(-exp((-shape / scale) * expm1(scale * q)))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1((-shape / scale) * expm1(scale * q))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- (-shape / scale) * expm1(scale * q)
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp((-shape / scale) * expm1(scale * q))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # pgompertz
qgompertz <- function(p, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- log1p((-scale / shape) * log(-expm1(ln.p))) / scale
ans[ln.p > 0] <- NaN
} else {
ans <- log1p((-scale / shape) * log1p(-p)) / scale
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- log1p((-scale / shape) * ln.p) / scale
ans[ln.p > 0] <- NaN
} else {
ans <- log1p((-scale / shape) * log(p)) / scale
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # qgompertz
rgompertz <- function(n, scale = 1, shape) {
qgompertz(runif(n), scale = scale, shape = shape)
}
gompertz.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
gompertz <-
function(lscale = "loglink", lshape = "loglink",
iscale = NULL, ishape = NULL,
nsimEIM = 500,
zero = NULL, nowarning = FALSE) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
new("vglmff",
blurb = c("Gompertz distribution\n\n",
"Links: ",
namesof("scale", lscale, escale ), ", ",
namesof("shape", lshape, eshape ), "\n",
"Median: scale * log(2 - 1 / shape)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "gompertz",
nsimEIM = .nsimEIM,
parameters.names = c("scale", "shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matH <- matrix(if (length( .ishape )) .ishape else 0 + NA,
n, ncoly, byrow = TRUE)
matC <- matrix(if (length( .iscale )) .iscale else 0 + NA,
n, ncoly, byrow = TRUE)
shape.grid <- c(exp(-seq(4, 0.1, len = 07)), 1,
exp( seq(0.1, 4, len = 07)))
scale.grid <- c(exp(-seq(4, 0.1, len = 07)), 1,
exp( seq(0.1, 4, len = 07)))
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
gompertz.Loglikfun <-
function(scaleval, y, x, w, extraargs) {
ans <-
sum(c(w) * dgompertz(x = y, shape = extraargs$Shape,
scale = scaleval, log = TRUE))
ans
}
mymat <- matrix(-1, length(shape.grid), 2)
for (jlocal in seq_along(shape.grid)) {
mymat[jlocal, ] <-
grid.search(scale.grid,
objfun = gompertz.Loglikfun,
y = yvec, x = x, w = wvec,
ret.objfun = TRUE,
extraargs = list(Shape = shape.grid[jlocal]))
}
index.shape <- which(mymat[, 2] == max(mymat[, 2]))[1]
if (!length( .ishape ))
matH[, spp.] <- shape.grid[index.shape]
if (!length( .iscale ))
matC[, spp.] <- mymat[index.shape, 1]
} # spp.
etastart <- cbind(theta2eta(matC, .lscale , .escale ),
theta2eta(matH, .lshape , .eshape ))[,
interleave.VGAM(M, M1 = M1)]
} # End of !length(etastart)
}), list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale,
.ishape = ishape, .iscale = iscale
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
log1p((scale / shape) * log(2)) / scale
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M,
M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape
}
misc$M1 <- M1
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dgompertz(x = y, scale = scale,
shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
vfamily = c("gompertz"),
# ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
rgompertz(nsim * length(Scale),
shape = c(Shape), scale = c(Scale))
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
deriv = eval(substitute(expression({
M1 <- 2
scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
temp2 <- exp(y * scale)
temp4 <- -expm1(y * scale)
dl.dshape <- 1 / shape + temp4 / scale
dl.dscale <- y * (1 - shape * temp2 / scale) -
shape * temp4 / scale^2
dscale.deta <- dtheta.deta(scale, .lscale , .escale )
dshape.deta <- dtheta.deta(shape, .lshape , .eshape )
dthetas.detas <- cbind(dscale.deta, dshape.deta)
myderiv <- c(w) * cbind(dl.dscale, dl.dshape) * dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1) # wz is 'tridiagonal'
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE)
for (spp. in 1:NOS) {
run.varcov <- 0
Shape <- shape[, spp.]
Scale <- scale[, spp.]
for (ii in 1:( .nsimEIM )) {
ysim <- rgompertz(n = n, shape = Shape, scale = Scale)
if (ii < 3) {
}
temp2 <- exp(ysim * scale)
temp4 <- -expm1(ysim * scale)
dl.dshape <- 1 / shape + temp4 / scale
dl.dscale <- ysim * (1 - shape * temp2 / scale) -
shape * temp4 / scale^2
temp7 <- cbind(dl.dscale, dl.dshape)
run.varcov <- run.varcov +
temp7[, ind1$row.index] *
temp7[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM )
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov),
nrow = n, ncol = ncol(run.varcov), byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) {
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay,
M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}), list( .lscale = lscale,
.escale = escale,
.nsimEIM = nsimEIM ))))
} # gompertz()
dmoe <- function (x, alpha = 1, lambda = 1, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x),
length(alpha),
length(lambda))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(alpha) != LLL) alpha <- rep_len(alpha, LLL)
if (length(lambda) != LLL) lambda <- rep_len(lambda, LLL)
index0 <- (x < 0)
if (log.arg) {
ans <- log(lambda) + (lambda * x) -
2 * log(expm1(lambda * x) + alpha)
ans[index0] <- log(0)
} else {
ans <- lambda * exp(lambda * x) / (expm1(lambda * x) + alpha)^2
ans[index0] <- 0
}
ans[alpha <= 0 | lambda <= 0] <- NaN
ans
}
pmoe <- function (q, alpha = 1, lambda = 1) {
ret <- ifelse(alpha <= 0 | lambda <= 0, NaN,
1 - 1 / (expm1(lambda * q) + alpha))
ret[q < log(2 - alpha) / lambda] <- 0
ret
}
qmoe <- function (p, alpha = 1, lambda = 1) {
ifelse(p < 0 | p > 1 | alpha <= 0 | lambda <= 0, NaN,
log1p(-alpha + 1 / (1 - p)) / lambda)
}
rmoe <- function (n, alpha = 1, lambda = 1) {
qmoe(p = runif(n), alpha = alpha, lambda = lambda)
}
exponential.mo.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
exponential.mo <-
function(lalpha = "loglink", llambda = "loglink",
ealpha = list(), elambda = list(),
ialpha = 1, ilambda = NULL,
imethod = 1,
nsimEIM = 200,
zero = NULL) {
stop("fundamentally unable to estimate the parameters as ",
"the support of the density depends on the parameters")
lalpha <- as.list(substitute(lalpha))
ealpha <- link2list(lalpha)
lalpha <- attr(ealpha, "function.name")
llambda <- as.list(substitute(llambda))
elambda <- link2list(llambda)
llambda <- attr(elambda, "function.name")
lalpha0 <- lalpha
ealpha0 <- ealpha
ialpha0 <- ialpha
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ialpha0))
if (!is.Numeric(ialpha0, positive = TRUE))
stop("argument 'ialpha' values must be positive")
if (length(ilambda))
if (!is.Numeric(ilambda, positive = TRUE))
stop("argument 'ilambda' values must be positive")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 2)
stop("argument 'imethod' must be 1 or 2")
new("vglmff",
blurb = c("Marshall-Olkin exponential distribution\n\n",
"Links: ",
namesof("alpha", lalpha0, ealpha0 ), ", ",
namesof("lambda", llambda, elambda ), "\n",
"Median: log(3 - alpha) / lambda"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "moe",
nsimEIM = .nsimEIM,
parameters.names = c("alpha", "lambda"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("alpha", ncoly, skip1 = TRUE)
mynames2 <- param.names("lambda", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lalpha0 , .ealpha0 , tag = FALSE),
namesof(mynames2, .llambda , .elambda , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matL <- matrix(if (length( .ilambda )) .ilambda else 0,
n, ncoly, byrow = TRUE)
matA <- matrix(if (length( .ialpha0 )) .ialpha0 else 0,
n, ncoly, byrow = TRUE)
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
moexpon.Loglikfun <-
function(lambdaval, y, x, w, extraargs) {
ans <-
sum(c(w) * log(dmoe(x = y, alpha = extraargs$alpha,
lambda = lambdaval)))
ans
}
Alpha.init <- .ialpha0
lambda.grid <- seq(0.1, 10.0, len = 21)
Lambda.init <- grid.search(lambda.grid,
objfun = moexpon.Loglikfun,
y = y, x = x, w = w,
extraargs = list(alpha = Alpha.init))
if (length(mustart)) {
Lambda.init <- Lambda.init / (1 - Phimat.init)
}
if (!length( .ialpha0 ))
matA[, spp.] <- Alpha0.init
if (!length( .ilambda ))
matL[, spp.] <- Lambda.init
} # spp.
etastart <- cbind(theta2eta(matA, .lalpha0, .ealpha0 ),
theta2eta(matL, .llambda, .elambda ))[,
interleave.VGAM(M, M1 = M1)]
mustart <- NULL # Since etastart has been computed.
} # End of !length(etastart)
}), list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda,
.ialpha0 = ialpha0, .ilambda = ilambda,
.imethod = imethod
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
alpha0 <- eta2theta(eta[, c(TRUE, FALSE)], .lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE)], .llambda , .elambda )
log(3 - alpha0) / lambda
}, list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lalpha0 , ncoly),
rep_len( .llambda , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M,
M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .ealpha0
misc$earg[[M1*ii ]] <- .elambda
}
misc$M1 <- M1
misc$imethod <- .imethod
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda,
.nsimEIM = nsimEIM,
.imethod = imethod ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
alpha0 <- eta2theta(eta[, c(TRUE, FALSE)], .lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE)], .llambda , .elambda )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * log(dmoe(x = y, alpha = alpha0,
lambda = lambda))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
vfamily = c("exponential.mo"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
alpha0 <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.llambda , .elambda )
okay1 <- all(is.finite(alpha0)) && all(0 < alpha0) &&
all(is.finite(lambda)) && all(0 < lambda)
okay1
}, list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
deriv = eval(substitute(expression({
M1 <- 2
alpha0 <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.llambda , .elambda )
temp2 <- (expm1(lambda * y) + alpha0)
dl.dalpha0 <- -2 / temp2
dl.dlambda <- 1 / lambda + y -
2 * y * exp(lambda * y) / temp2
dalpha0.deta <- dtheta.deta(alpha0, .lalpha0 , .ealpha0 )
dlambda.deta <- dtheta.deta(lambda, .llambda , .elambda )
dthetas.detas <- cbind(dalpha0.deta,
dlambda.deta)
myderiv <- c(w) * cbind(dl.dalpha0, dl.dlambda) *
dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1) # wz is 'tridiagonal'
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE)
for (spp. in 1:NOS) {
run.varcov <- 0
Alph <- alpha0[, spp.]
Lamb <- lambda[, spp.]
for (ii in 1:( .nsimEIM )) {
ysim <- rmoe(n = n, alpha = Alph, lambda = Lamb)
if (ii < 3) {
}
temp2 <- (expm1(lambda * ysim) + alpha0)
dl.dalpha0 <- -2 / temp2
dl.dlambda <- 1 / lambda + ysim -
2 * ysim * exp(lambda * ysim) / temp2
temp3 <- cbind(dl.dalpha0, dl.dlambda)
run.varcov <- run.varcov +
temp3[, ind1$row.index] *
temp3[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM)
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov),
n, ncol(run.varcov), byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) {
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay,
M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}), list( .llambda = llambda,
.elambda = elambda,
.nsimEIM = nsimEIM ))))
} # exponential.mo()
genbetaII.Loglikfun4 <-
function(scaleval, shape1.a, shape2.p, shape3.q,
y, x, w, extraargs) {
sum(c(w) * dgenbetaII(x = y, scale = scaleval,
shape1.a = shape1.a,
shape2.p = shape2.p,
shape3.q = shape3.q,
log = TRUE))
}
genbetaII <-
function(lscale = "loglink",
lshape1.a = "loglink",
lshape2.p = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape1.a = NULL,
ishape2.p = NULL,
ishape3.q = NULL,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = exp(-5:5),
gshape2.p = exp(-5:5),
gshape3.q = exp(-5:5),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Generalized Beta II distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale * gamma(shape2.p + 1/shape1.a) * ",
"gamma(shape3.q - 1/shape1.a) / ",
"(gamma(shape2.p) * gamma(shape3.q))"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 4,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 4,
Q1 = 1,
dpqrfun = "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a", "shape2.p", "shape3.q") else
c("shape1.a", "scale", "shape2.p", "shape3.q"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a ,
lshape2.p = .lshape2.p , lshape3.q = .lshape3.q ,
eshape2.p = .eshape2.p , eshape3.q = .eshape3.q )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ,
.zero = zero ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 4 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <- c(
if ( .lss ) {
c(namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE),
namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE),
namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE))
},
namesof(sha2.names , .lshape2.p , .eshape2.p , tag = FALSE),
namesof(sha3.names , .lshape3.q , .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <-
pp.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
gscale <- .gscale
gshape1.a <- .gshape1.a
gshape2.p <- .gshape2.p
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
if (length( .ishape2.p )) gshape2.p <-
rep_len( .ishape2.p , NOS)
if (length( .ishape3.q )) gshape3.q <-
rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, gshape1.a, gshape2.p, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
pp.init[, spp.] <- try.this["Value3"]
qq.init[, spp.] <- try.this["Value4"]
} # End of for (spp. ...)
