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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 predict method for clm objects.
predict.clm <-
function(object, newdata, se.fit = FALSE, interval = FALSE,
level = 0.95,
type = c("prob", "class", "cum.prob", "linear.predictor"),
na.action = na.pass, ...)
### result - a list of predictions (fit)
### OPTION: restore names of the fitted values
###
### Assumes object has terms, xlevels, contrasts, tJac
{
## match and test arguments:
type <- match.arg(type)
se.fit <- as.logical(se.fit)[1]
interval <- as.logical(interval)[1]
stopifnot(length(level) == 1 && is.numeric(level) && level < 1 &&
level > 0)
if(type == "class" && (se.fit || interval)) {
warning("se.fit and interval set to FALSE for type = 'class'")
se.fit <- interval <- FALSE
}
cov <- if(se.fit || interval) vcov(object) else NULL
### Get newdata object; fill in response if missing and always for
### type=="class":
has.response <- TRUE
if(type == "class" && missing(newdata))
## newdata <- update(object, method="model.frame")$mf
newdata <- model.frame(object)
## newdata supplied or type=="class":
has.newdata <- !(missing(newdata) || is.null(newdata))
if(has.newdata || type=="class") {
if(has.newdata && sum(unlist(object$aliased)) > 0)
warning("predictions from column rank-deficient fit may be misleading")
newdata <- as.data.frame(newdata)
## Test if response is in newdata:
resp <- response.name(object$terms)
## remove response from newdata if type == "class"
if(type == "class") newdata <- newdata[!names(newdata) %in% resp]
has.response <- resp %in% names(newdata) ## FALSE for type == "class"
if(!has.response) {
## fill in response variable in newdata if missing:
ylev <- object$y.levels
nlev <- length(ylev)
nnd <- nrow(newdata)
newdata <-
cbind(newdata[rep(1:nnd, each=nlev) , , drop=FALSE],
factor(rep(ylev, nnd), levels=ylev, ordered=TRUE))
names(newdata)[ncol(newdata)] <- resp
}
### Set model matrices:
if(is.null(attr(object$terms, "predvars")))
warning(paste0("terms object does not have a predvars attribute: ",
"predictions may be misleading"))
mf <- model.frame(object$terms, newdata, na.action=na.action,
xlev=object$xlevels)
## model.frame will warn, but here we also throw an error:
if(nrow(mf) != nrow(newdata))
stop("length of variable(s) found do not match nrow(newdata)")
## check that variables are of the right type:
if (!is.null(cl <- attr(object$terms, "dataClasses")))
.checkMFClasses(cl, mf)
## make model.matrix:
X <- model.matrix(object$terms, mf, contrasts = object$contrasts)
Xint <- match("(Intercept)", colnames(X), nomatch = 0L)
n <- nrow(X)
if(Xint <= 0) X <- cbind("(Intercept)" = rep(1, n), X)
# if(object$control$sign.location == "negative") NOM[, -1] <- -NOM[, -1]
## drop aliased columns:
if(sum(object$aliased$beta) > 0)
X <- X[, !c(FALSE, object$aliased$beta), drop=FALSE]
## handle offset (from predict.lm):
### NOTE: Could factor the offset handling out in its own function for
### code clarity:
offset <- rep(0, nrow(X))
if(!is.null(off.num <- attr(object$terms, "offset")))
for(i in off.num) offset <- offset +
eval(attr(object$terms, "variables")[[i + 1]], newdata)
y <- model.response(mf)
if(any(!levels(y) %in% object$y.levels))
stop(gettextf("response factor '%s' has new levels",
response.name(object$terms)))
### make NOMINAL model.matrix:
if(is.nom <- !is.null(object$nom.terms)) {
## allows NAs to pass through to fit, se.fit, lwr and upr:
nom.mf <- model.frame(object$nom.terms, newdata,
na.action=na.action,
xlev=object$nom.xlevels)
## model.frame will warn, but here we also throw an error:
if(nrow(nom.mf) != nrow(newdata))
stop("length of variable(s) found do not match nrow(newdata)")
if (!is.null(cl <- attr(object$nom.terms, "dataClasses")))
.checkMFClasses(cl, nom.mf)
NOM <- model.matrix(object$nom.terms, nom.mf,
contrasts=object$nom.contrasts)
NOMint <- match("(Intercept)", colnames(NOM), nomatch = 0L)
if(NOMint <= 0) NOM <- cbind("(Intercept)" = rep(1, n), NOM)
# if(object$control$sign.nominal == "negative") NOM[, -1] <- -NOM[, -1]
alias <- t(matrix(object$aliased$alpha,
nrow=length(object$y.levels) - 1))[,1]
if(sum(alias) > 0)
NOM <- NOM[, !c(FALSE, alias), drop=FALSE]
}
### make SCALE model.matrix:
if(is.scale <- !is.null(object$S.terms)) {
## allows NAs to pass through to fit, se.fit, lwr and upr:
S.mf <- model.frame(object$S.terms, newdata,
na.action=na.action,
xlev=object$S.xlevels)
## model.frame will warn, but here we also throw an error:
if(nrow(S.mf) != nrow(newdata))
stop("length of variable(s) found do not match nrow(newdata)")
if (!is.null(cl <- attr(object$S.terms, "dataClasses")))
.checkMFClasses(cl, S.mf)
S <- model.matrix(object$S.terms, S.mf,
contrasts=object$S.contrasts)
Sint <- match("(Intercept)", colnames(S), nomatch = 0L)
if(Sint <= 0) S <- cbind("(Intercept)" = rep(1, n), S)
if(sum(object$aliased$zeta) > 0)
S <- S[, !c(FALSE, object$aliased$zeta), drop=FALSE]
Soff <- rep(0, nrow(S))
if(!is.null(off.num <- attr(object$S.terms, "offset")))
for(i in off.num) Soff <- Soff +
eval(attr(object$S.terms, "variables")[[i + 1]], newdata)
}
### Construct model environment:
tJac <- object$tJac
dimnames(tJac) <- NULL
env <- clm.newRho(parent.frame(), y=y, X=X,
NOM=if(is.nom) NOM else NULL,
S=if(is.scale) S else NULL,
weights=rep(1, n), offset=offset,
S.offset=if(is.scale) Soff else rep(0, n),
tJac=tJac, control=object$control)
setLinks(env, link=object$link)
} ## end !missing(newdata) or type == "class"
else {
env <- get_clmRho.clm(object)
## env <- update(object, doFit=FALSE)
}
env$par <- as.vector(coef(object))
env$par <- env$par[!is.na(env$par)]
