Nothing
R/profile.R
In evd: Functions for Extreme Value Distributions
"profile.evd" <- function(fitted, which = names(fitted$estimate), conf = 0.999, mesh = fitted$std.err[which]/4, xmin = rep(-Inf, length(which)), xmax = rep(Inf, length(which)), convergence = FALSE, method = "BFGS", control = list(maxit = 500), ...)
{
if(!inherits(fitted, "evd"))
stop("Use only with `evd' objects")
if(inherits(fitted, "extreme"))
stop("profiles not implemented for this model")
if(length(xmin) != length(which))
stop("`xmin' and `which' must have the same length")
if(length(xmax) != length(which))
stop("`xmax' and `which' must have the same length")
if(length(fitted$estimate) < 2)
stop("cannot profile one dimensional likelihood")
if(!is.character(which))
stop("`which' must be a character vector")
if(!all(which %in% names(fitted$estimate)))
stop("`which' contains unrecognized or unestimated parameters")
if(is.null(fitted$std.err) && missing(mesh))
stop("fitted model must contain standard errors")
prof.list <- as.list(numeric(length(which)))
names(xmin) <- names(xmax) <- names(prof.list) <- which
if(is.null(names(mesh))) names(mesh) <- which
mles <- fitted$estimate[which]
for(j in which) {
print(paste("profiling",j))
prof1 <- prof2 <- matrix(nrow = 0, ncol = length(fitted$estimate) + 1)
npmles <- fitted$estimate[!names(fitted$estimate) %in% j]
start <- as.list(npmles)
call.args <- c(list(fitted$data, start, 0), as.list(fitted$fixed),
list(FALSE, FALSE, method, FALSE, control))
names(call.args) <- c("x", "start", j, names(fitted$fixed),
"std.err", "corr", "method", "warn.inf", "control")
dimnames(prof1) <- dimnames(prof2) <- list(NULL, c(j, "deviance",
names(start)))
call.fn <- paste("f", class(fitted)[1], sep="")
if(inherits(fitted, "gev")) {
call.args$nsloc <- fitted$nsloc
call.args$prob <- fitted$prob
}
if(inherits(fitted, "gumbelx")) {
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
}
if(inherits(fitted, "pot")) {
call.args$threshold <- fitted$threshold
call.args$npp <- fitted$npp
call.args$period <- fitted$period
call.args$cmax <- fitted$cmax
call.args$r <- fitted$r
call.args$ulow <- fitted$ulow
call.args$rlow <- fitted$rlow
call.args$mper <- fitted$mper
}
if(inherits(fitted, "bvevd")) {
call.args$model <- fitted$model
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
call.args$sym <- fitted$sym
call.args$cloc <- fitted$cmar[1]
call.args$cscale <- fitted$cmar[2]
call.args$cshape <- fitted$cmar[3]
}
if(inherits(fitted, "bvpot")) {
call.args$threshold <- fitted$threshold
call.args$likelihood <- fitted$likelihood
call.args$model <- fitted$model
call.args$sym <- fitted$sym
call.args$cscale <- fitted$cmar[1]
call.args$cshape <- fitted$cmar[2]
}
lcnt <- TRUE; ppar <- mles[j]
while(lcnt) {
ppar <- as.vector(ppar + mesh[j])
if(ppar >= xmax[j]) ppar <- as.vector(xmax[j])
call.args[[j]] <- ppar
fit.mod <- do.call(call.fn, call.args)
if(convergence) print(fit.mod$convergence)
call.args[["start"]] <- as.list(fit.mod$estimate)
rop <- c(ppar, fit.mod$deviance, fit.mod$estimate)
prof1 <- rbind(prof1, rop)
ddf <- fit.mod$deviance - fitted$deviance
lcnt <- (ddf <= qchisq(conf, 1)) && (ppar != xmax[j])
}
call.args[["start"]] <- as.list(npmles)
lcnt <- TRUE; ppar <- mles[j]
while(lcnt) {
ppar <- as.vector(ppar - mesh[j])
if(ppar <= xmin[j]) ppar <- as.vector(xmin[j])
