Nothing
conLM.lm <- function(object, constraints = NULL, se = "standard",
B = 999, rhs = NULL, neq = 0L, mix.weights = "pmvnorm",
mix.bootstrap = 99999L, parallel = "no", ncpus = 1L, cl = NULL,
seed = NULL, control = list(), verbose = FALSE,
debug = FALSE, ...) {
# check class
if (!(class(object)[1] == "lm")) {
stop("Restriktor ERROR: object must be of class lm.")
}
# standard error methods
if (se == "default") {
se <- "standard"
} else if (se == "boot.residual") {
se <- "boot.model.based"
}
if (!(se %in% c("none","standard","const","boot.model.based","boot.standard",
"HC","HC0","HC1","HC2","HC3","HC4","HC4m","HC5"))) {
stop("Restriktor ERROR: standard error method ", sQuote(se), " unknown.", call. = FALSE)
}
# check method to compute chi-square-bar weights
if (!(mix.weights %in% c("pmvnorm", "boot", "none"))) {
stop("Restriktor ERROR: ", sQuote(mix.weights), " method unknown. Choose from \"pmvnorm\", \"boot\", or \"none\".", call. = FALSE)
}
# timing
start.time0 <- start.time <- proc.time()[3]; timing <- list()
# store call
mc <- match.call()
# rename for internal use
Amat <- constraints
bvec <- rhs
meq <- neq
# response variable
y <- as.matrix(object$model[, attr(object$terms, "response")])
## model matrix
X <- model.matrix(object)[,,drop = FALSE]
# model summary
so <- summary(object)
# unconstrained residual variance (weighted)
s2 <- so$sigma^2
# weigths
weights <- weights(object)
# unconstrained estimates
b.unrestr <- coef(object)
b.unrestr[abs(b.unrestr) < ifelse(is.null(control$tol), sqrt(.Machine$double.eps),
control$tol)] <- 0L
# ML unconstrained MSE
Sigma <- vcov(object)
# number of parameters
p <- length(coef(object))
# sample size
n <- dim(X)[1]
# compute log-likelihood
residuals <- object$residuals
object.restr <- list(residuals = residuals, weights = weights)
ll.unrestr <- con_loglik_lm(object.restr)
if (debug) {
print(list(loglik.unc = ll.unrestr))
}
timing$preparation <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
# deal with constraints
if (!is.null(constraints)) {
restr.OUT <- con_constraints(object,
VCOV = Sigma,
est = b.unrestr,
constraints = Amat,
bvec = bvec,
meq = meq,
debug = debug)
# a list with useful information about the restriktions.}
CON <- restr.OUT$CON
# a parameter table with information about the observed variables in the object
# and the imposed restriktions.}
parTable <- restr.OUT$parTable
# constraints matrix
Amat <- restr.OUT$Amat
# rhs
bvec <- restr.OUT$bvec
# neq
meq <- restr.OUT$meq
} else if (is.null(constraints)) {
# no constraints specified - needed for GORIC to include unconstrained model
CON <- NULL
parTable <- NULL
Amat <- rbind(rep(0L, p))
bvec <- rep(0L, nrow(Amat))
meq <- 0L
}
## if only new parameters are defined and no constraints
if (length(Amat) == 0L) {
Amat <- rbind(rep(0L, p))
bvec <- rep(0L, nrow(Amat))
meq <- 0L
}
## create list for warning messages
messages <- list()
## check if constraint matrix is of full-row rank.
rAmat <- GaussianElimination(t(Amat))
if (mix.weights == "pmvnorm") {
if (rAmat$rank < nrow(Amat) && rAmat$rank != 0L) {
messages$mix_weights <- paste(
"Restriktor message: Since the constraint matrix is not full row-rank, the level probabilities
are calculated using mix.weights = \"boot\" (the default is mix.weights = \"pmvnorm\").