finite.mean <- 1 < aa.init * qq.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init * qq.init
}
etastart <-
cbind(if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale )),
theta2eta(pp.init , .lshape2.p , earg = .eshape2.p ),
theta2eta(qq.init , .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.ishape2.p = ishape2.p, .ishape3.q = ishape3.q,
.gshape2.p = gshape2.p, .gshape3.q = gshape3.q,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 4
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- cbind(Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq)))
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 4
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly),
rep_len( .lshape2.p , ncoly),
rep_len( .lshape3.q , ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names, sha2.names, sha3.names)
} else {
c(sha1.names, scaL.names, sha2.names, sha3.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
if ( .lss ) {
misc$earg[[M1*ii-3]] <- .escale
misc$earg[[M1*ii-2]] <- .eshape1.a
} else {
misc$earg[[M1*ii-3]] <- .eshape1.a
misc$earg[[M1*ii-2]] <- .escale
}
misc$earg[[M1*ii-1]] <- .eshape2.p
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 4
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
vfamily = c("genbetaII"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 4
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a*p < 1 < a*q has ",
"been violated; solution may be at the",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 4
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
dp.deta <- dtheta.deta(parg, .lshape2.p , .eshape2.p )
dq.deta <- dtheta.deta(qq, .lshape3.q , .eshape3.q )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta,
dl.dp * dp.deta,
dl.dq * dq.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta,
dl.dp * dp.deta,
dl.dq * dq.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dq <- temp5b - temp5
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
ned2l.dap <- -(qq * (temp3a -temp3b) -1) / (aa*(parg+qq))
ned2l.daq <- -(parg * (temp3b -temp3a) -1) / (aa*(parg+qq))
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
ned2l.dpq <- -temp5
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dap * da.deta * dp.deta,
c(w) * ned2l.dpq * dp.deta * dq.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.daq * da.deta * dq.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.dpq * dp.deta * dq.deta,
c(w) * ned2l.dap * da.deta * dp.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta,
c(w) * ned2l.daq * da.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))))
} # genbetaII
dgenbetaII <-
function(x, scale = 1, shape1.a, shape2.p, shape3.q,
log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("Bad input for argument 'log'")
rm(log)
logden <- log(shape1.a) +
(shape1.a * shape2.p - 1) * log(abs(x)) -
shape1.a * shape2.p * log(scale) -
lbeta(shape2.p, shape3.q) -
(shape2.p + shape3.q) * log1p((abs(x)/scale)^shape1.a)
if (any(x <= 0) || any(is.infinite(x))) {
LLL <- max(length(x), length(scale),
length(shape1.a), length(shape2.p), length(shape3.q))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
logden[is.infinite(x)] <- log(0)
logden[x < 0] <- log(0)
x.eq.0 <- !is.na(x) & (x == 0)
if (any(x.eq.0)) {
axp <- shape1.a[x.eq.0] * shape2.p[x.eq.0]
logden[x.eq.0 & axp < 1] <- log(Inf)
ind5 <- x.eq.0 & axp == 1
logden[ind5] <- log(shape1.a[ind5]) -
shape1.a[ind5] * shape2.p[ind5] * log(scale[ind5]) -
lbeta(shape2.p[ind5], shape3.q[ind5]) -
(shape2.p[ind5] + shape3.q[ind5]) *
log1p((0/scale[ind5])^shape1.a[ind5])
logden[x.eq.0 & axp > 1] <- log(0)
}
}
if (log.arg) logden else exp(logden)
} # dgenbetaII
rsinmad <- function(n, scale = 1, shape1.a, shape3.q)
qsinmad(runif(n), shape1.a = shape1.a, scale = scale,
shape3.q = shape3.q)
rlomax <- function(n, scale = 1, shape3.q)
rsinmad(n, scale = scale, shape1.a = 1, shape3.q = shape3.q)
rfisk <- function(n, scale = 1, shape1.a)
rsinmad(n, scale = scale, shape1.a = shape1.a, shape3.q = 1)
rparalogistic <- function(n, scale = 1, shape1.a)
rsinmad(n, scale = scale, shape1.a = shape1.a,
shape3.q = shape1.a)
rdagum <- function(n, scale = 1, shape1.a, shape2.p)
qdagum(runif(n), scale = scale, shape1.a = shape1.a,
shape2.p = shape2.p)
rinv.lomax <- function(n, scale = 1, shape2.p)
rdagum(n, scale = scale, shape1.a = 1, shape2.p = shape2.p)
rinv.paralogistic <- function(n, scale = 1, shape1.a)
rdagum(n, scale = scale, shape1.a = shape1.a,
shape2.p = shape1.a)
qsinmad <- function(p, scale = 1, shape1.a, shape3.q,
lower.tail = TRUE,
log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape1.a), length(scale),
length(shape3.q))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- scale * expm1((-1/shape3.q) *
log(-expm1(ln.p)))^(1/shape1.a)
} else {
ans <- scale * expm1((-1/shape3.q) * log1p(-p))^(1/shape1.a)
ans[p == 0] <- 0
ans[p == 1] <- Inf
}
} else {
if (log.p) {
ln.p <- p
ans <- scale * expm1(-ln.p / shape3.q)^(1/shape1.a)
} else {
ans <- scale * expm1(-log(p) / shape3.q)^(1/shape1.a)
ans[p == 0] <- Inf
ans[p == 1] <- 0
}
}
ans[scale <= 0 | shape1.a <= 0 | shape3.q <= 0] <- NaN
ans
} # qsinmad
qlomax <- function(p, scale = 1, shape3.q,
lower.tail = TRUE, log.p = FALSE)
qsinmad(p, shape1.a = 1, scale = scale, shape3.q = shape3.q,
lower.tail = lower.tail, log.p = log.p)
qfisk <- function(p, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
qsinmad(p, shape1.a = shape1.a, scale = scale, shape3.q = 1,
lower.tail = lower.tail, log.p = log.p)
qparalogistic <- function(p, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
qsinmad(p, shape1.a = shape1.a, scale = scale,
shape3.q = shape1.a,
lower.tail = lower.tail, log.p = log.p)
qdagum <- function(p, scale = 1, shape1.a, shape2.p,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape1.a), length(scale),
length(shape2.p))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- scale * (expm1(-ln.p/shape2.p))^(-1/shape1.a)
ans[ln.p > 0] <- NaN
} else {
ans <- scale * (expm1(-log(p)/shape2.p))^(-1/shape1.a)
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- scale *
(expm1(-log(-expm1(ln.p))/shape2.p))^(-1/shape1.a)
ans[ln.p > 0] <- NaN
} else {
ans <- scale * (expm1(-log1p(-p)/shape2.p))^(-1/shape1.a)
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[scale <= 0 | shape1.a <= 0 | shape2.p <= 0] <- NaN
ans
} # qdagum
qinv.lomax <- function(p, scale = 1, shape2.p,
lower.tail = TRUE, log.p = FALSE)
qdagum(p, scale = scale, shape1.a = 1, shape2.p = shape2.p,
lower.tail = lower.tail, log.p = log.p)
qinv.paralogistic <- function(p, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
qdagum(p, scale = scale, shape1.a = shape1.a,
shape2.p = shape1.a, ## 20150121 Kai; add shape2.p=shape1.a
lower.tail = lower.tail, log.p = log.p)
psinmad <- function(q, scale = 1, shape1.a, shape3.q,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape1.a), length(scale),
length(shape3.q))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
# 20150121 KaiH
if (lower.tail) {
if (log.p) {
ans <- log1p(-(1 + (q / scale)^shape1.a)^(-shape3.q))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- exp(log1p(-(1 + (q / scale)^shape1.a)^(-shape3.q)))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- (-shape3.q) * log1p((q / scale)^shape1.a)
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- (1 + (q / scale)^shape1.a)^(-shape3.q)
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[scale <= 0 | shape1.a <= 0 | shape3.q <= 0] <- NaN
ans
} # psinmad
plomax <-
function(q, scale = 1, shape3.q, # Change the order
lower.tail = TRUE, log.p = FALSE)
psinmad(q, shape1.a = 1, scale = scale, shape3.q = shape3.q,
lower.tail = lower.tail, log.p = log.p)
pfisk <- function(q, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
psinmad(q, shape1.a = shape1.a, scale = scale, shape3.q = 1,
lower.tail = lower.tail, log.p = log.p)
pparalogistic <-
function(q, scale = 1, shape1.a, # Change the order
lower.tail = TRUE, log.p = FALSE)
psinmad(q, shape1.a = shape1.a, scale = scale,
shape3.q = shape1.a, # Add shape3.q = shape1.a
lower.tail = lower.tail, log.p = log.p)
pdagum <- function(q, scale = 1, shape1.a, shape2.p,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape1.a), length(scale),
length(shape2.p))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
if (lower.tail) {
if (log.p) {
ans <- (-shape2.p) * log1p((q/scale)^(-shape1.a))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- exp( (-shape2.p) * log1p((q/scale)^(-shape1.a)) )
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- log1p(-(1 + (q/scale)^(-shape1.a))^(-shape2.p))
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
stop("unfinished")
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape1.a <= 0 | scale <= 0 | shape2.p <= 0] <- NaN
ans
} # pdagum
pinv.lomax <- function(q, scale = 1, shape2.p,
lower.tail = TRUE, log.p = FALSE)
pdagum(q, scale = scale, shape1.a = 1, shape2.p = shape2.p,
lower.tail = lower.tail, log.p = log.p)
pinv.paralogistic <-
function(q, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
pdagum(q, scale = scale, shape1.a = shape1.a,
shape2.p = shape1.a,
lower.tail = lower.tail, log.p = log.p)
dbetaII <-
function(x, scale = 1, shape2.p, shape3.q, log = FALSE)
dgenbetaII(x = x, scale = scale, shape1.a = 1,
shape2.p = shape2.p, shape3.q = shape3.q, log = log)
dsinmad <-
function(x, scale = 1, shape1.a, shape3.q, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape1.a),
length(scale), length(shape3.q))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
Loglik <- rep_len(log(0), LLL)
xok <- (x > 0) & !is.na(x) # Avoids log(x) if x<0, and handles NAs
Loglik[xok] <- log(shape1.a[xok]) + log(shape3.q[xok]) +
(shape1.a[xok]-1) * log(x[xok]) -
shape1.a[xok] * log(scale[xok]) -
(1 + shape3.q[xok]) *
log1p((x[xok]/scale[xok])^shape1.a[xok])
x.eq.0 <- (x == 0) & !is.na(x)
Loglik[x.eq.0] <- log(shape1.a[x.eq.0]) + log(shape3.q[x.eq.0]) -
shape1.a[x.eq.0] * log(scale[x.eq.0])
Loglik[is.na(x)] <- NA
Loglik[is.nan(x)] <- NaN
Loglik[x == Inf] <- log(0)
if (log.arg) Loglik else exp(Loglik)
}
dlomax <- function(x, scale = 1, shape3.q, log = FALSE)
dsinmad(x, scale = scale, shape1.a = 1, shape3.q = shape3.q,
log = log)
dfisk <- function(x, scale = 1, shape1.a, log = FALSE)
dsinmad(x, scale = scale, shape1.a = shape1.a, shape3.q = 1,
log = log)
dparalogistic <- function(x, scale = 1, shape1.a, log = FALSE)
dsinmad(x, scale = scale, shape1.a = shape1.a, shape3.q = shape1.a,
log = log)
ddagum <-
function(x, scale = 1, shape1.a, shape2.p, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x),
length(shape1.a),
length(scale),
length(shape2.p))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
Loglik <- rep_len(log(0), LLL)
xok <- (x > 0) & !is.na(x) # Avoids log(x) if x<0, and handles NAs
Loglik[xok] <- log(shape1.a[xok]) +
log(shape2.p[xok]) +
(shape1.a[xok] * shape2.p[xok]-1) * log( x[xok]) -
shape1.a[xok] * shape2.p[xok] * log(scale[xok]) -
(1 + shape2.p[xok]) *
log1p((x[xok]/scale[xok])^shape1.a[xok])
Loglik[shape2.p <= 0] <- NaN
x.eq.0 <- (x == 0) & !is.na(x)
Loglik[x.eq.0] <- log(shape1.a[x.eq.0]) +
log(shape2.p[x.eq.0]) -
shape1.a[x.eq.0] * shape2.p[x.eq.0] *
log(scale[x.eq.0])
Loglik[is.na(x)] <- NA
Loglik[is.nan(x)] <- NaN
Loglik[x == Inf] <- log(0)
if (log.arg) Loglik else exp(Loglik)
}
dinv.lomax <- function(x, scale = 1, shape2.p, log = FALSE)
ddagum(x, scale = scale, shape1.a = 1, shape2.p = shape2.p,
log = log)
dinv.paralogistic <- function(x, scale = 1, shape1.a, log = FALSE)
ddagum(x, scale = scale, shape1.a = shape1.a, shape2.p = shape1.a,
log = log)
sinmad <- function(lscale = "loglink",
lshape1.a = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape1.a = NULL,
ishape3.q = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = exp(-5:5),
gshape3.q = exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Singh-Maddala distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale * gamma(shape2.p + 1/shape1.a) * ",
"gamma(shape3.q - 1/shape1.a) / ",
"gamma(shape3.q)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "sinmad", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a", "shape3.q") else
c("shape1.a", "scale", "shape3.q"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a ,
lshape3.q = .lshape3.q ,
eshape3.q = .eshape3.q )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
scrambleseed <- runif(1) # To scramble the seed
qnorm(psinmad(y, scale = Scale, shape1.a = aa,
shape3.q = qq))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 3 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <- c(
if ( .lss ) {
c(namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE),
namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE),
namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE))
},
namesof(sha3.names , .lshape3.q , .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
if (length( .ishape3.q )) gshape3.q <-
rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, gshape1.a, vov3 = 1, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
qq.init[, spp.] <- try.this["Value4"]
} else { # .imethod == 2
qvec <- .probs.y
ishape3.q <- if (length( .ishape3.q )) .ishape3.q else 1
xvec <- log( (1-qvec)^(-1/ ishape3.q ) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else 1/fit0$coef[2]
qq.init[, spp.] <- ishape3.q
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init * qq.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init * qq.init
}
etastart <-
cbind(if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale )),
theta2eta(qq.init , .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.ishape3.q = ishape3.q,
.gshape3.q = gshape3.q,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) - lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[qq <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 3
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly),
rep_len( .lshape3.q , ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names, sha3.names)
} else {
c(sha1.names, scaL.names, sha3.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
if ( .lss ) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape1.a
} else {
misc$earg[[M1*ii-2]] <- .eshape1.a
misc$earg[[M1*ii-1]] <- .escale
}
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
vfamily = c("sinmad"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 3
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
rsinmad(nsim * length(qq), shape1.a = aa, scale = Scale,
shape3.q = qq)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a < 1 < a*q has ",
"been violated; solution may be at the",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
dq.deta <- dtheta.deta(qq, .lshape3.q , .eshape3.q )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta,
dl.dq * dq.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta,
dl.dq * dq.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dq <- temp5b - temp5
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
ned2l.daq <- -(parg * (temp3b -temp3a) -1) / (aa*(parg+qq))
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.daq * da.deta * dq.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta,
c(w) * ned2l.daq * da.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))))
} # sinmad
dagum <-
function(lscale = "loglink",
lshape1.a = "loglink",
lshape2.p = "loglink",
iscale = NULL,
ishape1.a = NULL,
ishape2.p = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
gshape2.p = exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
new("vglmff",
blurb =
c("Dagum distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), "\n",
"Mean: scale * gamma(shape2.p + 1/shape1.a) * ",
"gamma(1 - 1/shape1.a) / ",
"gamma(shape2.p)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "dagum", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names =
if ( .lss ) c("scale", "shape1.a", "shape2.p") else
c("shape1.a", "scale", "shape2.p"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a ,
lshape2.p = .lshape2.p ,
eshape2.p = .eshape2.p )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss , .zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
scrambleseed <- runif(1) # To scramble the seed
qnorm(pdagum(y, scale = Scale, shape1.a = aa,
shape2.p = parg)) # shape3.q = 1
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 3 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
predictors.names <- c(
if ( .lss ) {
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE),
namesof(scaL.names , .lscale , .escale , tag = FALSE))
},
namesof(sha2.names , .lshape2.p , .eshape2.p , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <-
pp.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
gshape2.p <- .gshape2.p
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
if (length( .ishape2.p )) gshape2.p <-
rep_len( .ishape2.p , NOS)
try.this <-
grid.search4(gscale, gshape1.a, gshape2.p, vov4 = 1,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
pp.init[, spp.] <- try.this["Value3"]
} else { # .imethod == 2
qvec <- .probs.y
ishape2.p <- if (length( .ishape2.p )) .ishape2.p else 1
xvec <- log( qvec^(-1/ ishape2.p) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a )) .ishape1.a else
-1/fit0$coef[2]
pp.init[, spp.] <- ishape2.p
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init
}
etastart <-
cbind(if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale )),
theta2eta(pp.init , .lshape2.p , earg = .eshape2.p ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.ishape2.p = ishape2.p,
.gshape2.p = gshape2.p,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- 1
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[parg <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 3
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly),
rep_len( .lshape2.p , ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names, sha2.names)
} else {
c(sha1.names, scaL.names, sha2.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
if ( .lss ) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape1.a
} else {
misc$earg[[M1*ii-2]] <- .eshape1.a
misc$earg[[M1*ii-1]] <- .escale
}
misc$earg[[M1*ii ]] <- .eshape2.p
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = 1,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
vfamily = c("dagum"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- 1
rdagum(nsim * length(parg), shape1.a = aa, scale = Scale,
shape2.p = parg)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a*p < 1 < a has ",
"been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
dp.deta <- dtheta.deta(parg, .lshape2.p , .eshape2.p )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta,
dl.dp * dp.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta,
dl.dp * dp.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
ned2l.dap <- -(qq * (temp3a -temp3b) -1) / (aa*(parg+qq))
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dap * da.deta * dp.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.dap * da.deta * dp.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))))
} # dagum
betaII <-
function(lscale = "loglink",
lshape2.p = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape2.p = NULL,
ishape3.q = NULL,
imethod = 1,
gscale = exp(-5:5),
gshape2.p = exp(-5:5),
gshape3.q = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale ) && !is.Numeric(iscale , positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Beta II distribution \n\n",
"Links: ",
namesof("scale" , lscale , earg = escale ), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale * gamma(shape2.p + 1) * ",
"gamma(shape3.q - 1) / ",
"(gamma(shape2.p) * gamma(shape3.q))"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "betaII", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = c("scale", "shape2.p", "shape3.q"),
lscale = .lscale ,
escale = .escale ,
lshape2.p = .lshape2.p , lshape3.q = .lshape3.q ,
eshape2.p = .eshape2.p , eshape3.q = .eshape3.q )
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.zero = zero ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 3 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <-
c(namesof(scaL.names , .lscale , earg = .escale , tag = FALSE),
namesof(sha2.names , .lshape2.p , earg = .eshape2.p , tag = FALSE),
namesof(sha3.names , .lshape3.q , earg = .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
sc.init <-
pp.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape2.p <- .gshape2.p
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <- rep_len( .iscale , NOS)
if (length( .ishape2.p )) gshape2.p <- rep_len( .ishape2.p , NOS)
if (length( .ishape3.q )) gshape3.q <- rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, vov2 = 1, gshape2.p, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
pp.init[, spp.] <- try.this["Value3"]
qq.init[, spp.] <- try.this["Value4"]
} else { # .imethod == 2
sc.init[, spp.] <- if (length( .iscale )) .iscale else {
qvec <- .probs.y
ishape3.q <- if (length( .ishape3.q )) .ishape3.q else 1
xvec <- log( (1-qvec)^(-1/ ishape3.q ) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec )))
exp(fit0$coef[1])
}
pp.init[, spp.] <- if (length( .ishape2.p )) .ishape2.p else 1.0
qq.init[, spp.] <- if (length( .ishape3.q )) .ishape3.q else 1.0
}
} # End of for (spp. ...)
finite.mean <- 1 < qq.init
COP.use <- 1.15
while (any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
finite.mean <- 1 < qq.init
}
etastart <-
cbind(theta2eta(sc.init , .lscale , earg = .escale ),
theta2eta(pp.init , .lshape2.p , earg = .eshape2.p ),
theta2eta(qq.init , .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale ,
.escale = escale ,
.iscale = iscale ,
.gscale = gscale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.ishape2.p = ishape2.p, .ishape3.q = ishape3.q,
.gshape2.p = gshape2.p, .gshape3.q = gshape3.q,
.imethod = imethod , .probs.y = probs.y
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) - lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[parg <= 0] <- NA
ans[qq <= 0] <- NA
ans
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
last = eval(substitute(expression({
M1 <- 3
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape2.p , ncoly),
rep_len( .lshape3.q , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(scaL.names, sha2.names, sha3.names)
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape2.p
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 3
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
vfamily = c("betaII"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -p < 1 < q has",
" been violated; solution may be at the ",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q ))),
deriv = eval(substitute(expression({
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
dp.deta <- dtheta.deta(parg, .lshape2.p , .eshape2.p )
dq.deta <- dtheta.deta(qq, .lshape3.q , .eshape3.q )
myderiv <-
c(w) * cbind(dl.dscale * dscale.deta,
dl.dp * dp.deta,
dl.dq * dq.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dq <- temp5b - temp5
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
ned2l.dpq <- -temp5
wz <-
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.dpq * dp.deta * dq.deta, # Switched!!