call.args[[j]] <- ppar
fit.mod <- do.call(call.fn, call.args)
if(convergence) print(fit.mod$convergence)
call.args[["start"]] <- as.list(fit.mod$estimate)
rop <- c(ppar, fit.mod$deviance, fit.mod$estimate)
prof2 <- rbind(prof2, rop)
ddf <- fit.mod$deviance - fitted$deviance
lcnt <- (ddf <= qchisq(conf, 1)) && (ppar != xmin[j])
}
rop <- c(mles[j], fitted$deviance, npmles)
prof2 <- prof2[nrow(prof2):1, ,drop = FALSE]
prof <- rbind(prof2, rop, prof1)
rownames(prof) <- NULL
rdev <- qchisq(conf, 1) + fitted$deviance
if(prof[1, "deviance"] == 2e6) {
prof <- prof[-1, ,drop = FALSE]
if(prof[1,"deviance"] <= rdev)
warning(paste("If", j, "is to satisfy `conf',",
"`mesh' must be smaller"))
}
if(prof[nrow(prof), "deviance"] == 2e6) {
prof <- prof[-nrow(prof), ,drop = FALSE]
if(prof[nrow(prof),"deviance"] <= rdev)
warning(paste("If", j, "is to satisfy `conf',",
"`mesh' must be smaller"))
}
prof.list[[j]] <- prof
}
structure(prof.list, deviance = fitted$deviance,
xmin = xmin, xmax = xmax, class = "profile.evd")
}
profile2d <- function (fitted, ...) {
UseMethod("profile2d")
}
"profile2d.evd" <- function(fitted, prof, which, pts = 20, convergence = FALSE, method = "Nelder-Mead", control = list(maxit = 5000), ...)
{
if(!inherits(fitted, "evd"))
stop("Use only with `evd' objects")
if(inherits(fitted, "extreme"))
stop("profiles not implemented for this model")
if (!inherits(prof, "profile.evd"))
stop("`prof' must be a `profile.evd' object")
if(length(fitted$estimate) < 3)
stop("Cannot profile two dimensional likelihood")
if(missing(which) || !is.character(which) || length(which) != 2)
stop("`which' must be a character vector of length two")
if(!all(which %in% names(fitted$estimate)))
stop("`which' contains unrecognized or unestimated parameters")
if(!all(which %in% names(prof)))
stop("`which' contains unprofiled parameters")
if(is.null(fitted$std.err))
stop("fitted model must contain standard errors")
prof.list <- as.list(numeric(3))
names(prof.list) <- c("trace", which)
limits1 <- range(prof[[which[1]]][,1])
limits2 <- range(prof[[which[2]]][,1])
mles <- fitted$estimate[which]
prof <- matrix(NA, nrow = pts^2, ncol = length(fitted$estimate) + 1)
parvec1 <- seq(limits1[1], limits1[2], length = pts)
prof.list[[which[1]]] <- parvec1
parvec2 <- seq(limits2[1], limits2[2], length = pts)
prof.list[[which[2]]] <- parvec2
pars <- expand.grid(parvec1, parvec2)
start <- as.list(fitted$estimate[!names(fitted$estimate) %in% which])
# if method unspecified supress optim 1d warnings
if(missing(method) && length(start) == 1) oldopt <- options(warn = -1)
call.args <- c(list(fitted$data, start, 0, 0), as.list(fitted$fixed),
list(FALSE, FALSE, method, FALSE, control))
names(call.args) <- c("x", "start", which[1], which[2],
names(fitted$fixed), "std.err", "corr", "method",
"warn.inf", "control")
dimnames(prof) <- list(NULL, c(which, "deviance", names(start)))
call.fn <- paste("f", class(fitted)[1], sep="")
if(inherits(fitted, "gev")) {
call.args$nsloc <- fitted$nsloc
call.args$prob <- fitted$prob
}
if(inherits(fitted, "gumbelx")) {
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
}
#if(inherits(fitted, "pot")) {
# call.args$threshold <- fitted$threshold
# call.args$npp <- fitted$npp
# call.args$period <- fitted$period
# call.args$cmax <- fitted$cmax
# call.args$r <- fitted$r
# call.args$ulow <- fitted$ulow
# call.args$rlow <- fitted$rlow
# call.args$mper <- fitted$mper
#}
