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#############################################################################
## Copyright (c) 2010-2022 Rune Haubo Bojesen Christensen
##
## This file is part of the ordinal package for R (*ordinal*)
##
## *ordinal* is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 2 of the License, or
## (at your option) any later version.
##
## *ordinal* is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## A copy of the GNU General Public License is available at
## <https://www.r-project.org/Licenses/> and/or
## <http://www.gnu.org/licenses/>.
#############################################################################
## This file contains:
## The function clm.fit() - an lm.fit or glm.fit equivalent for CLMs.
clm.fit <- function(y, ...) {
UseMethod("clm.fit")
}
clm.fit.factor <-
function(y, X, S, N, weights = rep(1, nrow(X)),
offset = rep(0, nrow(X)), S.offset = rep(0, nrow(X)),
control = list(), start, doFit=TRUE,
link = c("logit", "probit", "cloglog", "loglog", "cauchit",
"Aranda-Ordaz", "log-gamma"),
threshold = c("flexible", "symmetric", "symmetric2", "equidistant"),
...)
### This function basically does the same as clm, but without setting
### up the model matrices from formulae, and with minimal post
### processing after parameter estimation.
{
## Initial argument matching and testing:
threshold <- match.arg(threshold)
link <- match.arg(link)
control <- do.call(clm.control, control)
if(missing(y)) stop("please specify y")
if(missing(X)) X <- cbind("(Intercept)" = rep(1, length(y)))
stopifnot(is.factor(y), is.matrix(X))
if(missing(weights) || is.null(weights))
weights <- rep(1, length(y))
if(missing(offset) || is.null(offset))
offset <- rep(0, length(y))
if(missing(S.offset) || is.null(S.offset))
S.offset <- rep(0, length(y))
stopifnot(length(y) == nrow(X) &&
length(y) == length(weights) &&
length(y) == length(offset) &&
length(y) == length(S.offset))
frames <- list(y=y, X=X)
y[weights <= 0] <- NA
y.levels <- levels(droplevels(y))
struct <- namedList(y, X, weights, offset, S.offset, y.levels,
threshold, link, control, doFit)
## S and N are optional:
if(!missing(S) && !is.null(S)) {
struct$S <- S
stopifnot(is.matrix(S),
length(y) == nrow(S))
}
if(!missing(N) && !is.null(N)) {
struct$NOM <- N
stopifnot(is.matrix(N),
length(y) == nrow(N))
}
clm.fit.default(struct)
}
clm.fit.default <- function(y, ...)
### y: design object with the following components: ...
### (tJac=NULL), (y.levels=NULL), threshold, (aliased=NULL),
### (start=NULL), link, control, weights, (coef.names=NULL), y, X,
### (S=NULL), (NOM=NULL), doFit=TRUE, S.offset=NULL
{
## check args:
stopifnot(is.list(y))
y <- c(y, list(...))
stopifnot(all(
c("y", "X", "offset", "weights", "link", "threshold",
"control", "doFit") %in% names(y) ))
## preprocess design objects if needed:
if(is.null(y$y.levels)) y$y.levels <- levels(y$y)
if(is.null(y$tJac)) {
y <- c(y, makeThresholds(y$y.levels, y$threshold))
}
if(is.null(y$aliased))
y <- drop.cols(y, silent=TRUE, drop.scale=FALSE)
## Make model environment:
rho <- do.call(clm.newRho, y)
setLinks(rho, y$link)
start <- set.start(rho, start=y$start, get.start=is.null(y$start),
threshold=y$threshold, link=y$link,
frames=y)
rho$par <- as.vector(start) ## remove attributes
if(y$doFit == FALSE) return(rho)
if(length(rho$lambda) > 0 && y$control$method != "nlminb") {
message("Changing to 'nlminb' optimizer for flexible link function")
y$control$method <- "nlminb"
}
## Fit the model:
fit <- if(length(rho$lambda) > 0) {
clm_fit_flex(rho, control=y$control$ctrl)
} else if(y$control$method == "Newton") {
clm_fit_NR(rho, y$control)
} else {
clm_fit_optim(rho, y$control$method, y$control$ctrl)
}
## Adjust iteration count:
if(y$control$method == "Newton" &&
!is.null(start.iter <- attr(start, "start.iter")))
fit$niter <- fit$niter + start.iter
## Update coefficients, gradient, Hessian, edf, nobs, n,
## fitted.values, df.residual:
fit <- clm.finalize(fit, y$weights, y$coef.names, y$aliased)
fit$tJac <- format_tJac(y$tJac, y$y.levels, y$alpha.names)
th.res <- formatTheta(fit$alpha, fit$tJac, y, y$control$sign.nominal)
## Check convergence:
conv <- conv.check(fit, control=y$control, Theta.ok=th.res$Theta.ok,
tol=y$control$tol)
print.conv.check(conv, action=y$control$convergence) ## print convergence message
th.res$Theta.ok <- NULL
fit <- c(fit, conv[c("vcov", "cond.H")], th.res)
fit$convergence <- conv[!names(conv) %in% c("vcov", "cond.H")]
fit <- fit[sort(names(fit))]
class(fit) <- "clm.fit"
fit
}
clm.finalize <- function(fit, weights, coef.names, aliased)
### extracFromFit
###
### distinguishing between par and coef where the former does not
### contain aliased coefficients.
{
nalpha <- length(aliased$alpha)
nbeta <- length(aliased$beta)
nzeta <- length(aliased$zeta)
nlambda <- length(fit$lambda)
ncoef <- nalpha + nbeta + nzeta + nlambda ## including aliased coef
npar <- sum(!unlist(aliased)) + nlambda ## excluding aliased coef
stopifnot(length(fit$par) == npar)
if(nlambda) aliased <- c(aliased, list(lambda = FALSE))
if(nlambda) coef.names <- c(coef.names, list(lambda="lambda"))
fit <- within(fit, {
coefficients <- rep(NA, ncoef)
## ensure correct order of alpha, beta and zeta:
keep <- match(c("alpha", "beta", "zeta", "lambda"), names(aliased),
nomatch=0)
aliased <- lapply(aliased[keep], as.logical)
for(i in names(aliased))
names(aliased[[i]]) <- coef.names[keep][[i]]
names(coefficients) <- unlist(coef.names[keep])
par.names <- names(coefficients)[!unlist(aliased)]
coefficients[!unlist(aliased)] <- par
alpha <- coefficients[1:nalpha]
if(nbeta) beta <- coefficients[nalpha + 1:nbeta]
if(nzeta) zeta <- coefficients[nalpha + nbeta + 1:nzeta]
names(gradient) <- par.names
dimnames(Hessian) <- list(par.names, par.names)
edf <- npar ## estimated degrees of freedom
nobs <- sum(weights)
n <- length(weights)
fitted.values <- fitted
df.residual = nobs - edf
## keep <- i <- fitted <- par.names <- par <- coef.names <- NULL
})
notkeep <- c("keep", "i", "fitted", "par.names", "par",
"coef.names")
fit[!names(fit) %in% notkeep]
}
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