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))))
} # betaII
lomax <-
function(lscale = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape3.q = NULL,
imethod = 1,
gscale = exp(-5:5),
gshape3.q = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Lomax distribution \n\n",
"Links: ",
namesof("scale" , lscale , earg = escale ), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale / (shape3.q - 1)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "lomax", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = c("scale", "shape3.q"),
lscale = .lscale ,
escale = .escale ,
lshape3.q = .lshape3.q ,
eshape3.q = .eshape3.q )
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
scrambleseed <- runif(1) # To scramble the seed
qnorm(plomax(y, scale = Scale, shape3.q = qq))
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <-
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha3.names , .lshape3.q , .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1) {
gscale <- .gscale
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape3.q )) gshape3.q <-
rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, vov2 = 1, vov3 = 1, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
qq.init[, spp.] <- try.this["Value4"]
} else { # .imethod == 2
qvec <- .probs.y
iscale <- if (length( .iscale )) .iscale else 1
xvec <- log1p( quantile(yvec / iscale, probs = qvec) )
fit0 <- lsfit(xvec, y = -log1p(-qvec), intercept = FALSE)
sc.init[, spp.] <- iscale
qq.init[, spp.] <- if (length( .ishape3.q ))
.ishape3.q else
fit0$coef
}
} # End of for (spp. ...)
finite.mean <- 1 < qq.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
finite.mean <- 1 < qq.init
}
etastart <-
cbind(theta2eta(sc.init, .lscale , earg = .escale ),
theta2eta(qq.init, .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale ,
.escale = escale ,
.iscale = iscale ,
.gscale = gscale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.ishape3.q = ishape3.q,
.gshape3.q = gshape3.q,
.imethod = imethod , .probs.y = probs.y
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[qq <= 0] <- NA
ans
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
last = eval(substitute(expression({
M1 <- 2
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape3.q , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <-
c(scaL.names, sha3.names)
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
vfamily = c("lomax"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 2
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
rlomax(nsim * length(qq), scale = Scale, shape3.q = qq)
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint 1 < q has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
dq.deta <- dtheta.deta(qq, .lshape3.q , earg = .eshape3.q )
myderiv <-
c(w) * cbind(dl.dscale * dscale.deta,
dl.dq * dq.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dq <- temp5b - temp5
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
wz <-
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))))
} # lomax
fisk <-
function(lscale = "loglink",
lshape1.a = "loglink",
iscale = NULL,
ishape1.a = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
new("vglmff",
blurb =
c("Fisk distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), "\n",
"Mean: scale * gamma(1 + 1/shape1.a) * ",
"gamma(1 - 1/shape1.a)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "fisk", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a") else
c("shape1.a", "scale"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
scrambleseed <- runif(1) # To scramble the seed
qnorm(pfisk(y, scale = Scale, shape1.a = aa))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
predictors.names <- if ( .lss ) {
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE),
namesof(scaL.names , .lscale , .escale , tag = FALSE))
}
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
try.this <-
grid.search4(gscale, gshape1.a, vov3 = 1, vov4 = 1,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
} else { # .imethod == 2
qvec <- .probs.y
iscale <- if (length( .iscale )) .iscale else 1
xvec <- log( quantile(yvec / iscale, probs = qvec) )
fit0 <- lsfit(x = xvec, y = logitlink(qvec),
intercept = FALSE)
sc.init[, spp.] <- iscale
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else
fit0$coef
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 +
aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init
}
etastart <-
if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[aa <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 2
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names)
} else {
c(sha1.names, scaL.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly)
if ( .lss ) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape1.a
} else {
misc$earg[[M1*ii-1]] <- .eshape1.a
misc$earg[[M1*ii ]] <- .escale
}
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dfisk(x = y, scale = Scale, shape1.a = aa,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
vfamily = c("fisk"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
rfisk(nsim * length(aa), shape1.a = aa, scale = Scale)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a < 1 < a has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , earg = .eshape1.a )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
weight = eval(substitute(expression({
ned2l.da <- (1 + (-6 + pi^2) / 9) / aa^2
ned2l.dscale <- ((aa / Scale)^2) / 3
wz <- matrix(0, n, M) # Diagonal
if ( .lss ) {
wz[, c(TRUE, FALSE)] <- ned2l.dscale * dscale.deta^2
wz[, c(FALSE, TRUE)] <- ned2l.da * da.deta^2
} else {
wz[, c(TRUE, FALSE)] <- ned2l.da * da.deta^2
wz[, c(FALSE, TRUE)] <- ned2l.dscale * dscale.deta^2
}
c(w) * wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))))
} # fisk
inv.lomax <- function(lscale = "loglink",
lshape2.p = "loglink",
iscale = NULL,
ishape2.p = NULL,
imethod = 1,
gscale = exp(-5:5),
gshape2.p = exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape2.p") {
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
new("vglmff",
blurb =
c("Inverse Lomax distribution \n\n",
"Links: ",
namesof("scale" , lscale , earg = escale), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), "\n",
"Mean: does not exist"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "inv.lomax", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = c("scale", "shape2.p"),
lscale = .lscale ,
escale = .escale ,
lshape2.p = .lshape2.p ,
eshape2.p = .eshape2.p )
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
scrambleseed <- runif(1) # To scramble the seed
qnorm(pinv.lomax(y, scale = Scale, shape2.p = parg))
}, list( .lscale = lscale , .lshape2.p = lshape2.p,
.escale = escale , .eshape2.p = eshape2.p ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
predictors.names <-
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha2.names , .lshape2.p , .eshape2.p , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
pp.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape2.p <- .gshape2.p
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape2.p )) gshape2.p <-
rep_len( .ishape2.p , NOS)
try.this <-
grid.search4(gscale, vov2 = 1, gshape2.p, vov4 = 1,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
pp.init[, spp.] <- try.this["Value3"]
} else { # .imethod == 2
qvec <- .probs.y
ishape2.p <- if (length( .ishape2.p )) .ishape2.p else 1
xvec <- log( qvec^(-1/ ishape2.p) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
pp.init[, spp.] <- ishape2.p
}
} # End of for (spp. ...)
etastart <-
cbind(theta2eta(sc.init, .lscale , earg = .escale ),
theta2eta(pp.init, .lshape2.p , earg = .eshape2.p ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale ,
.escale = escale ,
.iscale = iscale ,
.gscale = gscale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.ishape2.p = ishape2.p,
.gshape2.p = gshape2.p,
.imethod = imethod , .probs.y = probs.y
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qinv.lomax(p = 0.5, scale = Scale, shape2.p = parg)
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
last = eval(substitute(expression({
M1 <- 2
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape2.p , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(scaL.names, sha2.names)
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape2.p
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = 1,
shape2.p = parg, shape3.q = 1,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
vfamily = c("inv.lomax"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
aa <- 1
qq <- 1
rinv.lomax(nsim * length(Scale), scale = Scale, shape2.p = parg)
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- 1
aa <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 ) else TRUE
if (!okay.support)
warning("parameter constraint -a*p < 1 has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- 1
aa <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
dp.deta <- dtheta.deta(parg, .lshape2.p , earg = .eshape2.p )
myderiv <-
c(w) * cbind(dl.dscale * dscale.deta,
dl.dp * dp.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
wz <-
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dscalep * dscale.deta * dp.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))))
} # inv.lomax
paralogistic <-
function(lscale = "loglink",
lshape1.a = "loglink",
iscale = NULL,
ishape1.a = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
new("vglmff",
blurb =
c("Paralogistic distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), "\n",
"Mean: scale * gamma(1 + 1/shape1.a) * ",
"gamma(shape1.a - 1/shape1.a) / ",
"gamma(shape1.a)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "paralogistic", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a") else
c("shape1.a", "scale"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
scrambleseed <- runif(1) # To scramble the seed
qnorm(pparalogistic(y, scale = Scale, shape1.a = aa))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
predictors.names <-
if ( .lss ) {
c(namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE),
namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE),
namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE))
}
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
paralogistic.Loglikfun2 <-
function(scaleval, shape1.a,
y, x, w, extraargs) {
sum(c(w) * dgenbetaII(x = y, scale = scaleval,
shape1.a = shape1.a,
shape2.p = 1,
shape3.q = shape1.a, log = TRUE))
}
try.this <-
grid.search2(gscale, gshape1.a, # vov3 = 1, vov4 = gshape1.a,
objfun = paralogistic.Loglikfun2, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
} else { # .imethod == 2
qvec <- .probs.y
ishape3.q <- if (length( .ishape1.a )) .ishape1.a else 1
xvec <- log( (1-qvec)^(-1/ ishape3.q) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else
1/fit0$coef[2]
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init * aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init * aa.init
}
etastart <- if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 2
misc$link <- c(rep_len(if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len(if ( .lss ) .lshape1.a else .lscale ,
ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names)
} else {
c(sha1.names, scaL.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly)
if ( .lss ) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape1.a
} else {
misc$earg[[M1*ii-1]] <- .eshape1.a
misc$earg[[M1*ii ]] <- .escale
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = aa,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
vfamily = c("paralogistic"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
rparalogistic(nsim * length(Scale), shape1.a = aa, scale = Scale)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a < 1 < a*a has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss))))
} # paralogistic
inv.paralogistic <-
function(lscale = "loglink",
lshape1.a = "loglink",
iscale = NULL,
ishape1.a = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
new("vglmff",
blurb =
c("Inverse paralogistic distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), "\n",
"Mean: scale * gamma(shape1.a + 1/shape1.a) * ",
"gamma(1 - 1/shape1.a) / ",
"gamma(shape1.a)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "inv.paralogistic", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a") else
c("shape1.a", "scale"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
scrambleseed <- runif(1) # To scramble the seed
qnorm(pinv.paralogistic(y, scale = Scale, shape1.a = aa))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
predictors.names <- if ( .lss ) {
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE),
namesof(scaL.names , .lscale , .escale , tag = FALSE))
}
predictors.names <- predictors.names[interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
inv.paralogistic.Loglikfun2 <-
function(scaleval, shape1.a,
y, x, w, extraargs) {
sum(c(w) * dgenbetaII(x = y, scale = scaleval,
shape1.a = shape1.a,
shape2.p = shape1.a,
shape3.q = 1, log = TRUE))
}
try.this <-
grid.search2(gscale, gshape1.a, # vov3 = 1, vov4 = gshape1.a,
objfun = inv.paralogistic.Loglikfun2, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
} else { # .imethod == 2
qvec <- .probs.y
ishape2.p <- if (length( .ishape1.a )) .ishape1.a else 1
xvec <- log( qvec^(-1/ ishape2.p) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else
-1/fit0$coef[2]
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init
}
etastart <- if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 2
misc$link <- c(rep_len(if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len(if ( .lss ) .lshape1.a else .lscale ,
ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names)
} else {
c(sha1.names, scaL.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly)
if ( .lss ) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape1.a
} else {
misc$earg[[M1*ii-1]] <- .eshape1.a
misc$earg[[M1*ii ]] <- .escale
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = aa, shape3.q = 1,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
vfamily = c("inv.paralogistic"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
rinv.paralogistic(nsim * length(Scale), shape1.a = aa, scale = Scale)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a*a < 1 < a has",
" been violated; solution may be at the ",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , earg = .eshape1.a )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))))
} # inv.paralogistic
Try the VGAM package in your browser
Any scripts or data that you put into this service are public.
VGAM documentation built on Sept. 19, 2023, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions inv.paralogistic paralogistic inv.lomax fisk lomax betaII dagum sinmad dinv.paralogistic dinv.lomax ddagum dparalogistic dfisk dlomax dsinmad dbetaII pinv.paralogistic pinv.lomax pdagum pparalogistic pfisk plomax psinmad qinv.paralogistic qinv.lomax qdagum qparalogistic qfisk qlomax qsinmad rinv.paralogistic rinv.lomax rdagum rparalogistic rfisk rlomax rsinmad dgenbetaII genbetaII genbetaII.Loglikfun4 exponential.mo exponential.mo.control gompertz gompertz.control rgompertz qgompertz pgompertz dgompertz makeham makeham.control rmakeham qmakeham pmakeham dmakeham perks perks.control rperks qperks pperks dperks qbeard dbeard dbeard pmperks dmperks pmbeard dmbeard gumbelII rgumbelII qgumbelII pgumbelII dgumbelII
Documented in betaII dagum dbetaII ddagum dfisk dgenbetaII dgompertz dgumbelII dinv.lomax dinv.paralogistic dlomax dmakeham dparalogistic dperks dsinmad fisk genbetaII genbetaII.Loglikfun4 gompertz gumbelII inv.lomax inv.paralogistic lomax makeham paralogistic pdagum perks pfisk pgompertz pgumbelII pinv.lomax pinv.paralogistic plomax pmakeham pparalogistic pperks psinmad qdagum qfisk qgompertz qgumbelII qinv.lomax qinv.paralogistic qlomax qmakeham qparalogistic qperks qsinmad rdagum rfisk rgompertz rgumbelII rinv.lomax rinv.paralogistic rlomax rmakeham rparalogistic rperks rsinmad sinmad