if(inherits(fitted, "bvevd")) {
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
call.args$model <- fitted$model
call.args$sym <- fitted$sym
call.args$cloc <- fitted$cmar[1]
call.args$cscale <- fitted$cmar[2]
call.args$cshape <- fitted$cmar[3]
}
if(inherits(fitted, "bvpot")) {
call.args$threshold <- fitted$threshold
call.args$likelihood <- fitted$likelihood
call.args$model <- fitted$model
call.args$sym <- fitted$sym
call.args$cscale <- fitted$cmar[1]
call.args$cshape <- fitted$cmar[2]
}
for(i in 1:pts^2) {
call.args[[which[1]]] <- pars[i,1]
call.args[[which[2]]] <- pars[i,2]
fit.mod <- do.call(call.fn, call.args)
if(convergence) print(fit.mod$convergence)
prof[i,1] <- pars[i,1]
prof[i,2] <- pars[i,2]
prof[i,3] <- fit.mod$deviance
prof[i,-(1:3)] <- fit.mod$estimate
}
prof.list[["trace"]] <- prof
if(missing(method) && length(start) == 1) oldopt <- options(oldopt)
if(any(prof[,"deviance"] == 2e6))
warning("non-convergence present in profile2d object")
structure(prof.list, deviance = fitted$deviance, class = "profile2d.evd")
}
"plot.profile.evd" <- function(x, which = names(x), main = NULL,
ask = nb.fig < length(which) && dev.interactive(), ci = 0.95,
clty = 2, ...)
{
if (!inherits(x, "profile.evd"))
stop("Use only with `profile.evd' objects")
if(!is.character(which))
stop("`which' must be a character vector")
if(!all(which %in% names(x)))
stop("`which' contains unprofiled parameters")
nb.fig <- prod(par("mfcol"))
if (ask) {
op <- par(ask = TRUE)
on.exit(par(op))
}
if(is.null(main)) {
fls <- toupper(substr(which, 1, 1))
ols <- substr(which, 2, nchar(which))
cwhich <- paste(fls, ols, sep = "")
main <- paste("Profile Log-likelihood of", cwhich)
}
for(i in which) {
plot(spline(x[[i]][,1], -x[[i]][,2]/2, n = 75), type = "l",
xlab = i, ylab = "profile log-likelihood",
main = main[match(i,which)], ...)
cdist <- -(attributes(x)$deviance + qchisq(ci, df = 1))/2
abline(h = cdist, lty = clty)
}
invisible(x)
}
"confint.profile.evd" <- function(object, parm, level = 0.95, ...)
{
if(missing(parm))
parm <- names(object)
if(!all(parm %in% names(object)))
stop("`parm' contains unprofiled parameters")
rdev <- attributes(object)$deviance + qchisq(level, df = 1)
pct <- c("lower", "upper")
ci <- array(NA, dim = c(length(parm), 2), dimnames = list(parm, pct))
# Assumes profile trace is unimodal
for(i in parm) {
x <- object[[i]]
n <- nrow(x)
th.l <- (x[1, 1] == attributes(object)$xmin[i])
th.u <- (x[n, 1] == attributes(object)$xmax[i])
halves <- c(diff(x[,"deviance"]) < 0, FALSE)
if(x[1,"deviance"] <= rdev && !th.l) {
warning(paste("cannot calculate lower confidence limit for", i))
ci[i,1] <- NA
}
if(x[1,"deviance"] <= rdev && th.l) ci[i,1] <- x[1, 1]
if(x[1,"deviance"] > rdev)
ci[i,1] <- approx(x[halves,2], x[halves,1], xout = rdev)$y
if(x[n,"deviance"] <= rdev && !th.u) {
warning(paste("cannot calculate upper confidence limit for", i))
ci[i,2] <- NA
}
if(x[n,"deviance"] <= rdev && th.u) ci[i,2] <- x[n, 1]
if(x[n,"deviance"] > rdev)
ci[i,2] <- approx(x[!halves,2], x[!halves,1], xout = rdev)$y
}
ci
}
"plot.profile2d.evd" <- function(x, main = NULL, ci = c(0.5,0.8,0.9,0.95,0.975, 0.99, 0.995), col = heat.colors(8), intpts = 75, xaxs = "r", yaxs = "r", ...)