For more information see ?restriktor.\n"
)
mix.weights <- "boot"
}
} else if (rAmat$rank < nrow(Amat) &&
!(se %in% c("none", "boot.model.based", "boot.standard")) &&
rAmat$rank != 0L) {
se <- "none"
warning(paste("\nRestriktor Warning: No standard errors could be computed.
The constraint matrix must be full row-rank.
Try se = \"boot.model.based\" or \"boot.standard\"."), call. = FALSE)
}
## some checks
if (ncol(Amat) != length(b.unrestr)) {
stop("Restriktor ERROR: length coefficients and the number of",
"\n columns constraints-matrix must be identical", call. = FALSE)
}
if (!(nrow(Amat) == length(bvec))) {
stop("nrow(Amat) != length(bvec)")
}
timing$constraints <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
# compute residual degreees of freedom, corrected for equality constraints.
df.residual <- n - (p - qr(Amat[0:meq,])$rank)
# check if the constraints are not in line with the data, else skip optimization
if (all(Amat %*% c(b.unrestr) - bvec >= 0 * bvec) & meq == 0) {
b.restr <- b.unrestr
OUT <- list(CON = CON,
call = mc,
timing = timing,
parTable = parTable,
b.unrestr = b.unrestr,
b.restr = b.unrestr,
residuals = residuals, # unweighted residuals
fitted = object$fitted.values,
weights = weights,
df.residual = object$df.residual,
R2.org = so$r.squared,
R2.reduced = so$r.squared,
s2 = s2,
loglik = ll.unrestr,
Sigma = Sigma,
constraints = Amat,
rhs = bvec,
neq = meq,
wt.bar = NULL,
iact = 0L,
bootout = NULL,
control = control)
} else {
# compute constrained estimates using quadprog
out.solver <- con_solver_lm(X = X,
y = y,
w = weights,
Amat = Amat,
bvec = bvec,
meq = meq,
absval = ifelse(is.null(control$absval),
sqrt(.Machine$double.eps),
control$absval),
maxit = ifelse(is.null(control$maxit), 1e04,
control$maxit))
out.QP <- out.solver$qp
b.restr <- out.QP$solution
names(b.restr) <- names(b.unrestr)
b.restr[abs(b.restr) < ifelse(is.null(control$tol),
sqrt(.Machine$double.eps),
control$tol)] <- 0L
timing$optim <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
# lm
if (ncol(y) == 1L) {
fitted <- X %*% b.restr
residuals <- y - fitted
# compute log-likelihood
object.restr <- list(residuals = residuals, weights = weights)
ll.restr <- con_loglik_lm(object.restr)
if (debug) {
print(list(loglik.restr = ll.restr))
}
# compute R^2
if (is.null(weights)) {
mss <- if (attr(object$terms, "intercept")) {
sum((fitted - mean(fitted))^2)
} else { sum(fitted^2) }
rss <- sum(residuals^2)
} else {
mss <- if (attr(object$terms, "intercept")) {
m <- sum(weights * fitted / sum(weights))
sum(weights * (fitted - m)^2)
} else { sum(weights * fitted^2) }
rss <- sum(weights * residuals^2)
}
R2.reduced <- mss / (mss + rss)
# compute weighted residuals
if (is.null(weights)) {
s2 <- sum(residuals^2) / df.residual
} else {
s2 <- sum(weights * residuals^2) / df.residual
}
} else {
stop("mlm not supported. Switch to conMLM.")