# These functions are
# Copyright (C) 1998-2023 T.W. Yee, University of Auckland.
# All rights reserved.
dgumbelII <- function(x, scale = 1, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
ans <- x
index0 <- (x < 0) & is.finite(x) & !is.na(x)
ans[!index0] <- log(shape[!index0] / scale[!index0]) +
(shape[!index0] + 1) * log(scale[!index0] / x[!index0]) -
(x[!index0] / scale[!index0])^(-shape[!index0])
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
}
pgumbelII <- function(q, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
# 20150121 KaiH
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
# 20150121 KaiH
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
# 20150121 KaiH
if (lower.tail) {
if (log.p) {
ans <- -(q / scale)^(-shape)
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- exp(-(q / scale)^(-shape))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- log(-expm1(-(q / scale)^(-shape)))
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- -expm1(-(q / scale)^(-shape))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
}
qgumbelII <- function(p, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- scale * (-ln.p)^(-1 / shape)
ans[ln.p > 0] <- NaN
} else { # Default
ans <- scale * (-log(p))^(-1 / shape)
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- scale * (-log(-expm1(ln.p)))^(-1 / shape)
ans[ln.p > 0] <- NaN
} else {
ans <- scale * (-log1p(-p))^(-1 / shape)
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
}
rgumbelII <- function(n, scale = 1, shape) {
qgumbelII(runif(n), shape = shape, scale = scale)
}
gumbelII <-
function(lscale = "loglink", lshape = "loglink",
iscale = NULL, ishape = NULL,
probs.y = c(0.2, 0.5, 0.8),
perc.out = NULL, # 50,
imethod = 1, zero = "shape", nowarning = FALSE) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 2)
stop("argument 'imethod' must be 1 or 2")
if (!is.Numeric(probs.y, positive = TRUE) ||
length(probs.y) < 2 ||
max(probs.y) >= 1)
stop("bad input for argument 'probs.y'")
if (length(perc.out))
if (!is.Numeric(perc.out, positive = TRUE) ||
max(probs.y) >= 100)
stop("bad input for argument 'perc.out'")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
new("vglmff",
blurb = c("Gumbel Type II distribution\n\n",
"Links: ",
namesof("scale", lscale, escale), ", ",
namesof("shape", lshape, eshape), "\n",
"Mean: scale^(1/shape) * gamma(1 - 1 / shape)\n",
"Variance: scale^(2/shape) * (gamma(1 - 2/shape) - ",
"gamma(1 + 1/shape)^2)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "gumbelII",
parameters.names = c("scale", "shape"),
perc.out = .perc.out ,
zero = .zero )
}, list( .zero = zero,
.perc.out = perc.out
))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
Shape.init <- matrix(if (length( .ishape ))
.ishape else 0 + NA,
n, ncoly, byrow = TRUE)
Scale.init <- matrix(if (length( .iscale ))
.iscale else 0 + NA,
n, ncoly, byrow = TRUE)
if (!length(etastart)) {
if (!length( .ishape ) ||
!length( .iscale )) {
for (ilocal in 1:ncoly) {
anyc <- FALSE # extra$leftcensored|extra$rightcensored
i11 <- if ( .imethod == 1)
anyc else FALSE # can be all data
probs.y <- .probs.y
xvec <- log(-log(probs.y))
fit0 <- lsfit(y = xvec,
x = log(quantile(y[!i11, ilocal],
probs = probs.y )))
if (!is.Numeric(Shape.init[, ilocal]))
Shape.init[, ilocal] <- -fit0$coef["X"]
if (!is.Numeric(Scale.init[, ilocal]))
Scale.init[, ilocal] <-
exp(fit0$coef["Intercept"] / Shape.init[, ilocal])
} # ilocal
etastart <-
cbind(theta2eta(Scale.init, .lscale , .escale ),
theta2eta(Shape.init, .lshape , .eshape ))[,
interleave.VGAM(M, M1 = M1)]
}
}
}), list(
.lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.iscale = iscale, .ishape = ishape,
.probs.y = probs.y,
.imethod = imethod ) )),
linkinv = eval(substitute(function(eta, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
Shape <- as.matrix(Shape)
if (length( .perc.out ) > 1 && ncol(Shape) > 1)
stop("argument 'perc.out' should be of length one since ",
"there are multiple responses")
if (!length( .perc.out )) {
return(Scale * gamma(1 - 1 / Shape))
}
ans <- if (length( .perc.out ) > 1) {
qgumbelII(p = matrix( .perc.out / 100, length(Shape),
length( .perc.out ), byrow = TRUE),
shape = Shape, scale = Scale)
} else {
qgumbelII(p = .perc.out / 100, Shape, scale = Scale)
}
colnames(ans) <- paste0(as.character( .perc.out ), "%")
ans
}, list(
.lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.perc.out = perc.out ) )),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[
interleave.VGAM(M, M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape
}
misc$M1 <- M1
misc$imethod <- .imethod
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$perc.out <- .perc.out
misc$true.mu <- FALSE # @fitted is not a true mu
}), list(
.lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.perc.out = perc.out,
.imethod = imethod ) )),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dgumbelII(x = y, shape = Shape,
scale = Scale, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape
) )),
vfamily = c("gumbelII"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(Shape)) && all(0 < Shape)
okay1
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape) )),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
rgumbelII(nsim * length(Scale), shape = Shape, scale = Scale)
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape
) )),
deriv = eval(substitute(expression({
M1 <- 2
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
dl.dshape <- 1 / Shape + log(Scale / y) -
log(Scale / y) * (Scale / y)^Shape
dl.dscale <- Shape / Scale -
(Shape / y) * (Scale / y)^(Shape - 1)
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
dshape.deta <- dtheta.deta(Shape, .lshape , .eshape )
myderiv <- c(w) * cbind(dl.dscale, dl.dshape) *
cbind(dscale.deta, dshape.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape
) )),
weight = eval(substitute(expression({
EulerM <- -digamma(1.0)
ned2l.dshape2 <- (1 + trigamma(2) + digamma(2)^2) / Shape^2
ned2l.dscale2 <- (Shape / Scale)^2
ned2l.dshapescale <- digamma(2) / Scale
wz <- array(c(c(w) * ned2l.dscale2 * dscale.deta^2,
c(w) * ned2l.dshape2 * dshape.deta^2,
c(w) * ned2l.dshapescale * dscale.deta *
dshape.deta),
dim = c(n, M / M1, 3))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale, .lshape = lshape ))))
}
dmbeard <-
function(x, shape, scale = 1, rho, epsilon, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(rho), length(epsilon))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(rho) != LLL) rho <- rep_len(rho, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
index0 <- (x < 0)
ans <- log(epsilon * exp(-x * scale) + shape) +
(-epsilon * x -
((rho * epsilon - 1) / (rho * scale)) *
(log1p(rho * shape) -
log(exp(-x * scale) + rho * shape) - scale * x)) -
log(exp(-x * scale) + shape * rho)
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | rho <= 0 | epsilon <= 0] <- NaN
ans
}
pmbeard <- function(q, shape, scale = 1, rho, epsilon) {
LLL <- max(length(q), length(shape), length(scale),
length(rho), length(epsilon))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(rho) != LLL) rho <- rep_len(rho, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
ans <- -expm1(-epsilon * q -
((rho * epsilon - 1) / (rho * scale)) *
(log1p(rho * shape) -
log(exp(-scale * q) + rho * shape) - scale * q))
ans[(q <= 0)] <- 0
ans[shape <= 0 | scale <= 0 | rho <= 0 | epsilon <= 0] <- NaN
ans[q == Inf] <- 1
ans
}
dmperks <-
function(x, scale = 1, shape, epsilon, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(epsilon))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
index0 <- (x < 0)
ans <- log(epsilon * exp(-x * scale) + shape) +
(-epsilon * x -
((epsilon - 1) / scale) *
(log1p(shape) -
log(shape + exp(-x * scale)) -x * scale)) -
log(exp(-x * scale) + shape)
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | epsilon <= 0] <- NaN
ans
} # dmperks
pmperks <- function(q, scale = 1, shape, epsilon) {
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
ans <- -expm1(-epsilon * q -
((epsilon - 1) / scale) *
(log1p(shape) -
log(shape + exp(-q * scale)) - q * scale))
ans[(q <= 0)] <- 0
ans[shape <= 0 | scale <= 0] <- NaN
ans[q == Inf] <- 1
ans
}
dbeard <- function(x, shape, scale = 1, rho, log = FALSE) {
warning("does not integrate to unity")
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(rho))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(rho) != LLL) rho <- rep_len(rho, LLL)
index0 <- (x < 0)
ans <- log(shape) - x * scale * (rho^(-1 / scale)) +
log(rho) + log(scale) +
(rho^(-1 / scale)) * log1p(shape * rho) -
(1 + rho^(-1 / scale)) *
log(shape * rho + exp(-x * scale))
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | rho <= 0] <- NaN
ans
}
dbeard <- function(x, shape, scale = 1, rho, log = FALSE) {
alpha <- shape
beta <- scale
warning("does not integrate to unity")
ret <- ifelse(x <= 0 | beta <= 0, NaN,
exp(alpha+beta*x)*
(1+exp(alpha+rho))**(exp(-rho/beta)) /
(1+exp(alpha+rho+beta*x))**(1+exp(-rho/beta)))
ret
}
qbeard <- function(x, u = 0.5, alpha = 1, beta = 1,rho = 1) {
ret <-
ifelse(x <= 0 | u <= 0 | u >= 1 |
length(x) != length(u) | beta <= 0,
NaN,
(1/beta) * (log((u**(-beta*exp(rho))) *
(1+exp(alpha+rho+beta*x))-1)-alpha-rho)-x)
return(ret)
}
dperks <- function(x, scale = 1, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
index0 <- (x < 0)
ans <- log(shape) - x +
log1p(shape) / scale -
(1 + 1 / scale) * log(shape + exp(-x * scale))
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # dperks
pperks <- function(q, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
logS <- -q + (log1p(shape) -
log(shape + exp(-q * scale))) / scale
if (lower.tail) {
if (log.p) {
ans <- log(-expm1(logS))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1(logS)
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- logS
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp(logS)
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # pperks
qperks <-
function(p, scale = 1, shape, lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
tmp <- scale * log(-expm1(ln.p))
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[ln.p > 0] <- NaN
} else {
tmp <- scale * log1p(-p)
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
tmp <- scale * ln.p
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[ln.p > 0] <- NaN
} else {
tmp <- scale * log(p)
onemFb <- exp(tmp)
ans <- (log1p(shape - onemFb) - log(shape) - tmp) / scale
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # qperks
rperks <- function(n, scale = 1, shape) {
qperks(runif(n), scale = scale, shape = shape)
}
perks.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
perks <-
function(lscale = "loglink", lshape = "loglink",
iscale = NULL, ishape = NULL,
gscale = exp(-5:5), gshape = exp(-5:5),
nsimEIM = 500,
oim.mean = FALSE,
zero = NULL, nowarning = FALSE) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
if (!is.logical(oim.mean) || length(oim.mean) != 1)
stop("bad input for argument 'oim.mean'")
new("vglmff",
blurb = c("Perks' distribution\n\n",
"Links: ",
namesof("scale", lscale, escale), ", ",
namesof("shape", lshape, eshape), "\n",
"Median: qperks(p = 0.5, scale = scale, shape = shape)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "perks",
nsimEIM = .nsimEIM ,
parameters.names = c("scale", "shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matH <- matrix(if (length( .ishape )) .ishape else 0 + NA,
n, ncoly, byrow = TRUE)
matC <- matrix(if (length( .iscale )) .iscale else 0 + NA,
n, ncoly, byrow = TRUE)
shape.grid <- .gshape
scale.grid <- .gscale
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
perks.Loglikfun2 <- function(scaleval, shapeval,
y, x, w, extraargs) {
sum(c(w) * dperks(x = y, shape = shapeval,
scale = scaleval, log = TRUE))
}
try.this <-
grid.search2(scale.grid, shape.grid,
objfun = perks.Loglikfun2,
y = yvec, w = wvec,
ret.objfun = TRUE) # Last value is \ell
if (!length( .iscale ))
matC[, spp.] <- try.this["Value1"]
if (!length( .ishape ))
matH[, spp.] <- try.this["Value2"]
} # spp.
etastart <-
cbind(theta2eta(matC, .lscale , .escale ),
theta2eta(matH, .lshape , .eshape ))[,
interleave.VGAM(M, M1 = M1)]
} # End of !length(etastart)
}), list( .lscale = lscale, .lshape = lshape,
.eshape = eshape, .escale = escale,
.gshape = gshape, .gscale = gscale,
.ishape = ishape, .iscale = iscale
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
qperks(p = 0.5, shape = Shape, scale = Scale)
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
last = eval(substitute(expression({
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M,
M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape
}
misc$M1 <- M1
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dperks(x = y, shape = Shape,
scale = Scale, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
vfamily = c("perks"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(Shape)) && all(0 < Shape)
okay1
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape) )),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
rperks(nsim * length(Scale), shape = Shape, scale = Scale)
}, list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
deriv = eval(substitute(expression({
M1 <- 2
scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
temp2 <- exp(y * scale)
temp3 <- 1 + shape * temp2
dl.dshape <- 1 / shape + 1 / (scale * (1 + shape)) -
(1 + 1 / scale) * temp2 / temp3
dl.dscale <- y - log1p(shape) / scale^2 +
log1p(shape * temp2) / scale^2 -
(1 + 1 / scale) * shape * y * temp2 / temp3
dshape.deta <- dtheta.deta(shape, .lshape , .eshape )
dscale.deta <- dtheta.deta(scale, .lscale , .escale )
dthetas.detas <- cbind(dscale.deta, dshape.deta)
myderiv <- c(w) * cbind(dl.dscale, dl.dshape) * dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale, .lshape = lshape,
.escale = escale, .eshape = eshape ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1) # wz is 'tridiagonal'
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE)
for (spp. in 1:NOS) {
run.varcov <- 0
Scale <- scale[, spp.]
Shape <- shape[, spp.]
if (FALSE && intercept.only && .oim.mean ) {
stop("this is wrong")
temp8 <- (1 + Shape * exp(Scale * y[, spp.]))^2
nd2l.dadb <- 2 * y[, spp.] * exp(Scale * y[, spp.]) / temp8
nd2l.dada <- 1 / Shape^2 + 1 / (1 + Shape)^2 -
2 * exp(2 * Scale * y[, spp.]) / temp8
nd2l.dbdb <- 2 * Shape * y[, spp.]^2 *
exp(Scale * y[, spp.]) / temp8
ave.oim11 <- weighted.mean(nd2l.dada, w[, spp.])
ave.oim12 <- weighted.mean(nd2l.dadb, w[, spp.])
ave.oim22 <- weighted.mean(nd2l.dbdb, w[, spp.])
run.varcov <- cbind(ave.oim11, ave.oim22, ave.oim12)
} else {
for (ii in 1:( .nsimEIM )) {
ysim <- rperks(n = n, shape = Shape, scale = Scale)
if (ii < 3) {
}
temp2 <- exp(ysim * Scale)
temp3 <- 1 + Shape * temp2
dl.dshape <- 1 / Shape + 1 / (Scale * (1 + Shape)) -
(1 + 1 / Scale) * temp2 / temp3
dl.dscale <- ysim - log1p(Shape) / Scale^2 +
log1p(Shape * temp2) / Scale^2 -
(1 + 1 / Scale) * Shape * ysim * temp2 / temp3
temp7 <- cbind(dl.dscale, dl.dshape)
if (ii < 3) {
}
run.varcov <- run.varcov +
temp7[, ind1$row.index] *
temp7[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM )
}
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov), n, ncol(run.varcov),
byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) {
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay,
M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}), list( .lscale = lscale,
.escale = escale,
.nsimEIM = nsimEIM, .oim.mean = oim.mean ))))
} # perks()
dmakeham <-
function(x, scale = 1, shape, epsilon = 0, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale),
length(epsilon))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
index0 <- (x < 0)
ans <- log(epsilon * exp(-x * scale) + shape) +
x * (scale - epsilon) -
(shape / scale) * expm1(x * scale)
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0 | epsilon < 0] <- NaN
ans
} # dmakeham
pmakeham <- function(q, scale = 1, shape, epsilon = 0,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale),
length(epsilon))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
if (lower.tail) {
if (log.p) {
ans <- log(-expm1(-q * epsilon - (shape / scale) *
expm1(scale * q)))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1(-q * epsilon - (shape / scale) *
expm1(scale * q))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- -q * epsilon - (shape / scale) * expm1(scale * q)
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp(-q * epsilon - (shape / scale) * expm1(scale * q))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0 | epsilon < 0] <- NaN
ans
} # pmakeham
qmakeham <- function(p, scale = 1, shape, epsilon = 0,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale),
length(epsilon))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(epsilon) != LLL) epsilon <- rep_len(epsilon, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- shape / (scale * epsilon) -
log(-expm1(ln.p)) / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
exp(log(-expm1(ln.p)) * (-scale / epsilon))) / scale
ans[ln.p == 0] <- Inf
ans[ln.p > 0] <- NaN
} else {
ans <- shape / (scale * epsilon) - log1p(-p) / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
exp( (-scale / epsilon) * log1p(-p) )) / scale
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- shape / (scale * epsilon) - ln.p / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
exp(ln.p * (-scale / epsilon))) / scale
ans[ln.p == -Inf] <- Inf
ans[ln.p > 0] <- NaN
} else {
ans <- shape / (scale * epsilon) - log(p) / epsilon -
lambertW((shape / epsilon) * exp(shape / epsilon) *
p^(-scale / epsilon)) / scale
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[epsilon == 0] <-
qgompertz(p = p[epsilon == 0],
shape = shape[epsilon == 0],
scale = scale[epsilon == 0],
lower.tail = lower.tail,
log.p = log.p)
ans[shape <= 0 | scale <= 0 | epsilon < 0] <- NaN
ans
} # qmakeham
rmakeham <- function(n, scale = 1, shape, epsilon = 0) {
qmakeham(runif(n), scale = scale, shape, epsilon = epsilon)
}
makeham.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
makeham <-
function(lscale = "loglink", lshape = "loglink",
lepsilon = "loglink",
iscale = NULL, ishape = NULL,
iepsilon = NULL, # 0.3,
gscale = exp(-5:5),
gshape = exp(-5:5),
gepsilon = exp(-4:1),
nsimEIM = 500,
oim.mean = TRUE,
zero = NULL, nowarning = FALSE) {
lepsil <- lepsilon
iepsil <- iepsilon
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lepsil <- as.list(substitute(lepsil))
eepsil <- link2list(lepsil)
lepsil <- attr(eepsil, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
if (length(iepsil))
if (!is.Numeric(iepsil, positive = TRUE))
stop("argument 'iepsil' values must be positive")
if (!is.logical(oim.mean) || length(oim.mean) != 1)
stop("bad input for argument 'oim.mean'")
new("vglmff",
blurb = c("Makeham distribution\n\n",
"Links: ",
namesof("scale", lscale, escale), ", ",
namesof("shape", lshape, eshape), ", ",
namesof("epsilon", lepsil, eepsil), "\n",
"Median: qmakeham(p = 0.5, scale, shape, epsilon)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "makeham",
nsimEIM = .nsimEIM,
parameters.names = c("scale", "shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 3
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
mynames3 <- param.names("epsilon", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE),
namesof(mynames3, .lepsil , .eepsil , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matC <- matrix(if (length( .iscale )) .iscale else 0 + NA,
n, ncoly, byrow = TRUE)
matH <- matrix(if (length( .ishape )) .ishape else 0 + NA,
n, ncoly, byrow = TRUE)
matE <- matrix(if (length( .iepsil )) .iepsil else 0.3,
n, ncoly, byrow = TRUE)
shape.grid <- unique(sort(c( .gshape )))
scale.grid <- unique(sort(c( .gscale )))
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
makeham.Loglikfun2 <- function(scaleval, shapeval,
y, x, w, extraargs) {
sum(c(w) * dmakeham(x = y, shape = shapeval,
epsilon = extraargs$Epsil,
scale = scaleval, log = TRUE))
}
try.this <-
grid.search2(scale.grid, shape.grid,
objfun = makeham.Loglikfun2,
y = yvec, w = wvec,
extraargs = list(Epsilon = matE[1, spp.]),
ret.objfun = TRUE) # Last value is \ell
if (!length( .iscale ))
matC[, spp.] <- try.this["Value1"]
if (!length( .ishape ))
matH[, spp.] <- try.this["Value2"]
} # spp.
epsil.grid <- c( .gepsil )
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
makeham.Loglikfun2 <-
function(epsilval, y, x, w, extraargs) {
ans <-
sum(c(w) * dmakeham(x = y, shape = extraargs$Shape,
epsilon = epsilval, log = TRUE,
scale = extraargs$Scale))
ans
}
Init.epsil <-
grid.search(epsil.grid,
objfun = makeham.Loglikfun2,
y = yvec, x = x, w = wvec,
extraargs = list(Shape = matH[1, spp.],
Scale = matC[1, spp.]))
matE[, spp.] <- Init.epsil
} # spp.
etastart <- cbind(theta2eta(matC, .lscale , .escale ),
theta2eta(matH, .lshape , .eshape ),
theta2eta(matE, .lepsil , .eepsil ))[,
interleave.VGAM(M, M1 = M1)]
} # End of !length(etastart)
}),
list(
.lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil,
.gshape = gshape, .gscale = gscale, .gepsil = gepsilon,
.ishape = ishape, .iscale = iscale, .iepsil = iepsil
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE)],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE)],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE)],
.lepsil , .eepsil )
qmakeham(p = 0.5, scale = scale, shape, epsil = epsil)
},
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly),
rep_len( .lepsil , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2, mynames3)[
interleave.VGAM(M, M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape
misc$earg[[M1*ii ]] <- .eepsil
}
misc$M1 <- M1
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}),
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE)],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE)],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE)],
.lepsil , .eepsil )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dmakeham(y, scale = scale, shape,
epsil = epsil, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
},
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
vfamily = c("makeham"),
# ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE), drop = FALSE],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE), drop = FALSE],
.lepsil , .eepsil )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(shape)) && all(0 < shape) &&
all(is.finite(epsil)) && all(0 < epsil)
okay1
},
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE), drop = FALSE],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE), drop = FALSE],
.lepsil , .eepsil )
rmakeham(nsim * length(Scale),
scale = c(Scale), shape = c(shape), epsilon = c(epsil))
}, list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
deriv = eval(substitute(expression({
scale <- eta2theta(eta[, c(TRUE, FALSE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE, FALSE), drop = FALSE],
.lshape , .eshape )
epsil <- eta2theta(eta[, c(FALSE, FALSE, TRUE), drop = FALSE],
.lepsil , .eepsil )
temp2 <- exp(y * scale)
temp3 <- epsil + shape * temp2
dl.dshape <- temp2 / temp3 - expm1(y * scale) / scale
dl.dscale <- shape * y * temp2 / temp3 +
shape * expm1(y * scale) / scale^2 -
shape * y * temp2 / scale
dl.depsil <- 1 / temp3 - y
dshape.deta <- dtheta.deta(shape, .lshape , .eshape )
dscale.deta <- dtheta.deta(scale, .lscale , .escale )
depsil.deta <- dtheta.deta(epsil, .lepsil , .eepsil )
dthetas.detas <- cbind(dscale.deta, dshape.deta, depsil.deta)
myderiv <- c(w) * cbind(dl.dscale,
dl.dshape,
dl.depsil) * dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}),
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1 + M - 2) # wz has half-bandwidth 3
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE) # Use SFS
for (spp. in 1:NOS) {
run.varcov <- 0
Shape <- shape[, spp.]