{
if (!inherits(x, "profile2d.evd"))
stop("Use only with `profile2d.evd' objects")
which <- names(x)[2:3]
if(is.null(main)) {
fls <- toupper(substr(which, 1, 1))
ols <- substr(which, 2, nchar(which))
cwhich <- paste(fls, ols, sep = "")
main <- paste("Profile Log-likelihood of", cwhich[1], "and", cwhich[2])
}
br.pts <- attributes(x)$deviance + qchisq(c(0,ci), df = 2)
prof <- x$trace[,"deviance"]
if(any(prof == 2e6))
warning("non-convergence present in profile2d object")
prof <- -prof/2
br.pts <- (-br.pts/2)[length(br.pts):1]
col <- col[length(col):1]
if(!requireNamespace("interp", quietly = TRUE)) {
image(x[[which[1]]], x[[which[2]]],
matrix(prof, nrow = length(x[[which[1]]])),
col = col, breaks = c(-1e6+1, br.pts),
main = main, xlab = which[1], ylab = which[2], xaxs = xaxs,
yaxs = yaxs, ...)
}
else {
lim1 <- range(x[[which[1]]])
lim2 <- range(x[[which[2]]])
prof.interp <- interp::interp(x$trace[,1], x$trace[,2], prof,
xo = seq(lim1[1], lim1[2], length = intpts),
yo = seq(lim2[1], lim2[2], length = intpts))
image(prof.interp, col = col, breaks = c(min(prof), br.pts),
main = main, xlab = which[1], ylab = which[2], xaxs = xaxs,
yaxs = yaxs, ...)
}
invisible(x)
}
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"profile.evd" <- function(fitted, which = names(fitted$estimate), conf = 0.999, mesh = fitted$std.err[which]/4, xmin = rep(-Inf, length(which)), xmax = rep(Inf, length(which)), convergence = FALSE, method = "BFGS", control = list(maxit = 500), ...)
{
if(!inherits(fitted, "evd"))
stop("Use only with `evd' objects")
if(inherits(fitted, "extreme"))
stop("profiles not implemented for this model")
if(length(xmin) != length(which))
stop("`xmin' and `which' must have the same length")
if(length(xmax) != length(which))
stop("`xmax' and `which' must have the same length")
if(length(fitted$estimate) < 2)
stop("cannot profile one dimensional likelihood")
if(!is.character(which))
stop("`which' must be a character vector")
if(!all(which %in% names(fitted$estimate)))
stop("`which' contains unrecognized or unestimated parameters")
if(is.null(fitted$std.err) && missing(mesh))
stop("fitted model must contain standard errors")
prof.list <- as.list(numeric(length(which)))
names(xmin) <- names(xmax) <- names(prof.list) <- which
if(is.null(names(mesh))) names(mesh) <- which
mles <- fitted$estimate[which]
for(j in which) {
print(paste("profiling",j))
prof1 <- prof2 <- matrix(nrow = 0, ncol = length(fitted$estimate) + 1)
npmles <- fitted$estimate[!names(fitted$estimate) %in% j]
start <- as.list(npmles)
call.args <- c(list(fitted$data, start, 0), as.list(fitted$fixed),
list(FALSE, FALSE, method, FALSE, control))
names(call.args) <- c("x", "start", j, names(fitted$fixed),
"std.err", "corr", "method", "warn.inf", "control")
dimnames(prof1) <- dimnames(prof2) <- list(NULL, c(j, "deviance",
names(start)))