}
OUT <- list(CON = CON,
call = mc,
timing = timing,
parTable = parTable,
b.unrestr = b.unrestr,
b.restr = b.restr,
residuals = residuals, # unweighted residuals
fitted = fitted,
weights = weights,
df.residual = object$df.residual,
R2.org = so$r.squared,
R2.reduced = R2.reduced,
s2 = s2,
loglik = ll.restr,
Sigma = Sigma,
constraints = Amat,
rhs = bvec,
neq = meq,
wt.bar = NULL,
iact = out.QP$iact,
bootout = NULL,
control = control)
}
# original object
OUT$model.org <- object
# type standard error
OUT$se <- se
OUT$information <- 1/s2 * crossprod(X)
## compute standard errors based on the augmented inverted information matrix or
## based on the standard bootstrap or model.based bootstrap
if (se != "none") {
is.augmented <- TRUE
if (all(c(Amat) == 0)) {
# unrestricted case
is.augmented <- FALSE
}
if (!(se %in% c("boot.model.based", "boot.standard"))) {
information.inv <- try(con_augmented_information(information = OUT$information,
is.augmented = is.augmented,
X = X,
b.unrestr = b.unrestr,
b.restr = b.restr,
Amat = Amat,
bvec = bvec,
meq = meq), silent = TRUE)
if (inherits(information.inv, "try-error")) {
stop(paste("Restriktor Warning: No standard errors could be computed.
Try to set se = \"none\", \"boot.model.based\" or \"boot.standard\"."),
call. = FALSE)
}
attr(OUT$information, "inverted") <- information.inv$information
attr(OUT$information, "augmented") <- information.inv$information.augmented
if (debug) {
print(list(information = OUT$information))
}
} else if (se == "boot.model.based") {
if (attr(object$terms, "intercept") && any(Amat[, 1] == 1)) {
stop("Restriktor ERROR: no restrictions on intercept possible",
"\n for 'se = boot.model.based' bootstrap method.", call. = FALSE)
}
OUT$bootout <- con_boot_lm(object = object,
B = B,
fixed = TRUE,
Amat = Amat,
bvec = bvec,
meq = meq,
se = "none",
mix.weights = "none",
parallel = parallel,
ncpus = ncpus,
cl = cl)
if (debug) {
print(list(bootout = OUT$bootout))
}
} else if (se == "boot.standard") {
OUT$bootout <- con_boot_lm(object = object,
B = B,
fixed = FALSE,
Amat = Amat,
bvec = bvec,
meq = meq,
se = "none",
mix.weights = "none",
parallel = parallel,
ncpus = ncpus,
cl = cl)
if (debug) {
print(list(bootout = OUT$bootout))
}
}
timing$standard.error <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
}
## determing level probabilities
# start timing
start.time <- proc.time()[3]
# compute chi-square-bar weights
if (mix.weights != "none") {
if (nrow(Amat) == meq) {
# equality constraints only
wt.bar <- rep(0L, ncol(Sigma) + 1)
wt.bar.idx <- ncol(Sigma) - qr(Amat)$rank + 1
wt.bar[wt.bar.idx] <- 1
} else if (all(c(Amat) == 0)) {
# unrestricted case
wt.bar <- c(rep(0L, p), 1)
} else if (mix.weights == "boot") {
# compute chi-square-bar weights based on Monte Carlo simulation
wt.bar <- con_weights_boot(VCOV = Sigma,
Amat = Amat,
meq = meq,
R = mix.bootstrap,
parallel = parallel,
ncpus = ncpus,
cl = cl,
seed = seed,
verbose = verbose)
attr(wt.bar, "mix.bootstrap") <- mix.bootstrap
} else if (mix.weights == "pmvnorm" && (meq < nrow(Amat))) {
# compute chi-square-bar weights based on pmvnorm
wt.bar <- rev(con_weights(Amat %*% Sigma %*% t(Amat), meq = meq))
}
} else {
wt.bar <- NA
}
attr(wt.bar, "method") <- mix.weights
OUT$wt.bar <- wt.bar
if (debug) {
print(list(mix.weights = wt.bar))
}
timing$mix.weights <- (proc.time()[3] - start.time)
OUT$messages <- messages
OUT$timing$total <- (proc.time()[3] - start.time0)
class(OUT) <- c("restriktor", "conLM")
OUT
}
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