Scale <- scale[, spp.]
Epsil <- epsil[, spp.]
for (ii in 1:( .nsimEIM )) {
ysim <- rmakeham(n, scale = Scale, Shape, epsil = Epsil)
temp2 <- exp(ysim * Scale)
temp3 <- Epsil + Shape * temp2
dl.dshape <- temp2 / temp3 - expm1(ysim * Scale) / Scale
dl.dscale <- Shape * ysim * temp2 / temp3 +
Shape * expm1(ysim * Scale) / Scale^2 -
Shape * ysim * temp2 / Scale
dl.depsil <- 1 / temp3 - ysim
temp7 <- cbind(dl.dscale, dl.dshape, dl.depsil)
run.varcov <- run.varcov + temp7[, ind1$row.index] *
temp7[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM )
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov, na.rm = TRUE),
n, ncol(run.varcov), byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) { # Now copy wz1 into wz
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay, M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}),
list( .lshape = lshape, .lscale = lscale, .lepsil = lepsil,
.eshape = eshape, .escale = escale, .eepsil = eepsil,
.nsimEIM = nsimEIM, .oim.mean = oim.mean ))))
} # makeham()
dgompertz <- function(x, scale = 1, shape, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape), length(scale))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
index0 <- (x < 0)
index1 <- abs(x * scale) < 0.1 & is.finite(x * scale)
ans <- log(shape) + x * scale -
(shape / scale) * (exp(x * scale) - 1)
ans[index1] <- log(shape[index1]) + x[index1] * scale[index1] -
(shape[index1] / scale[index1]) *
expm1(x[index1] * scale[index1])
ans[index0] <- log(0)
ans[x == Inf] <- log(0)
if (log.arg) {
} else {
ans <- exp(ans)
ans[index0] <- 0
ans[x == Inf] <- 0
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # dgompertz
pgompertz <- function(q, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape), length(scale))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ans <- log1p(-exp((-shape / scale) * expm1(scale * q)))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- -expm1((-shape / scale) * expm1(scale * q))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- (-shape / scale) * expm1(scale * q)
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- exp((-shape / scale) * expm1(scale * q))
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # pgompertz
qgompertz <- function(p, scale = 1, shape,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape), length(scale))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape) != LLL) shape <- rep_len(shape, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- log1p((-scale / shape) * log(-expm1(ln.p))) / scale
ans[ln.p > 0] <- NaN
} else {
ans <- log1p((-scale / shape) * log1p(-p)) / scale
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- log1p((-scale / shape) * ln.p) / scale
ans[ln.p > 0] <- NaN
} else {
ans <- log1p((-scale / shape) * log(p)) / scale
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[shape <= 0 | scale <= 0] <- NaN
ans
} # qgompertz
rgompertz <- function(n, scale = 1, shape) {
qgompertz(runif(n), scale = scale, shape = shape)
}
gompertz.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
gompertz <-
function(lscale = "loglink", lshape = "loglink",
iscale = NULL, ishape = NULL,
nsimEIM = 500,
zero = NULL, nowarning = FALSE) {
lshape <- as.list(substitute(lshape))
eshape <- link2list(lshape)
lshape <- attr(eshape, "function.name")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ishape))
if (!is.Numeric(ishape, positive = TRUE))
stop("argument 'ishape' values must be positive")
if (length(iscale))
if (!is.Numeric(iscale, positive = TRUE))
stop("argument 'iscale' values must be positive")
new("vglmff",
blurb = c("Gompertz distribution\n\n",
"Links: ",
namesof("scale", lscale, escale ), ", ",
namesof("shape", lshape, eshape ), "\n",
"Median: scale * log(2 - 1 / shape)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "gompertz",
nsimEIM = .nsimEIM,
parameters.names = c("scale", "shape"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("scale", ncoly, skip1 = TRUE)
mynames2 <- param.names("shape", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lscale , .escale , tag = FALSE),
namesof(mynames2, .lshape , .eshape , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matH <- matrix(if (length( .ishape )) .ishape else 0 + NA,
n, ncoly, byrow = TRUE)
matC <- matrix(if (length( .iscale )) .iscale else 0 + NA,
n, ncoly, byrow = TRUE)
shape.grid <- c(exp(-seq(4, 0.1, len = 07)), 1,
exp( seq(0.1, 4, len = 07)))
scale.grid <- c(exp(-seq(4, 0.1, len = 07)), 1,
exp( seq(0.1, 4, len = 07)))
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
wvec <- w[, spp.]
gompertz.Loglikfun <-
function(scaleval, y, x, w, extraargs) {
ans <-
sum(c(w) * dgompertz(x = y, shape = extraargs$Shape,
scale = scaleval, log = TRUE))
ans
}
mymat <- matrix(-1, length(shape.grid), 2)
for (jlocal in seq_along(shape.grid)) {
mymat[jlocal, ] <-
grid.search(scale.grid,
objfun = gompertz.Loglikfun,
y = yvec, x = x, w = wvec,
ret.objfun = TRUE,
extraargs = list(Shape = shape.grid[jlocal]))
}
index.shape <- which(mymat[, 2] == max(mymat[, 2]))[1]
if (!length( .ishape ))
matH[, spp.] <- shape.grid[index.shape]
if (!length( .iscale ))
matC[, spp.] <- mymat[index.shape, 1]
} # spp.
etastart <- cbind(theta2eta(matC, .lscale , .escale ),
theta2eta(matH, .lshape , .eshape ))[,
interleave.VGAM(M, M1 = M1)]
} # End of !length(etastart)
}), list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale,
.ishape = ishape, .iscale = iscale
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
log1p((scale / shape) * log(2)) / scale
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M,
M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape
}
misc$M1 <- M1
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale,
.nsimEIM = nsimEIM ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * dgompertz(x = y, scale = scale,
shape = shape, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
vfamily = c("gompertz"),
# ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
validparams = eval(substitute(function(eta, y, extra = NULL) {
Scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
okay1 <- all(is.finite(Scale)) && all(0 < Scale) &&
all(is.finite(shape)) && all(0 < shape)
okay1
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, c(TRUE, FALSE)], .lscale , .escale )
Shape <- eta2theta(eta[, c(FALSE, TRUE)], .lshape , .eshape )
rgompertz(nsim * length(Scale),
shape = c(Shape), scale = c(Scale))
}, list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
deriv = eval(substitute(expression({
M1 <- 2
scale <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lscale , .escale )
shape <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.lshape , .eshape )
temp2 <- exp(y * scale)
temp4 <- -expm1(y * scale)
dl.dshape <- 1 / shape + temp4 / scale
dl.dscale <- y * (1 - shape * temp2 / scale) -
shape * temp4 / scale^2
dscale.deta <- dtheta.deta(scale, .lscale , .escale )
dshape.deta <- dtheta.deta(shape, .lshape , .eshape )
dthetas.detas <- cbind(dscale.deta, dshape.deta)
myderiv <- c(w) * cbind(dl.dscale, dl.dshape) * dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lshape = lshape, .lscale = lscale,
.eshape = eshape, .escale = escale ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1) # wz is 'tridiagonal'
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE)
for (spp. in 1:NOS) {
run.varcov <- 0
Shape <- shape[, spp.]
Scale <- scale[, spp.]
for (ii in 1:( .nsimEIM )) {
ysim <- rgompertz(n = n, shape = Shape, scale = Scale)
if (ii < 3) {
}
temp2 <- exp(ysim * scale)
temp4 <- -expm1(ysim * scale)
dl.dshape <- 1 / shape + temp4 / scale
dl.dscale <- ysim * (1 - shape * temp2 / scale) -
shape * temp4 / scale^2
temp7 <- cbind(dl.dscale, dl.dshape)
run.varcov <- run.varcov +
temp7[, ind1$row.index] *
temp7[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM )
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov),
nrow = n, ncol = ncol(run.varcov), byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) {
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay,
M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}), list( .lscale = lscale,
.escale = escale,
.nsimEIM = nsimEIM ))))
} # gompertz()
dmoe <- function (x, alpha = 1, lambda = 1, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x),
length(alpha),
length(lambda))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(alpha) != LLL) alpha <- rep_len(alpha, LLL)
if (length(lambda) != LLL) lambda <- rep_len(lambda, LLL)
index0 <- (x < 0)
if (log.arg) {
ans <- log(lambda) + (lambda * x) -
2 * log(expm1(lambda * x) + alpha)
ans[index0] <- log(0)
} else {
ans <- lambda * exp(lambda * x) / (expm1(lambda * x) + alpha)^2
ans[index0] <- 0
}
ans[alpha <= 0 | lambda <= 0] <- NaN
ans
}
pmoe <- function (q, alpha = 1, lambda = 1) {
ret <- ifelse(alpha <= 0 | lambda <= 0, NaN,
1 - 1 / (expm1(lambda * q) + alpha))
ret[q < log(2 - alpha) / lambda] <- 0
ret
}
qmoe <- function (p, alpha = 1, lambda = 1) {
ifelse(p < 0 | p > 1 | alpha <= 0 | lambda <= 0, NaN,
log1p(-alpha + 1 / (1 - p)) / lambda)
}
rmoe <- function (n, alpha = 1, lambda = 1) {
qmoe(p = runif(n), alpha = alpha, lambda = lambda)
}
exponential.mo.control <- function(save.weights = TRUE, ...) {
list(save.weights = save.weights)
}
exponential.mo <-
function(lalpha = "loglink", llambda = "loglink",
ealpha = list(), elambda = list(),
ialpha = 1, ilambda = NULL,
imethod = 1,
nsimEIM = 200,
zero = NULL) {
stop("fundamentally unable to estimate the parameters as ",
"the support of the density depends on the parameters")
lalpha <- as.list(substitute(lalpha))
ealpha <- link2list(lalpha)
lalpha <- attr(ealpha, "function.name")
llambda <- as.list(substitute(llambda))
elambda <- link2list(llambda)
llambda <- attr(elambda, "function.name")
lalpha0 <- lalpha
ealpha0 <- ealpha
ialpha0 <- ialpha
if (!is.Numeric(nsimEIM, length.arg = 1,
integer.valued = TRUE))
stop("bad input for argument 'nsimEIM'")
if (nsimEIM <= 50)
warning("argument 'nsimEIM' should be an integer ",
"greater than 50, say")
if (length(ialpha0))
if (!is.Numeric(ialpha0, positive = TRUE))
stop("argument 'ialpha' values must be positive")
if (length(ilambda))
if (!is.Numeric(ilambda, positive = TRUE))
stop("argument 'ilambda' values must be positive")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE, positive = TRUE) ||
imethod > 2)
stop("argument 'imethod' must be 1 or 2")
new("vglmff",
blurb = c("Marshall-Olkin exponential distribution\n\n",
"Links: ",
namesof("alpha", lalpha0, ealpha0 ), ", ",
namesof("lambda", llambda, elambda ), "\n",
"Median: log(3 - alpha) / lambda"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "moe",
nsimEIM = .nsimEIM,
parameters.names = c("alpha", "lambda"),
zero = .zero )
}, list( .zero = zero,
.nsimEIM = nsimEIM ))),
initialize = eval(substitute(expression({
temp5 <-
w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
w <- temp5$w
y <- temp5$y
ncoly <- ncol(y)
M1 <- 2
extra$ncoly <- ncoly
extra$M1 <- M1
M <- M1 * ncoly
mynames1 <- param.names("alpha", ncoly, skip1 = TRUE)
mynames2 <- param.names("lambda", ncoly, skip1 = TRUE)
predictors.names <-
c(namesof(mynames1, .lalpha0 , .ealpha0 , tag = FALSE),
namesof(mynames2, .llambda , .elambda , tag = FALSE))[
interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
matL <- matrix(if (length( .ilambda )) .ilambda else 0,
n, ncoly, byrow = TRUE)
matA <- matrix(if (length( .ialpha0 )) .ialpha0 else 0,
n, ncoly, byrow = TRUE)
for (spp. in 1:ncoly) {
yvec <- y[, spp.]
moexpon.Loglikfun <-
function(lambdaval, y, x, w, extraargs) {
ans <-
sum(c(w) * log(dmoe(x = y, alpha = extraargs$alpha,
lambda = lambdaval)))
ans
}
Alpha.init <- .ialpha0
lambda.grid <- seq(0.1, 10.0, len = 21)
Lambda.init <- grid.search(lambda.grid,
objfun = moexpon.Loglikfun,
y = y, x = x, w = w,
extraargs = list(alpha = Alpha.init))
if (length(mustart)) {
Lambda.init <- Lambda.init / (1 - Phimat.init)
}
if (!length( .ialpha0 ))
matA[, spp.] <- Alpha0.init
if (!length( .ilambda ))
matL[, spp.] <- Lambda.init
} # spp.
etastart <- cbind(theta2eta(matA, .lalpha0, .ealpha0 ),
theta2eta(matL, .llambda, .elambda ))[,
interleave.VGAM(M, M1 = M1)]
mustart <- NULL # Since etastart has been computed.
} # End of !length(etastart)
}), list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda,
.ialpha0 = ialpha0, .ilambda = ilambda,
.imethod = imethod
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
alpha0 <- eta2theta(eta[, c(TRUE, FALSE)], .lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE)], .llambda , .elambda )
log(3 - alpha0) / lambda
}, list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
last = eval(substitute(expression({
M1 <- extra$M1
misc$link <-
c(rep_len( .lalpha0 , ncoly),
rep_len( .llambda , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(mynames1, mynames2)[interleave.VGAM(M,
M1 = M1)]
names(misc$link) <- temp.names
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .ealpha0
misc$earg[[M1*ii ]] <- .elambda
}
misc$M1 <- M1
misc$imethod <- .imethod
misc$expected <- TRUE
misc$multipleResponses <- TRUE
misc$nsimEIM <- .nsimEIM
}), list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda,
.nsimEIM = nsimEIM,
.imethod = imethod ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE, eta,
extra = NULL,
summation = TRUE) {
alpha0 <- eta2theta(eta[, c(TRUE, FALSE)], .lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE)], .llambda , .elambda )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <- c(w) * log(dmoe(x = y, alpha = alpha0,
lambda = lambda))
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
vfamily = c("exponential.mo"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
alpha0 <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.llambda , .elambda )
okay1 <- all(is.finite(alpha0)) && all(0 < alpha0) &&
all(is.finite(lambda)) && all(0 < lambda)
okay1
}, list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
deriv = eval(substitute(expression({
M1 <- 2
alpha0 <- eta2theta(eta[, c(TRUE, FALSE), drop = FALSE],
.lalpha0 , .ealpha0 )
lambda <- eta2theta(eta[, c(FALSE, TRUE), drop = FALSE],
.llambda , .elambda )
temp2 <- (expm1(lambda * y) + alpha0)
dl.dalpha0 <- -2 / temp2
dl.dlambda <- 1 / lambda + y -
2 * y * exp(lambda * y) / temp2
dalpha0.deta <- dtheta.deta(alpha0, .lalpha0 , .ealpha0 )
dlambda.deta <- dtheta.deta(lambda, .llambda , .elambda )
dthetas.detas <- cbind(dalpha0.deta,
dlambda.deta)
myderiv <- c(w) * cbind(dl.dalpha0, dl.dlambda) *
dthetas.detas
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lalpha0 = lalpha0, .llambda = llambda,
.ealpha0 = ealpha0, .elambda = elambda ))),
weight = eval(substitute(expression({
NOS <- M / M1
dThetas.detas <- dthetas.detas[, interleave.VGAM(M, M1 = M1)]
wz <- matrix(0.0, n, M + M - 1) # wz is 'tridiagonal'
ind1 <- iam(NA, NA, M = M1, both = TRUE, diag = TRUE)
for (spp. in 1:NOS) {
run.varcov <- 0
Alph <- alpha0[, spp.]
