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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:
## A implementation of simple CLMs (simple_clm), i.e., CLMs without
## scale and nominal effects.
simple_clm <-
function(formula, data, weights, start, subset, offset,
doFit = TRUE, na.action, contrasts, model = TRUE,
control = list(),
link = c("logit", "probit", "cloglog", "loglog"),
threshold = c("flexible", "symmetric", "symmetric2", "equidistant"), ...)
{
## Initial argument matching and testing:
mc <- match.call(expand.dots = FALSE)
link <- match.arg(link)
threshold <- match.arg(threshold)
## check for presence of formula:
if(missing(formula)) stop("Model needs a formula")
if(missing(contrasts)) contrasts <- NULL
## set control parameters:
control <- do.call(clm.control, c(control, list(...)))
## Compute: y, X, wts, off, mf:
if (missing(data))
data <- environment(formula)
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset", "weights", "na.action",
"offset"), names(mf), 0L)
mf <- mf[c(1L, m)]
mf$drop.unused.levels <- TRUE
mf[[1L]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
## Return model.frame?
if(control$method == "model.frame") return(mf)
y <- model.response(mf, "any") ## any storage mode
if(!is.factor(y)) stop("response needs to be a factor", call.=FALSE)
## design matrix:
mt <- attr(mf, "terms")
X <- if (!is.empty.model(mt))
model.matrix(mt, mf, contrasts)
else cbind("(Intercept)" = rep(1, NROW(y)))
## Test for intercept in X:
Xint <- match("(Intercept)", colnames(X), nomatch = 0)
if(Xint <= 0) {
X <- cbind("(Intercept)" = rep(1, NROW(y)), X)
warning("an intercept is needed and assumed in 'formula'",
call.=FALSE)
} ## intercept in X is guaranteed.
wts <- getWeights(mf)
off <- getOffsetStd(mf)
ylevels <- levels(droplevels(y[wts > 0]))
frames <- list(y=y, ylevels=ylevels, X=X)
## Compute the transpose of the Jacobian for the threshold function,
## tJac and the names of the threshold parameters, alpha.names:
frames <- c(frames, makeThresholds(ylevels, threshold))
## test for column rank deficiency in design matrices:
frames <- drop.cols(frames, silent=TRUE)
## Set envir rho with variables: B1, B2, o1, o2, wts, fitted:
rho <- clm.newRho(parent.frame(), y=frames$y, X=frames$X,
NOM=NULL, S=NULL,
weights=wts, offset=off, S.offset=NULL,
tJac=frames$tJac, control=control)
## Set starting values for the parameters:
start <- set.start(rho, start=start, get.start=missing(start),
threshold=threshold, link=link, frames=frames)
rho$par <- as.vector(start) ## remove attributes
## Set pfun, dfun and gfun in rho:
setLinks(rho, link)
## Possibly return the environment rho without fitting:
if(!doFit) return(rho)
## Fit the clm:
if(control$method == "Newton")
fit <- clm.fit.NR(rho, control)
else
fit <- clm.fit.optim(rho, control$method, control$ctrl)
### NOTE: we could add arg non.conv = c("error", "warn", "message") to
### allow non-converged fits to be returned.
## Modify and return results:
res <- clm.finalize(fit, weights=wts,
coef.names=frames$coef.names,
aliased=frames$aliased)
res$control <- control
res$link <- link
res$start <- start
if(control$method == "Newton" &&
!is.null(start.iter <- attr(start, "start.iter")))
res$niter <- res$niter + start.iter
res$threshold <- threshold
res$call <- match.call()
res$contrasts <- attr(frames$X, "contrasts")
res$na.action <- attr(mf, "na.action")
res$terms <- mt
res$xlevels <- .getXlevels(mt, mf)
res$tJac <- frames$tJac
res$y.levels <- frames$ylevels
## Check convergence:
conv <- conv.check(res, Theta.ok=TRUE, tol=control$tol)
print.conv.check(conv, action=control$convergence) ## print convergence message
res$vcov <- conv$vcov
res$cond.H <- conv$cond.H
res$convergence <- conv[!names(conv) %in% c("vcov", "cond.H")]
res$info <- with(res, {
data.frame("link" = link,
"threshold" = threshold,
"nobs" = nobs,
"logLik" = formatC(logLik, digits=2, format="f"),
"AIC" = formatC(-2*logLik + 2*edf, digits=2,
format="f"),
"niter" = paste(niter[1], "(", niter[2], ")", sep=""),
### NOTE: iterations to get starting values for scale models *are*
### included here.
"max.grad" = formatC(maxGradient, digits=2,
format="e")
## BIC is not part of output since it is not clear what
## the no. observations are.
)
})
class(res) <- "clm"
## add model.frame to results list?
if(model) res$model <- mf
return(res)
}
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