call.fn <- paste("f", class(fitted)[1], sep="")
if(inherits(fitted, "gev")) {
call.args$nsloc <- fitted$nsloc
call.args$prob <- fitted$prob
}
if(inherits(fitted, "gumbelx")) {
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
}
if(inherits(fitted, "pot")) {
call.args$threshold <- fitted$threshold
call.args$npp <- fitted$npp
call.args$period <- fitted$period
call.args$cmax <- fitted$cmax
call.args$r <- fitted$r
call.args$ulow <- fitted$ulow
call.args$rlow <- fitted$rlow
call.args$mper <- fitted$mper
}
if(inherits(fitted, "bvevd")) {
call.args$model <- fitted$model
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
call.args$sym <- fitted$sym
call.args$cloc <- fitted$cmar[1]
call.args$cscale <- fitted$cmar[2]
call.args$cshape <- fitted$cmar[3]
}
if(inherits(fitted, "bvpot")) {
call.args$threshold <- fitted$threshold
call.args$likelihood <- fitted$likelihood
call.args$model <- fitted$model
call.args$sym <- fitted$sym
call.args$cscale <- fitted$cmar[1]
call.args$cshape <- fitted$cmar[2]
}
lcnt <- TRUE; ppar <- mles[j]
while(lcnt) {
ppar <- as.vector(ppar + mesh[j])
if(ppar >= xmax[j]) ppar <- as.vector(xmax[j])
call.args[[j]] <- ppar
fit.mod <- do.call(call.fn, call.args)
if(convergence) print(fit.mod$convergence)
call.args[["start"]] <- as.list(fit.mod$estimate)
rop <- c(ppar, fit.mod$deviance, fit.mod$estimate)
prof1 <- rbind(prof1, rop)
ddf <- fit.mod$deviance - fitted$deviance
lcnt <- (ddf <= qchisq(conf, 1)) && (ppar != xmax[j])
}
call.args[["start"]] <- as.list(npmles)
lcnt <- TRUE; ppar <- mles[j]
while(lcnt) {
ppar <- as.vector(ppar - mesh[j])
if(ppar <= xmin[j]) ppar <- as.vector(xmin[j])
call.args[[j]] <- ppar
fit.mod <- do.call(call.fn, call.args)
if(convergence) print(fit.mod$convergence)
call.args[["start"]] <- as.list(fit.mod$estimate)
rop <- c(ppar, fit.mod$deviance, fit.mod$estimate)
prof2 <- rbind(prof2, rop)
ddf <- fit.mod$deviance - fitted$deviance
lcnt <- (ddf <= qchisq(conf, 1)) && (ppar != xmin[j])
}
rop <- c(mles[j], fitted$deviance, npmles)
prof2 <- prof2[nrow(prof2):1, ,drop = FALSE]
prof <- rbind(prof2, rop, prof1)
rownames(prof) <- NULL
rdev <- qchisq(conf, 1) + fitted$deviance
if(prof[1, "deviance"] == 2e6) {
prof <- prof[-1, ,drop = FALSE]
if(prof[1,"deviance"] <= rdev)
warning(paste("If", j, "is to satisfy `conf',",
"`mesh' must be smaller"))
}
if(prof[nrow(prof), "deviance"] == 2e6) {
prof <- prof[-nrow(prof), ,drop = FALSE]
if(prof[nrow(prof),"deviance"] <= rdev)
warning(paste("If", j, "is to satisfy `conf',",
"`mesh' must be smaller"))
}
prof.list[[j]] <- prof
}
structure(prof.list, deviance = fitted$deviance,
xmin = xmin, xmax = xmax, class = "profile.evd")
}
profile2d <- function (fitted, ...) {
UseMethod("profile2d")
}
"profile2d.evd" <- function(fitted, prof, which, pts = 20, convergence = FALSE, method = "Nelder-Mead", control = list(maxit = 5000), ...)