Lamb <- lambda[, spp.]
for (ii in 1:( .nsimEIM )) {
ysim <- rmoe(n = n, alpha = Alph, lambda = Lamb)
if (ii < 3) {
}
temp2 <- (expm1(lambda * ysim) + alpha0)
dl.dalpha0 <- -2 / temp2
dl.dlambda <- 1 / lambda + ysim -
2 * ysim * exp(lambda * ysim) / temp2
temp3 <- cbind(dl.dalpha0, dl.dlambda)
run.varcov <- run.varcov +
temp3[, ind1$row.index] *
temp3[, ind1$col.index]
}
run.varcov <- cbind(run.varcov / .nsimEIM)
wz1 <- if (intercept.only)
matrix(colMeans(run.varcov),
n, ncol(run.varcov), byrow = TRUE) else
run.varcov
wz1 <- wz1 * dThetas.detas[, M1 * (spp. - 1) + ind1$row] *
dThetas.detas[, M1 * (spp. - 1) + ind1$col]
for (jay in 1:M1)
for (kay in jay:M1) {
cptr <- iam((spp. - 1) * M1 + jay,
(spp. - 1) * M1 + kay,
M = M)
wz[, cptr] <- wz1[, iam(jay, kay, M = M1)]
}
} # End of for (spp.) loop
w.wz.merge(w = w, wz = wz, n = n, M = M, ndepy = M / M1)
}), list( .llambda = llambda,
.elambda = elambda,
.nsimEIM = nsimEIM ))))
} # exponential.mo()
genbetaII.Loglikfun4 <-
function(scaleval, shape1.a, shape2.p, shape3.q,
y, x, w, extraargs) {
sum(c(w) * dgenbetaII(x = y, scale = scaleval,
shape1.a = shape1.a,
shape2.p = shape2.p,
shape3.q = shape3.q,
log = TRUE))
}
genbetaII <-
function(lscale = "loglink",
lshape1.a = "loglink",
lshape2.p = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape1.a = NULL,
ishape2.p = NULL,
ishape3.q = NULL,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = exp(-5:5),
gshape2.p = exp(-5:5),
gshape3.q = exp(-5:5),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Generalized Beta II distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale * gamma(shape2.p + 1/shape1.a) * ",
"gamma(shape3.q - 1/shape1.a) / ",
"(gamma(shape2.p) * gamma(shape3.q))"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 4,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 4,
Q1 = 1,
dpqrfun = "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a", "shape2.p", "shape3.q") else
c("shape1.a", "scale", "shape2.p", "shape3.q"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a ,
lshape2.p = .lshape2.p , lshape3.q = .lshape3.q ,
eshape2.p = .eshape2.p , eshape3.q = .eshape3.q )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ,
.zero = zero ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 4 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <- c(
if ( .lss ) {
c(namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE),
namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE),
namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE))
},
namesof(sha2.names , .lshape2.p , .eshape2.p , tag = FALSE),
namesof(sha3.names , .lshape3.q , .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <-
pp.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
gscale <- .gscale
gshape1.a <- .gshape1.a
gshape2.p <- .gshape2.p
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
if (length( .ishape2.p )) gshape2.p <-
rep_len( .ishape2.p , NOS)
if (length( .ishape3.q )) gshape3.q <-
rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, gshape1.a, gshape2.p, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
pp.init[, spp.] <- try.this["Value3"]
qq.init[, spp.] <- try.this["Value4"]
} # End of for (spp. ...)
finite.mean <- 1 < aa.init * qq.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init * qq.init
}
etastart <-
cbind(if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale )),
theta2eta(pp.init , .lshape2.p , earg = .eshape2.p ),
theta2eta(qq.init , .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.ishape2.p = ishape2.p, .ishape3.q = ishape3.q,
.gshape2.p = gshape2.p, .gshape3.q = gshape3.q,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 4
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- cbind(Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq)))
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 4
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly),
rep_len( .lshape2.p , ncoly),
rep_len( .lshape3.q , ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names, sha2.names, sha3.names)
} else {
c(sha1.names, scaL.names, sha2.names, sha3.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
if ( .lss ) {
misc$earg[[M1*ii-3]] <- .escale
misc$earg[[M1*ii-2]] <- .eshape1.a
} else {
misc$earg[[M1*ii-3]] <- .eshape1.a
misc$earg[[M1*ii-2]] <- .escale
}
misc$earg[[M1*ii-1]] <- .eshape2.p
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 4
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
vfamily = c("genbetaII"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 4
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a*p < 1 < a*q has ",
"been violated; solution may be at the",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 4
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 3, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
dp.deta <- dtheta.deta(parg, .lshape2.p , .eshape2.p )
dq.deta <- dtheta.deta(qq, .lshape3.q , .eshape3.q )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta,
dl.dp * dp.deta,
dl.dq * dq.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta,
dl.dp * dp.deta,
dl.dq * dq.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dq <- temp5b - temp5
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
ned2l.dap <- -(qq * (temp3a -temp3b) -1) / (aa*(parg+qq))
ned2l.daq <- -(parg * (temp3b -temp3a) -1) / (aa*(parg+qq))
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
ned2l.dpq <- -temp5
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dap * da.deta * dp.deta,
c(w) * ned2l.dpq * dp.deta * dq.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.daq * da.deta * dq.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.dpq * dp.deta * dq.deta,
c(w) * ned2l.dap * da.deta * dp.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta,
c(w) * ned2l.daq * da.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.lss = lss ))))
} # genbetaII
dgenbetaII <-
function(x, scale = 1, shape1.a, shape2.p, shape3.q,
log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("Bad input for argument 'log'")
rm(log)
logden <- log(shape1.a) +
(shape1.a * shape2.p - 1) * log(abs(x)) -
shape1.a * shape2.p * log(scale) -
lbeta(shape2.p, shape3.q) -
(shape2.p + shape3.q) * log1p((abs(x)/scale)^shape1.a)
if (any(x <= 0) || any(is.infinite(x))) {
LLL <- max(length(x), length(scale),
length(shape1.a), length(shape2.p), length(shape3.q))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
logden[is.infinite(x)] <- log(0)
logden[x < 0] <- log(0)
x.eq.0 <- !is.na(x) & (x == 0)
if (any(x.eq.0)) {
axp <- shape1.a[x.eq.0] * shape2.p[x.eq.0]
logden[x.eq.0 & axp < 1] <- log(Inf)
ind5 <- x.eq.0 & axp == 1
logden[ind5] <- log(shape1.a[ind5]) -
shape1.a[ind5] * shape2.p[ind5] * log(scale[ind5]) -
lbeta(shape2.p[ind5], shape3.q[ind5]) -
(shape2.p[ind5] + shape3.q[ind5]) *
log1p((0/scale[ind5])^shape1.a[ind5])
logden[x.eq.0 & axp > 1] <- log(0)
}
}
if (log.arg) logden else exp(logden)
} # dgenbetaII
rsinmad <- function(n, scale = 1, shape1.a, shape3.q)
qsinmad(runif(n), shape1.a = shape1.a, scale = scale,
shape3.q = shape3.q)
rlomax <- function(n, scale = 1, shape3.q)
rsinmad(n, scale = scale, shape1.a = 1, shape3.q = shape3.q)
rfisk <- function(n, scale = 1, shape1.a)
rsinmad(n, scale = scale, shape1.a = shape1.a, shape3.q = 1)
rparalogistic <- function(n, scale = 1, shape1.a)
rsinmad(n, scale = scale, shape1.a = shape1.a,
shape3.q = shape1.a)
rdagum <- function(n, scale = 1, shape1.a, shape2.p)
qdagum(runif(n), scale = scale, shape1.a = shape1.a,
shape2.p = shape2.p)
rinv.lomax <- function(n, scale = 1, shape2.p)
rdagum(n, scale = scale, shape1.a = 1, shape2.p = shape2.p)
rinv.paralogistic <- function(n, scale = 1, shape1.a)
rdagum(n, scale = scale, shape1.a = shape1.a,
shape2.p = shape1.a)
qsinmad <- function(p, scale = 1, shape1.a, shape3.q,
lower.tail = TRUE,
log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape1.a), length(scale),
length(shape3.q))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- scale * expm1((-1/shape3.q) *
log(-expm1(ln.p)))^(1/shape1.a)
} else {
ans <- scale * expm1((-1/shape3.q) * log1p(-p))^(1/shape1.a)
ans[p == 0] <- 0
ans[p == 1] <- Inf
}
} else {
if (log.p) {
ln.p <- p
ans <- scale * expm1(-ln.p / shape3.q)^(1/shape1.a)
} else {
ans <- scale * expm1(-log(p) / shape3.q)^(1/shape1.a)
ans[p == 0] <- Inf
ans[p == 1] <- 0
}
}
ans[scale <= 0 | shape1.a <= 0 | shape3.q <= 0] <- NaN
ans
} # qsinmad
qlomax <- function(p, scale = 1, shape3.q,
lower.tail = TRUE, log.p = FALSE)
qsinmad(p, shape1.a = 1, scale = scale, shape3.q = shape3.q,
lower.tail = lower.tail, log.p = log.p)
qfisk <- function(p, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
qsinmad(p, shape1.a = shape1.a, scale = scale, shape3.q = 1,
lower.tail = lower.tail, log.p = log.p)
qparalogistic <- function(p, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
qsinmad(p, shape1.a = shape1.a, scale = scale,
shape3.q = shape1.a,
lower.tail = lower.tail, log.p = log.p)
qdagum <- function(p, scale = 1, shape1.a, shape2.p,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(p), length(shape1.a), length(scale),
length(shape2.p))
if (length(p) != LLL) p <- rep_len(p, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
if (lower.tail) {
if (log.p) {
ln.p <- p
ans <- scale * (expm1(-ln.p/shape2.p))^(-1/shape1.a)
ans[ln.p > 0] <- NaN
} else {
ans <- scale * (expm1(-log(p)/shape2.p))^(-1/shape1.a)
ans[p < 0] <- NaN
ans[p == 0] <- 0
ans[p == 1] <- Inf
ans[p > 1] <- NaN
}
} else {
if (log.p) {
ln.p <- p
ans <- scale *
(expm1(-log(-expm1(ln.p))/shape2.p))^(-1/shape1.a)
ans[ln.p > 0] <- NaN
} else {
ans <- scale * (expm1(-log1p(-p)/shape2.p))^(-1/shape1.a)
ans[p < 0] <- NaN
ans[p == 0] <- Inf
ans[p == 1] <- 0
ans[p > 1] <- NaN
}
}
ans[scale <= 0 | shape1.a <= 0 | shape2.p <= 0] <- NaN
ans
} # qdagum
qinv.lomax <- function(p, scale = 1, shape2.p,
lower.tail = TRUE, log.p = FALSE)
qdagum(p, scale = scale, shape1.a = 1, shape2.p = shape2.p,
lower.tail = lower.tail, log.p = log.p)
qinv.paralogistic <- function(p, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
qdagum(p, scale = scale, shape1.a = shape1.a,
shape2.p = shape1.a, ## 20150121 Kai; add shape2.p=shape1.a
lower.tail = lower.tail, log.p = log.p)
psinmad <- function(q, scale = 1, shape1.a, shape3.q,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape1.a), length(scale),
length(shape3.q))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
# 20150121 KaiH
if (lower.tail) {
if (log.p) {
ans <- log1p(-(1 + (q / scale)^shape1.a)^(-shape3.q))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- exp(log1p(-(1 + (q / scale)^shape1.a)^(-shape3.q)))
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- (-shape3.q) * log1p((q / scale)^shape1.a)
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
ans <- (1 + (q / scale)^shape1.a)^(-shape3.q)
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[scale <= 0 | shape1.a <= 0 | shape3.q <= 0] <- NaN
ans
} # psinmad
plomax <-
function(q, scale = 1, shape3.q, # Change the order
lower.tail = TRUE, log.p = FALSE)
psinmad(q, shape1.a = 1, scale = scale, shape3.q = shape3.q,
lower.tail = lower.tail, log.p = log.p)
pfisk <- function(q, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
psinmad(q, shape1.a = shape1.a, scale = scale, shape3.q = 1,
lower.tail = lower.tail, log.p = log.p)
pparalogistic <-
function(q, scale = 1, shape1.a, # Change the order
lower.tail = TRUE, log.p = FALSE)
psinmad(q, shape1.a = shape1.a, scale = scale,
shape3.q = shape1.a, # Add shape3.q = shape1.a
lower.tail = lower.tail, log.p = log.p)
pdagum <- function(q, scale = 1, shape1.a, shape2.p,
lower.tail = TRUE, log.p = FALSE) {
if (!is.logical(lower.tail) || length(lower.tail ) != 1)
stop("bad input for argument 'lower.tail'")
if (!is.logical(log.p) || length(log.p) != 1)
stop("bad input for argument 'log.p'")
LLL <- max(length(q), length(shape1.a), length(scale),
length(shape2.p))
if (length(q) != LLL) q <- rep_len(q, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
if (lower.tail) {
if (log.p) {
ans <- (-shape2.p) * log1p((q/scale)^(-shape1.a))
ans[q <= 0 ] <- -Inf
ans[q == Inf] <- 0
} else {
ans <- exp( (-shape2.p) * log1p((q/scale)^(-shape1.a)) )
ans[q <= 0] <- 0
ans[q == Inf] <- 1
}
} else {
if (log.p) {
ans <- log1p(-(1 + (q/scale)^(-shape1.a))^(-shape2.p))
ans[q <= 0] <- 0
ans[q == Inf] <- -Inf
} else {
stop("unfinished")
ans[q <= 0] <- 1
ans[q == Inf] <- 0
}
}
ans[shape1.a <= 0 | scale <= 0 | shape2.p <= 0] <- NaN
ans
} # pdagum
pinv.lomax <- function(q, scale = 1, shape2.p,
lower.tail = TRUE, log.p = FALSE)
pdagum(q, scale = scale, shape1.a = 1, shape2.p = shape2.p,
lower.tail = lower.tail, log.p = log.p)
pinv.paralogistic <-
function(q, scale = 1, shape1.a,
lower.tail = TRUE, log.p = FALSE)
pdagum(q, scale = scale, shape1.a = shape1.a,
shape2.p = shape1.a,
lower.tail = lower.tail, log.p = log.p)
dbetaII <-
function(x, scale = 1, shape2.p, shape3.q, log = FALSE)
dgenbetaII(x = x, scale = scale, shape1.a = 1,
shape2.p = shape2.p, shape3.q = shape3.q, log = log)
dsinmad <-
function(x, scale = 1, shape1.a, shape3.q, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x), length(shape1.a),
length(scale), length(shape3.q))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape3.q) != LLL) shape3.q <- rep_len(shape3.q, LLL)
Loglik <- rep_len(log(0), LLL)
xok <- (x > 0) & !is.na(x) # Avoids log(x) if x<0, and handles NAs
Loglik[xok] <- log(shape1.a[xok]) + log(shape3.q[xok]) +
(shape1.a[xok]-1) * log(x[xok]) -
shape1.a[xok] * log(scale[xok]) -
(1 + shape3.q[xok]) *
log1p((x[xok]/scale[xok])^shape1.a[xok])
x.eq.0 <- (x == 0) & !is.na(x)
Loglik[x.eq.0] <- log(shape1.a[x.eq.0]) + log(shape3.q[x.eq.0]) -
shape1.a[x.eq.0] * log(scale[x.eq.0])
Loglik[is.na(x)] <- NA
Loglik[is.nan(x)] <- NaN
Loglik[x == Inf] <- log(0)
if (log.arg) Loglik else exp(Loglik)
}
dlomax <- function(x, scale = 1, shape3.q, log = FALSE)
dsinmad(x, scale = scale, shape1.a = 1, shape3.q = shape3.q,
log = log)
dfisk <- function(x, scale = 1, shape1.a, log = FALSE)
dsinmad(x, scale = scale, shape1.a = shape1.a, shape3.q = 1,
log = log)
dparalogistic <- function(x, scale = 1, shape1.a, log = FALSE)
dsinmad(x, scale = scale, shape1.a = shape1.a, shape3.q = shape1.a,
log = log)
ddagum <-
function(x, scale = 1, shape1.a, shape2.p, log = FALSE) {
if (!is.logical(log.arg <- log) || length(log) != 1)
stop("bad input for argument 'log'")
rm(log)
LLL <- max(length(x),
length(shape1.a),
length(scale),
length(shape2.p))
if (length(x) != LLL) x <- rep_len(x, LLL)
if (length(shape1.a) != LLL) shape1.a <- rep_len(shape1.a, LLL)
if (length(scale) != LLL) scale <- rep_len(scale, LLL)
if (length(shape2.p) != LLL) shape2.p <- rep_len(shape2.p, LLL)
Loglik <- rep_len(log(0), LLL)
xok <- (x > 0) & !is.na(x) # Avoids log(x) if x<0, and handles NAs
Loglik[xok] <- log(shape1.a[xok]) +
log(shape2.p[xok]) +
(shape1.a[xok] * shape2.p[xok]-1) * log( x[xok]) -
shape1.a[xok] * shape2.p[xok] * log(scale[xok]) -
(1 + shape2.p[xok]) *
log1p((x[xok]/scale[xok])^shape1.a[xok])
Loglik[shape2.p <= 0] <- NaN
x.eq.0 <- (x == 0) & !is.na(x)
Loglik[x.eq.0] <- log(shape1.a[x.eq.0]) +
log(shape2.p[x.eq.0]) -
shape1.a[x.eq.0] * shape2.p[x.eq.0] *
log(scale[x.eq.0])
Loglik[is.na(x)] <- NA
Loglik[is.nan(x)] <- NaN
Loglik[x == Inf] <- log(0)
if (log.arg) Loglik else exp(Loglik)
}
dinv.lomax <- function(x, scale = 1, shape2.p, log = FALSE)
ddagum(x, scale = scale, shape1.a = 1, shape2.p = shape2.p,
log = log)
dinv.paralogistic <- function(x, scale = 1, shape1.a, log = FALSE)
ddagum(x, scale = scale, shape1.a = shape1.a, shape2.p = shape1.a,
log = log)
sinmad <- function(lscale = "loglink",
lshape1.a = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape1.a = NULL,
ishape3.q = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = exp(-5:5),
gshape3.q = exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Singh-Maddala distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale * gamma(shape2.p + 1/shape1.a) * ",
"gamma(shape3.q - 1/shape1.a) / ",
"gamma(shape3.q)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "sinmad", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a", "shape3.q") else
c("shape1.a", "scale", "shape3.q"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a ,
lshape3.q = .lshape3.q ,
eshape3.q = .eshape3.q )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
scrambleseed <- runif(1) # To scramble the seed
qnorm(psinmad(y, scale = Scale, shape1.a = aa,
shape3.q = qq))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 3 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <- c(
if ( .lss ) {
c(namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE),
namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE),
namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE))
},
namesof(sha3.names , .lshape3.q , .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
if (length( .ishape3.q )) gshape3.q <-
rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, gshape1.a, vov3 = 1, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
qq.init[, spp.] <- try.this["Value4"]
} else { # .imethod == 2
qvec <- .probs.y
ishape3.q <- if (length( .ishape3.q )) .ishape3.q else 1
xvec <- log( (1-qvec)^(-1/ ishape3.q ) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else 1/fit0$coef[2]
qq.init[, spp.] <- ishape3.q
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init * qq.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init * qq.init
}
etastart <-
cbind(if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale )),
theta2eta(qq.init , .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.ishape3.q = ishape3.q,
.gshape3.q = gshape3.q,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) - lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[qq <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 3
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly),
rep_len( .lshape3.q , ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names, sha3.names)
} else {
c(sha1.names, scaL.names, sha3.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
if ( .lss ) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape1.a
} else {
misc$earg[[M1*ii-2]] <- .eshape1.a
misc$earg[[M1*ii-1]] <- .escale
}
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
vfamily = c("sinmad"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 3
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
rsinmad(nsim * length(qq), shape1.a = aa, scale = Scale,