{
if(!inherits(fitted, "evd"))
stop("Use only with `evd' objects")
if(inherits(fitted, "extreme"))
stop("profiles not implemented for this model")
if (!inherits(prof, "profile.evd"))
stop("`prof' must be a `profile.evd' object")
if(length(fitted$estimate) < 3)
stop("Cannot profile two dimensional likelihood")
if(missing(which) || !is.character(which) || length(which) != 2)
stop("`which' must be a character vector of length two")
if(!all(which %in% names(fitted$estimate)))
stop("`which' contains unrecognized or unestimated parameters")
if(!all(which %in% names(prof)))
stop("`which' contains unprofiled parameters")
if(is.null(fitted$std.err))
stop("fitted model must contain standard errors")
prof.list <- as.list(numeric(3))
names(prof.list) <- c("trace", which)
limits1 <- range(prof[[which[1]]][,1])
limits2 <- range(prof[[which[2]]][,1])
mles <- fitted$estimate[which]
prof <- matrix(NA, nrow = pts^2, ncol = length(fitted$estimate) + 1)
parvec1 <- seq(limits1[1], limits1[2], length = pts)
prof.list[[which[1]]] <- parvec1
parvec2 <- seq(limits2[1], limits2[2], length = pts)
prof.list[[which[2]]] <- parvec2
pars <- expand.grid(parvec1, parvec2)
start <- as.list(fitted$estimate[!names(fitted$estimate) %in% which])
# if method unspecified supress optim 1d warnings
if(missing(method) && length(start) == 1) oldopt <- options(warn = -1)
call.args <- c(list(fitted$data, start, 0, 0), as.list(fitted$fixed),
list(FALSE, FALSE, method, FALSE, control))
names(call.args) <- c("x", "start", which[1], which[2],
names(fitted$fixed), "std.err", "corr", "method",
"warn.inf", "control")
dimnames(prof) <- list(NULL, c(which, "deviance", names(start)))
call.fn <- paste("f", class(fitted)[1], sep="")
if(inherits(fitted, "gev")) {
call.args$nsloc <- fitted$nsloc
call.args$prob <- fitted$prob
}
if(inherits(fitted, "gumbelx")) {
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
}
#if(inherits(fitted, "pot")) {
# call.args$threshold <- fitted$threshold
# call.args$npp <- fitted$npp
# call.args$period <- fitted$period
# call.args$cmax <- fitted$cmax
# call.args$r <- fitted$r
# call.args$ulow <- fitted$ulow
# call.args$rlow <- fitted$rlow
# call.args$mper <- fitted$mper
#}
if(inherits(fitted, "bvevd")) {
call.args$nsloc1 <- fitted$nsloc1
call.args$nsloc2 <- fitted$nsloc2
call.args$model <- fitted$model
call.args$sym <- fitted$sym
call.args$cloc <- fitted$cmar[1]
call.args$cscale <- fitted$cmar[2]
call.args$cshape <- fitted$cmar[3]
}
if(inherits(fitted, "bvpot")) {
call.args$threshold <- fitted$threshold
call.args$likelihood <- fitted$likelihood
call.args$model <- fitted$model
call.args$sym <- fitted$sym
call.args$cscale <- fitted$cmar[1]
call.args$cshape <- fitted$cmar[2]
}
for(i in 1:pts^2) {
call.args[[which[1]]] <- pars[i,1]
call.args[[which[2]]] <- pars[i,2]
fit.mod <- do.call(call.fn, call.args)
if(convergence) print(fit.mod$convergence)
prof[i,1] <- pars[i,1]
prof[i,2] <- pars[i,2]
prof[i,3] <- fit.mod$deviance
prof[i,-(1:3)] <- fit.mod$estimate
}
prof.list[["trace"]] <- prof
if(missing(method) && length(start) == 1) oldopt <- options(oldopt)
if(any(prof[,"deviance"] == 2e6))
warning("non-convergence present in profile2d object")
structure(prof.list, deviance = fitted$deviance, class = "profile2d.evd")
}
"plot.profile.evd" <- function(x, which = names(x), main = NULL,
ask = nb.fig < length(which) && dev.interactive(), ci = 0.95,
clty = 2, ...)