shape3.q = qq)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a < 1 < a*q has ",
"been violated; solution may be at the",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
dq.deta <- dtheta.deta(qq, .lshape3.q , .eshape3.q )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta,
dl.dq * dq.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta,
dl.dq * dq.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dq <- temp5b - temp5
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
ned2l.daq <- -(parg * (temp3b -temp3a) -1) / (aa*(parg+qq))
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.daq * da.deta * dq.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta,
c(w) * ned2l.daq * da.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.lss = lss ))))
} # sinmad
dagum <-
function(lscale = "loglink",
lshape1.a = "loglink",
lshape2.p = "loglink",
iscale = NULL,
ishape1.a = NULL,
ishape2.p = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
gshape2.p = exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
new("vglmff",
blurb =
c("Dagum distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), "\n",
"Mean: scale * gamma(shape2.p + 1/shape1.a) * ",
"gamma(1 - 1/shape1.a) / ",
"gamma(shape2.p)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "dagum", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names =
if ( .lss ) c("scale", "shape1.a", "shape2.p") else
c("shape1.a", "scale", "shape2.p"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a ,
lshape2.p = .lshape2.p ,
eshape2.p = .eshape2.p )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss , .zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
scrambleseed <- runif(1) # To scramble the seed
qnorm(pdagum(y, scale = Scale, shape1.a = aa,
shape2.p = parg)) # shape3.q = 1
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 3 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
predictors.names <- c(
if ( .lss ) {
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE),
namesof(scaL.names , .lscale , .escale , tag = FALSE))
},
namesof(sha2.names , .lshape2.p , .eshape2.p , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <-
pp.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
gshape2.p <- .gshape2.p
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
if (length( .ishape2.p )) gshape2.p <-
rep_len( .ishape2.p , NOS)
try.this <-
grid.search4(gscale, gshape1.a, gshape2.p, vov4 = 1,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
pp.init[, spp.] <- try.this["Value3"]
} else { # .imethod == 2
qvec <- .probs.y
ishape2.p <- if (length( .ishape2.p )) .ishape2.p else 1
xvec <- log( qvec^(-1/ ishape2.p) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a )) .ishape1.a else
-1/fit0$coef[2]
pp.init[, spp.] <- ishape2.p
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init
}
etastart <-
cbind(if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale )),
theta2eta(pp.init , .lshape2.p , earg = .eshape2.p ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.ishape2.p = ishape2.p,
.gshape2.p = gshape2.p,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- 1
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[parg <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 3
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly),
rep_len( .lshape2.p , ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names, sha2.names)
} else {
c(sha1.names, scaL.names, sha2.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
if ( .lss ) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape1.a
} else {
misc$earg[[M1*ii-2]] <- .eshape1.a
misc$earg[[M1*ii-1]] <- .escale
}
misc$earg[[M1*ii ]] <- .eshape2.p
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 3
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = 1,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
vfamily = c("dagum"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- 1
rdagum(nsim * length(parg), shape1.a = aa, scale = Scale,
shape2.p = parg)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a*p < 1 < a has ",
"been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
}
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p)
qq <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
dp.deta <- dtheta.deta(parg, .lshape2.p , .eshape2.p )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta,
dl.dp * dp.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta,
dl.dp * dp.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
ned2l.dap <- -(qq * (temp3a -temp3b) -1) / (aa*(parg+qq))
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dap * da.deta * dp.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.dap * da.deta * dp.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.lss = lss ))))
} # dagum
betaII <-
function(lscale = "loglink",
lshape2.p = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape2.p = NULL,
ishape3.q = NULL,
imethod = 1,
gscale = exp(-5:5),
gshape2.p = exp(-5:5),
gshape3.q = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale ) && !is.Numeric(iscale , positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Beta II distribution \n\n",
"Links: ",
namesof("scale" , lscale , earg = escale ), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale * gamma(shape2.p + 1) * ",
"gamma(shape3.q - 1) / ",
"(gamma(shape2.p) * gamma(shape3.q))"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 3,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 3,
Q1 = 1,
dpqrfun = "betaII", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = c("scale", "shape2.p", "shape3.q"),
lscale = .lscale ,
escale = .escale ,
lshape2.p = .lshape2.p , lshape3.q = .lshape3.q ,
eshape2.p = .eshape2.p , eshape3.q = .eshape3.q )
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.zero = zero ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 3 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <-
c(namesof(scaL.names , .lscale , earg = .escale , tag = FALSE),
namesof(sha2.names , .lshape2.p , earg = .eshape2.p , tag = FALSE),
namesof(sha3.names , .lshape3.q , earg = .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
sc.init <-
pp.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape2.p <- .gshape2.p
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <- rep_len( .iscale , NOS)
if (length( .ishape2.p )) gshape2.p <- rep_len( .ishape2.p , NOS)
if (length( .ishape3.q )) gshape3.q <- rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, vov2 = 1, gshape2.p, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
pp.init[, spp.] <- try.this["Value3"]
qq.init[, spp.] <- try.this["Value4"]
} else { # .imethod == 2
sc.init[, spp.] <- if (length( .iscale )) .iscale else {
qvec <- .probs.y
ishape3.q <- if (length( .ishape3.q )) .ishape3.q else 1
xvec <- log( (1-qvec)^(-1/ ishape3.q ) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec )))
exp(fit0$coef[1])
}
pp.init[, spp.] <- if (length( .ishape2.p )) .ishape2.p else 1.0
qq.init[, spp.] <- if (length( .ishape3.q )) .ishape3.q else 1.0
}
} # End of for (spp. ...)
finite.mean <- 1 < qq.init
COP.use <- 1.15
while (any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
finite.mean <- 1 < qq.init
}
etastart <-
cbind(theta2eta(sc.init , .lscale , earg = .escale ),
theta2eta(pp.init , .lshape2.p , earg = .eshape2.p ),
theta2eta(qq.init , .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale ,
.escale = escale ,
.iscale = iscale ,
.gscale = gscale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q,
.ishape2.p = ishape2.p, .ishape3.q = ishape3.q,
.gshape2.p = gshape2.p, .gshape3.q = gshape3.q,
.imethod = imethod , .probs.y = probs.y
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) - lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[parg <= 0] <- NA
ans[qq <= 0] <- NA
ans
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
last = eval(substitute(expression({
M1 <- 3
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape2.p , ncoly),
rep_len( .lshape3.q , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(scaL.names, sha2.names, sha3.names)
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
misc$earg[[M1*ii-2]] <- .escale
misc$earg[[M1*ii-1]] <- .eshape2.p
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 3
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq, log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
vfamily = c("betaII"),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 3
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -p < 1 < q has",
" been violated; solution may be at the ",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q ))),
deriv = eval(substitute(expression({
M1 <- 3
NOS <- ncol(eta)/M1 # Needed for summary()
Scale <- eta2theta(eta[, M1*(1:NOS) - 2, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
dp.deta <- dtheta.deta(parg, .lshape2.p , .eshape2.p )
dq.deta <- dtheta.deta(qq, .lshape3.q , .eshape3.q )
myderiv <-
c(w) * cbind(dl.dscale * dscale.deta,
dl.dp * dp.deta,
dl.dq * dq.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dq <- temp5b - temp5
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
ned2l.dpq <- -temp5
wz <-
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dscalep * dscale.deta * dp.deta,
c(w) * ned2l.dpq * dp.deta * dq.deta, # Switched!!
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p, .lshape3.q = lshape3.q,
.eshape2.p = eshape2.p, .eshape3.q = eshape3.q
))))
} # betaII
lomax <-
function(lscale = "loglink",
lshape3.q = "loglink",
iscale = NULL,
ishape3.q = NULL,
imethod = 1,
gscale = exp(-5:5),
gshape3.q = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape3.q) && !is.Numeric(ishape3.q, positive = TRUE))
stop("Bad input for argument 'ishape3.q'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape3.q <- as.list(substitute(lshape3.q))
eshape3.q <- link2list(lshape3.q)
lshape3.q <- attr(eshape3.q, "function.name")
new("vglmff",
blurb =
c("Lomax distribution \n\n",
"Links: ",
namesof("scale" , lscale , earg = escale ), ", ",
namesof("shape3.q" , lshape3.q, earg = eshape3.q), "\n",
"Mean: scale / (shape3.q - 1)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "lomax", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = c("scale", "shape3.q"),
lscale = .lscale ,
escale = .escale ,
lshape3.q = .lshape3.q ,
eshape3.q = .eshape3.q )
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
scrambleseed <- runif(1) # To scramble the seed
qnorm(plomax(y, scale = Scale, shape3.q = qq))
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha3.names <- param.names("shape3.q", NOS, skip1 = TRUE)
predictors.names <-
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha3.names , .lshape3.q , .eshape3.q , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
qq.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1) {
gscale <- .gscale
gshape3.q <- .gshape3.q
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape3.q )) gshape3.q <-
rep_len( .ishape3.q , NOS)
try.this <-
grid.search4(gscale, vov2 = 1, vov3 = 1, gshape3.q,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
qq.init[, spp.] <- try.this["Value4"]
} else { # .imethod == 2
qvec <- .probs.y
iscale <- if (length( .iscale )) .iscale else 1
xvec <- log1p( quantile(yvec / iscale, probs = qvec) )
fit0 <- lsfit(xvec, y = -log1p(-qvec), intercept = FALSE)
sc.init[, spp.] <- iscale
qq.init[, spp.] <- if (length( .ishape3.q ))
.ishape3.q else
fit0$coef
}
} # End of for (spp. ...)
finite.mean <- 1 < qq.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
qq.init[!finite.mean] <- 0.1 + qq.init[!finite.mean] * COP.use
finite.mean <- 1 < qq.init
}
etastart <-
cbind(theta2eta(sc.init, .lscale , earg = .escale ),
theta2eta(qq.init, .lshape3.q , earg = .eshape3.q ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale ,
.escale = escale ,
.iscale = iscale ,
.gscale = gscale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q,
.ishape3.q = ishape3.q,
.gshape3.q = gshape3.q,
.imethod = imethod , .probs.y = probs.y
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[qq <= 0] <- NA
ans
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
last = eval(substitute(expression({
M1 <- 2
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape3.q , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <-
c(scaL.names, sha3.names)
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape3.q
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = qq,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
vfamily = c("lomax"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 2
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q )
rlomax(nsim * length(qq), scale = Scale, shape3.q = qq)
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint 1 < q has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- 1
parg <- 1
qq <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape3.q , earg = .eshape3.q)
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.dq <- temp3 - temp3b - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
dq.deta <- dtheta.deta(qq, .lshape3.q , earg = .eshape3.q )
myderiv <-
c(w) * cbind(dl.dscale * dscale.deta,
dl.dq * dq.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dq <- temp5b - temp5
ned2l.dscaleq <- -aa * parg / (Scale*(parg+qq))
wz <-
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dq * dq.deta^2,
c(w) * ned2l.dscaleq * dscale.deta * dq.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale ,
.escale = escale ,
.lshape3.q = lshape3.q,
.eshape3.q = eshape3.q
))))
} # lomax
fisk <-
function(lscale = "loglink",
lshape1.a = "loglink",
iscale = NULL,
ishape1.a = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
new("vglmff",
blurb =
c("Fisk distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), "\n",
"Mean: scale * gamma(1 + 1/shape1.a) * ",
"gamma(1 - 1/shape1.a)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "fisk", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a") else
c("shape1.a", "scale"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
scrambleseed <- runif(1) # To scramble the seed
qnorm(pfisk(y, scale = Scale, shape1.a = aa))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
predictors.names <- if ( .lss ) {
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE),
namesof(scaL.names , .lscale , .escale , tag = FALSE))
}
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
try.this <-
grid.search4(gscale, gshape1.a, vov3 = 1, vov4 = 1,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
} else { # .imethod == 2
qvec <- .probs.y
iscale <- if (length( .iscale )) .iscale else 1
xvec <- log( quantile(yvec / iscale, probs = qvec) )
fit0 <- lsfit(x = xvec, y = logitlink(qvec),
intercept = FALSE)
sc.init[, spp.] <- iscale
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else
fit0$coef
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 +
aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init
}
etastart <-
if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans[aa <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 2
misc$link <- c(rep_len( if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len( if ( .lss ) .lshape1.a else .lscale ,
ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names)
} else {
c(sha1.names, scaL.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly)
if ( .lss ) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape1.a
} else {
misc$earg[[M1*ii-1]] <- .eshape1.a
misc$earg[[M1*ii ]] <- .escale
}
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dfisk(x = y, scale = Scale, shape1.a = aa,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
vfamily = c("fisk"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
rfisk(nsim * length(aa), shape1.a = aa, scale = Scale)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a < 1 < a has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , earg = .eshape1.a )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
weight = eval(substitute(expression({
ned2l.da <- (1 + (-6 + pi^2) / 9) / aa^2
ned2l.dscale <- ((aa / Scale)^2) / 3
wz <- matrix(0, n, M) # Diagonal
if ( .lss ) {
wz[, c(TRUE, FALSE)] <- ned2l.dscale * dscale.deta^2
wz[, c(FALSE, TRUE)] <- ned2l.da * da.deta^2
} else {
wz[, c(TRUE, FALSE)] <- ned2l.da * da.deta^2
wz[, c(FALSE, TRUE)] <- ned2l.dscale * dscale.deta^2
}
c(w) * wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))))
} # fisk
inv.lomax <- function(lscale = "loglink",
lshape2.p = "loglink",
iscale = NULL,
ishape2.p = NULL,
imethod = 1,
gscale = exp(-5:5),
gshape2.p = exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape2.p") {
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape2.p) && !is.Numeric(ishape2.p, positive = TRUE))
stop("Bad input for argument 'ishape2.p'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape2.p <- as.list(substitute(lshape2.p))
eshape2.p <- link2list(lshape2.p)
lshape2.p <- attr(eshape2.p, "function.name")
new("vglmff",
blurb =
c("Inverse Lomax distribution \n\n",
"Links: ",
namesof("scale" , lscale , earg = escale), ", ",
namesof("shape2.p" , lshape2.p, earg = eshape2.p), "\n",
"Mean: does not exist"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "inv.lomax", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = c("scale", "shape2.p"),
lscale = .lscale ,
escale = .escale ,
lshape2.p = .lshape2.p ,
eshape2.p = .eshape2.p )
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
scrambleseed <- runif(1) # To scramble the seed
qnorm(pinv.lomax(y, scale = Scale, shape2.p = parg))
}, list( .lscale = lscale , .lshape2.p = lshape2.p,
.escale = escale , .eshape2.p = eshape2.p ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha2.names <- param.names("shape2.p", NOS, skip1 = TRUE)
predictors.names <-
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha2.names , .lshape2.p , .eshape2.p , tag = FALSE))
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
pp.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape2.p <- .gshape2.p
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape2.p )) gshape2.p <-
rep_len( .ishape2.p , NOS)
try.this <-
grid.search4(gscale, vov2 = 1, gshape2.p, vov4 = 1,
objfun = genbetaII.Loglikfun4, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
pp.init[, spp.] <- try.this["Value3"]
} else { # .imethod == 2
qvec <- .probs.y
ishape2.p <- if (length( .ishape2.p )) .ishape2.p else 1
xvec <- log( qvec^(-1/ ishape2.p) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
pp.init[, spp.] <- ishape2.p
}
} # End of for (spp. ...)
etastart <-
cbind(theta2eta(sc.init, .lscale , earg = .escale ),
theta2eta(pp.init, .lshape2.p , earg = .eshape2.p ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale ,
.escale = escale ,
.iscale = iscale ,
.gscale = gscale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p,
.ishape2.p = ishape2.p,
.gshape2.p = gshape2.p,
.imethod = imethod , .probs.y = probs.y
))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qinv.lomax(p = 0.5, scale = Scale, shape2.p = parg)
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
last = eval(substitute(expression({
M1 <- 2
misc$link <-
c(rep_len( .lscale , ncoly),
rep_len( .lshape2.p , ncoly))[interleave.VGAM(M, M1 = M1)]
temp.names <- c(scaL.names, sha2.names)
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape2.p
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = 1,
shape2.p = parg, shape3.q = 1,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
vfamily = c("inv.lomax"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
aa <- 1
qq <- 1
rinv.lomax(nsim * length(Scale), scale = Scale, shape2.p = parg)
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- 1
aa <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 ) else TRUE
if (!okay.support)
warning("parameter constraint -a*p < 1 has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
parg <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape2.p , earg = .eshape2.p )
qq <- 1
aa <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.dp <- aa * temp1 + temp3 - temp3a - temp4
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
dp.deta <- dtheta.deta(parg, .lshape2.p , earg = .eshape2.p )
myderiv <-
c(w) * cbind(dl.dscale * dscale.deta,
dl.dp * dp.deta)
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dp <- temp5a - temp5
ned2l.dscalep <- aa * qq / (Scale*(parg+qq))
wz <-
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dp * dp.deta^2,
c(w) * ned2l.dscalep * dscale.deta * dp.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale ,
.escale = escale ,
.lshape2.p = lshape2.p,
.eshape2.p = eshape2.p
))))
} # inv.lomax
paralogistic <-
function(lscale = "loglink",
lshape1.a = "loglink",
iscale = NULL,
ishape1.a = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
new("vglmff",
blurb =
c("Paralogistic distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), "\n",
"Mean: scale * gamma(1 + 1/shape1.a) * ",
"gamma(shape1.a - 1/shape1.a) / ",
"gamma(shape1.a)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "paralogistic", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a") else
c("shape1.a", "scale"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
scrambleseed <- runif(1) # To scramble the seed
qnorm(pparalogistic(y, scale = Scale, shape1.a = aa))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
predictors.names <-
if ( .lss ) {
c(namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE),
namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , earg = .eshape1.a ,
tag = FALSE),
namesof(scaL.names , .lscale , earg = .escale ,
tag = FALSE))
}
predictors.names <- predictors.names[interleave.VGAM(M,
M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
paralogistic.Loglikfun2 <-
function(scaleval, shape1.a,
y, x, w, extraargs) {
sum(c(w) * dgenbetaII(x = y, scale = scaleval,
shape1.a = shape1.a,
shape2.p = 1,
shape3.q = shape1.a, log = TRUE))
}
try.this <-
grid.search2(gscale, gshape1.a, # vov3 = 1, vov4 = gshape1.a,
objfun = paralogistic.Loglikfun2, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
} else { # .imethod == 2
qvec <- .probs.y
ishape3.q <- if (length( .ishape1.a )) .ishape1.a else 1
xvec <- log( (1-qvec)^(-1/ ishape3.q) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else
1/fit0$coef[2]
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init * aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init * aa.init
}
etastart <- if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 2
misc$link <- c(rep_len(if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len(if ( .lss ) .lshape1.a else .lscale ,
ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names)
} else {
c(sha1.names, scaL.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly)
if ( .lss ) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape1.a
} else {
misc$earg[[M1*ii-1]] <- .eshape1.a
misc$earg[[M1*ii ]] <- .escale
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = parg, shape3.q = aa,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
vfamily = c("paralogistic"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
rparalogistic(nsim * length(Scale), shape1.a = aa, scale = Scale)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a < 1 < a*a has",
" been violated; solution may be at",
" the boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- 1
qq <- aa
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dscale.deta <- dtheta.deta(Scale, .lscale , .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , .eshape1.a )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss))))
} # paralogistic
inv.paralogistic <-
function(lscale = "loglink",
lshape1.a = "loglink",
iscale = NULL,
ishape1.a = NULL,
imethod = 1,
lss = TRUE,
gscale = exp(-5:5),
gshape1.a = seq(0.75, 4, by = 0.25), # exp(-5:5),
probs.y = c(0.25, 0.50, 0.75),
zero = "shape") {
if (length(lss) != 1 && !is.logical(lss))
stop("Argument 'lss' not specified correctly")
if (!is.Numeric(imethod, length.arg = 1,
integer.valued = TRUE,
positive = TRUE) || imethod > 2)
stop("Bad input for argument 'imethod'")
if (length(iscale) && !is.Numeric(iscale, positive = TRUE))
stop("Bad input for argument 'iscale'")
if (length(ishape1.a) && !is.Numeric(ishape1.a, positive = TRUE))
stop("Bad input for argument 'ishape1.a'")
if (length(probs.y) < 2 || max(probs.y) > 1 ||
!is.Numeric(probs.y, positive = TRUE))
stop("Bad input for argument 'probs.y'")
lscale <- as.list(substitute(lscale))
escale <- link2list(lscale)
lscale <- attr(escale, "function.name")
lshape1.a <- as.list(substitute(lshape1.a))
eshape1.a <- link2list(lshape1.a)
lshape1.a <- attr(eshape1.a, "function.name")
new("vglmff",
blurb =
c("Inverse paralogistic distribution \n\n",
"Links: ",
ifelse (lss,
namesof("scale" , lscale , escale),
namesof("shape1.a", lshape1.a, eshape1.a)), ", ",
ifelse (lss,
namesof("shape1.a", lshape1.a, eshape1.a),
namesof("scale" , lscale , escale)), "\n",
"Mean: scale * gamma(shape1.a + 1/shape1.a) * ",
"gamma(1 - 1/shape1.a) / ",
"gamma(shape1.a)"),
constraints = eval(substitute(expression({
constraints <- cm.zero.VGAM(constraints, x = x, .zero ,
M = M, M1 = 2,
predictors.names = predictors.names)
}), list( .zero = zero ))),
infos = eval(substitute(function(...) {
list(M1 = 2,
Q1 = 1,
dpqrfun = "inv.paralogistic", # "genbetaII",
expected = TRUE,
zero = .zero ,
multipleResponses = TRUE,
parameters.names = if ( .lss )
c("scale", "shape1.a") else
c("shape1.a", "scale"),
lscale = .lscale , lshape1.a = .lshape1.a ,
escale = .escale , eshape1.a = .eshape1.a )
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ,
.zero = zero ))),
rqresslot = eval(substitute(
function(mu, y, w, eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
scrambleseed <- runif(1) # To scramble the seed
qnorm(pinv.paralogistic(y, scale = Scale, shape1.a = aa))
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
initialize = eval(substitute(expression({
temp5 <- w.y.check(w = w, y = y,
Is.positive.y = TRUE,
ncol.w.max = Inf,
ncol.y.max = Inf,
out.wy = TRUE,
colsyperw = 1,
maximize = TRUE)
y <- temp5$y
w <- temp5$w
M1 <- 2 # Number of parameters for one response
NOS <- ncoly <- ncol(y)
M <- M1*ncol(y)
scaL.names <- param.names("scale", NOS, skip1 = TRUE)
sha1.names <- param.names("shape1.a", NOS, skip1 = TRUE)
predictors.names <- if ( .lss ) {
c(namesof(scaL.names , .lscale , .escale , tag = FALSE),
namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE))
} else {
c(namesof(sha1.names , .lshape1.a , .eshape1.a , tag = FALSE),
namesof(scaL.names , .lscale , .escale , tag = FALSE))
}
predictors.names <- predictors.names[interleave.VGAM(M, M1 = M1)]
if (!length(etastart)) {
sc.init <-
aa.init <- matrix(NA_real_, n, NOS)
for (spp. in 1:NOS) { # For each response 'y_spp.'... do:
yvec <- y[, spp.]
wvec <- w[, spp.]
if ( .imethod == 1 ) {
gscale <- .gscale
gshape1.a <- .gshape1.a
if (length( .iscale )) gscale <-
rep_len( .iscale , NOS)
if (length( .ishape1.a )) gshape1.a <-
rep_len( .ishape1.a , NOS)
inv.paralogistic.Loglikfun2 <-
function(scaleval, shape1.a,
y, x, w, extraargs) {
sum(c(w) * dgenbetaII(x = y, scale = scaleval,
shape1.a = shape1.a,
shape2.p = shape1.a,
shape3.q = 1, log = TRUE))
}
try.this <-
grid.search2(gscale, gshape1.a, # vov3 = 1, vov4 = gshape1.a,
objfun = inv.paralogistic.Loglikfun2, # x = x,
y = yvec, w = wvec, # extraargs = NULL,
ret.objfun = TRUE) # Last value is \ell
sc.init[, spp.] <- try.this["Value1"]
aa.init[, spp.] <- try.this["Value2"]
} else { # .imethod == 2
qvec <- .probs.y
ishape2.p <- if (length( .ishape1.a )) .ishape1.a else 1
xvec <- log( qvec^(-1/ ishape2.p) - 1 )
fit0 <- lsfit(x = xvec, y = log(quantile(yvec, qvec)))
sc.init[, spp.] <- if (length( .iscale )) .iscale else
exp(fit0$coef[1])
aa.init[, spp.] <- if (length( .ishape1.a ))
.ishape1.a else
-1/fit0$coef[2]
}
} # End of for (spp. ...)
finite.mean <- 1 < aa.init
COP.use <- 1.15
while (FALSE && any(!finite.mean)) {
aa.init[!finite.mean] <- 0.1 + aa.init[!finite.mean] * COP.use
finite.mean <- 1 < aa.init
}
etastart <- if ( .lss )
cbind(theta2eta(sc.init, .lscale , .escale ),
theta2eta(aa.init, .lshape1.a , .eshape1.a )) else
cbind(theta2eta(aa.init, .lshape1.a , .eshape1.a ),
theta2eta(sc.init, .lscale , .escale ))
etastart <- etastart[, interleave.VGAM(M, M1 = M1)]
} # End of etastart.
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.iscale = iscale , .ishape1.a = ishape1.a,
.gscale = gscale , .gshape1.a = gshape1.a,
.imethod = imethod , .probs.y = probs.y,
.lss = lss ))),
linkinv = eval(substitute(function(eta, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
ans <- Scale * exp(lgamma(parg + 1/aa) +
lgamma(qq - 1/aa) -
lgamma(parg) - lgamma(qq))
ans[parg + 1/aa <= 0] <- NA
ans[qq - 1/aa <= 0] <- NA
ans[aa <= 0] <- NA
ans[Scale <= 0] <- NA
ans
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
last = eval(substitute(expression({
M1 <- 2
misc$link <- c(rep_len(if ( .lss ) .lscale else .lshape1.a ,
ncoly),
rep_len(if ( .lss ) .lshape1.a else .lscale ,
ncoly))[
interleave.VGAM(M, M1 = M1)]
temp.names <- if ( .lss ) {
c(scaL.names, sha1.names)
} else {
c(sha1.names, scaL.names)
}
names(misc$link) <- temp.names[interleave.VGAM(M, M1 = M1)]
misc$earg <- vector("list", M)
names(misc$earg) <- temp.names[interleave.VGAM(M, M1 = M1)]
for (ii in 1:ncoly)
if ( .lss ) {
misc$earg[[M1*ii-1]] <- .escale
misc$earg[[M1*ii ]] <- .eshape1.a
} else {
misc$earg[[M1*ii-1]] <- .eshape1.a
misc$earg[[M1*ii ]] <- .escale
}
misc$expected <- TRUE
misc$multipleResponses <- TRUE
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
loglikelihood = eval(substitute(
function(mu, y, w, residuals = FALSE,
eta, extra = NULL, summation = TRUE) {
M1 <- 2
NOS <- ncol(eta)/M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a )
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a )
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
if (residuals) {
stop("loglikelihood residuals not implemented yet")
} else {
ll.elts <-
c(w) * dgenbetaII(x = y, scale = Scale, shape1.a = aa,
shape2.p = aa, shape3.q = 1,
log = TRUE)
if (summation) {
sum(ll.elts)
} else {
ll.elts
}
}
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
vfamily = c("inv.paralogistic"),
simslot = eval(substitute(
function(object, nsim) {
pwts <- if (length(pwts <- object@prior.weights) > 0)
pwts else weights(object, type = "prior")
if (any(pwts != 1))
warning("ignoring prior weights")
eta <- predict(object)
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
rinv.paralogistic(nsim * length(Scale), shape1.a = aa, scale = Scale)
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
validparams = eval(substitute(function(eta, y, extra = NULL) {
M1 <- 2
NOS <- ncol(eta) / M1
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
okay1 <- all(is.finite(Scale)) && all(Scale > 0) &&
all(is.finite(aa )) && all(aa > 0) &&
all(is.finite(parg )) && all(parg > 0) &&
all(is.finite(qq )) && all(qq > 0)
okay.support <- if (okay1)
all(-aa * parg < 1 & 1 < aa * qq) else TRUE
if (!okay.support)
warning("parameter constraint -a*a < 1 < a has",
" been violated; solution may be at the ",
" boundary of the parameter space.")
okay1 && okay.support
}, list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
deriv = eval(substitute(expression({
M1 <- 2
NOS <- ncol(eta)/M1 # Needed for summary()
if ( .lss ) {
Scale <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lscale , earg = .escale )
aa <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lshape1.a , earg = .eshape1.a)
} else {
aa <- eta2theta(eta[, M1*(1:NOS) - 1, drop = FALSE],
.lshape1.a , earg = .eshape1.a)
Scale <- eta2theta(eta[, M1*(1:NOS) , drop = FALSE],
.lscale , earg = .escale )
}
parg <- aa
qq <- 1
temp1 <- log(y/Scale)
temp2 <- (y/Scale)^aa
temp3 <- digamma(parg + qq)
temp3a <- digamma(parg)
temp3b <- digamma(qq)
temp4 <- log1p(temp2)
dl.dscale <- (aa/Scale) * (-parg + (parg+qq) / (1+1/temp2))
dl.da <- 1/aa + parg * temp1 - (parg+qq) * temp1 / (1+1/temp2)
dscale.deta <- dtheta.deta(Scale, .lscale , earg = .escale )
da.deta <- dtheta.deta(aa, .lshape1.a , earg = .eshape1.a )
myderiv <- if ( .lss ) {
c(w) * cbind(dl.dscale * dscale.deta,
dl.da * da.deta)
} else {
c(w) * cbind(dl.da * da.deta,
dl.dscale * dscale.deta)
}
myderiv[, interleave.VGAM(M, M1 = M1)]
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))),
weight = eval(substitute(expression({
temp5 <- trigamma(parg + qq)
temp5a <- trigamma(parg)
temp5b <- trigamma(qq)
ned2l.da <- (1 + parg + qq + parg * qq * (temp5a + temp5b +
(temp3b - temp3a + (parg-qq)/(parg*qq))^2 -
(parg^2 + qq^2) / (parg*qq)^2)) / (aa^2 * (1+parg+qq))
ned2l.dscale <- (aa^2) * parg * qq / ((1+parg+qq) * Scale^2)
ned2l.dascale <- (parg - qq - parg * qq *
(temp3a -temp3b)) / (Scale*(1 + parg+qq))
wz <- if ( .lss ) {
array(c(c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
} else {
array(c(c(w) * ned2l.da * da.deta^2,
c(w) * ned2l.dscale * dscale.deta^2,
c(w) * ned2l.dascale * da.deta * dscale.deta),
dim = c(n, M/M1, M1*(M1+1)/2))
}
wz <- arwz2wz(wz, M = M, M1 = M1)
wz
}), list( .lscale = lscale , .lshape1.a = lshape1.a,
.escale = escale , .eshape1.a = eshape1.a,
.lss = lss ))))
} # inv.paralogistic
Try the VGAM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.