{
if (!inherits(x, "profile.evd"))
stop("Use only with `profile.evd' objects")
if(!is.character(which))
stop("`which' must be a character vector")
if(!all(which %in% names(x)))
stop("`which' contains unprofiled parameters")
nb.fig <- prod(par("mfcol"))
if (ask) {
op <- par(ask = TRUE)
on.exit(par(op))
}
if(is.null(main)) {
fls <- toupper(substr(which, 1, 1))
ols <- substr(which, 2, nchar(which))
cwhich <- paste(fls, ols, sep = "")
main <- paste("Profile Log-likelihood of", cwhich)
}
for(i in which) {
plot(spline(x[[i]][,1], -x[[i]][,2]/2, n = 75), type = "l",
xlab = i, ylab = "profile log-likelihood",
main = main[match(i,which)], ...)
cdist <- -(attributes(x)$deviance + qchisq(ci, df = 1))/2
abline(h = cdist, lty = clty)
}
invisible(x)
}
"confint.profile.evd" <- function(object, parm, level = 0.95, ...)
{
if(missing(parm))
parm <- names(object)
if(!all(parm %in% names(object)))
stop("`parm' contains unprofiled parameters")
rdev <- attributes(object)$deviance + qchisq(level, df = 1)
pct <- c("lower", "upper")
ci <- array(NA, dim = c(length(parm), 2), dimnames = list(parm, pct))
# Assumes profile trace is unimodal
for(i in parm) {
x <- object[[i]]
n <- nrow(x)
th.l <- (x[1, 1] == attributes(object)$xmin[i])
th.u <- (x[n, 1] == attributes(object)$xmax[i])
halves <- c(diff(x[,"deviance"]) < 0, FALSE)
if(x[1,"deviance"] <= rdev && !th.l) {
warning(paste("cannot calculate lower confidence limit for", i))
ci[i,1] <- NA
}
if(x[1,"deviance"] <= rdev && th.l) ci[i,1] <- x[1, 1]
if(x[1,"deviance"] > rdev)
ci[i,1] <- approx(x[halves,2], x[halves,1], xout = rdev)$y
if(x[n,"deviance"] <= rdev && !th.u) {
warning(paste("cannot calculate upper confidence limit for", i))
ci[i,2] <- NA
}
if(x[n,"deviance"] <= rdev && th.u) ci[i,2] <- x[n, 1]
if(x[n,"deviance"] > rdev)
ci[i,2] <- approx(x[!halves,2], x[!halves,1], xout = rdev)$y
}
ci
}
"plot.profile2d.evd" <- function(x, main = NULL, ci = c(0.5,0.8,0.9,0.95,0.975, 0.99, 0.995), col = heat.colors(8), intpts = 75, xaxs = "r", yaxs = "r", ...)
{
if (!inherits(x, "profile2d.evd"))
stop("Use only with `profile2d.evd' objects")
which <- names(x)[2:3]
if(is.null(main)) {
fls <- toupper(substr(which, 1, 1))
ols <- substr(which, 2, nchar(which))
cwhich <- paste(fls, ols, sep = "")
main <- paste("Profile Log-likelihood of", cwhich[1], "and", cwhich[2])
}
br.pts <- attributes(x)$deviance + qchisq(c(0,ci), df = 2)
prof <- x$trace[,"deviance"]
if(any(prof == 2e6))
warning("non-convergence present in profile2d object")
prof <- -prof/2
br.pts <- (-br.pts/2)[length(br.pts):1]
col <- col[length(col):1]
if(!requireNamespace("interp", quietly = TRUE)) {
image(x[[which[1]]], x[[which[2]]],
matrix(prof, nrow = length(x[[which[1]]])),
col = col, breaks = c(-1e6+1, br.pts),
main = main, xlab = which[1], ylab = which[2], xaxs = xaxs,
yaxs = yaxs, ...)
}
else {
lim1 <- range(x[[which[1]]])
lim2 <- range(x[[which[2]]])
prof.interp <- interp::interp(x$trace[,1], x$trace[,2], prof,
xo = seq(lim1[1], lim1[2], length = intpts),
yo = seq(lim2[1], lim2[2], length = intpts))
image(prof.interp, col = col, breaks = c(min(prof), br.pts),
main = main, xlab = which[1], ylab = which[2], xaxs = xaxs,
yaxs = yaxs, ...)
}
invisible(x)
}
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