### OPTION: Are there better ways to handle NAs in coef?
## if(length(env$par) != ncol(env$B1))
## stop(gettextf("design matrix has %d columns, but expecting %d (number of parameters)",
## ncol(env$B1), length(env$par)))
## Get predictions:
pred <-
switch(type,
"prob" = prob.predict.clm(env=env, cov=cov, se.fit=se.fit,
interval=interval, level=level),
"class" = prob.predict.clm(env=env, cov=cov, se.fit=se.fit,
interval=interval, level=level),
"cum.prob" = cum.prob.predict.clm(env=env, cov=cov,
se.fit=se.fit, interval=interval, level=level),
"linear.predictor" = lin.pred.predict.clm(env=env, cov=cov,
se.fit=se.fit, interval=interval, level=level) ##,
## "eta" = eta.pred.predict.clm(env=env, cov=cov,
## se.fit=se.fit, interval=interval, level=level)
)
### Arrange predictions in matrices if response is missing from
### newdata arg or type=="class":
if(!has.response || type == "class") {
pred <- lapply(pred, function(x) {
x <- matrix(unlist(x), ncol=nlev, byrow=TRUE)
dimnames(x) <- list(1:nrow(x), ylev)
x
})
## if(type == "eta")
## pred <- lapply(pred, function(x) {
## x <- x[, -nlev, drop=FALSE]
## colnames(x) <- names(object$alpha)
## })
if(type == "class")
pred <- lapply(pred, function(x) {
factor(max.col(x), levels=seq_along(ylev), labels=ylev) })
}
### Filter missing values (if relevant):
if(missing(newdata) && !is.null(object$na.action))
pred <- lapply(pred, function(x) napredict(object$na.action, x))
return(pred)
}
prob.predict.clm <-
function(env, cov, se.fit=FALSE, interval=FALSE, level=0.95)
### Works for linear and scale models:
### env - model environment with par set.
### cov - vcov for the parameters
{
## evaluate nll and grad to set dpi.psi in env:
clm.nll(env)
pred <- list(fit = as.vector(env$fitted))
if(se.fit || interval) {
se.pr <- get.se(env, cov, type="prob")
if(se.fit)
pred$se.fit <- se.pr
if(interval) {
pred.logit <- qlogis(pred$fit)
## se.logit <- dlogis(pred$fit) * se.pr
se.logit <- se.pr / (pred$fit * (1 - pred$fit))
a <- (1 - level)/2
pred$lwr <- plogis(pred.logit + qnorm(a) * se.logit)
pred$upr <- plogis(pred.logit - qnorm(a) * se.logit)
}
}
return(pred)
}
eta.pred.predict.clm <-
function(env, cov, se.fit=FALSE, interval=FALSE, level=0.95)
{
## clm.nll(env)
pred <- list(eta = c(with(env, B1 %*% par[1:n.psi])))
if(se.fit || interval) {
se <- get.se(env, cov, type="lp")
if(se.fit) {
pred$se.eta <- se[[1]]
}
if(interval) {
a <- (1 - level)/2
pred$lwr1 <- env$eta1 + qnorm(a) * se[[1]]
pred$upr1 <- env$eta1 - qnorm(a) * se[[1]]
}
}
pred
}
lin.pred.predict.clm <-
function(env, cov, se.fit=FALSE, interval=FALSE, level=0.95)
### get predictions on the scale of the linear predictor
{
## evaluate nll and grad to set dpi.psi in env:
clm.nll(env)
pred <- list(eta1=env$eta1, eta2=env$eta2)
if(se.fit || interval) {
se <- get.se(env, cov, type="lp")
if(se.fit) {
pred$se.eta1 <- se[[1]]
pred$se.eta2 <- se[[2]]
}
if(interval) {
a <- (1 - level)/2
pred$lwr1 <- env$eta1 + qnorm(a) * se[[1]]
pred$lwr2 <- env$eta2 + qnorm(a) * se[[2]]
pred$upr1 <- env$eta1 - qnorm(a) * se[[1]]
pred$upr2 <- env$eta2 - qnorm(a) * se[[2]]
}
}
return(pred) ## list with predictions.
}
cum.prob.predict.clm <-
function(env, cov, se.fit=FALSE, interval=FALSE, level=0.95)
{
## evaluate nll and grad to set dpi.psi in env:
clm.nll(env)
pred <- list(cprob1=env$pfun(env$eta1), cprob2=env$pfun(env$eta2))
if(se.fit || interval) {
se <- get.se(env, cov, type="gamma")
if(se.fit) {
pred$se.cprob1 <- se[[1]]
pred$se.cprob2 <- se[[2]]
}
if(interval) {
a <- (1 - level)/2
pred$lwr1 <- pred$cprob1 + qnorm(a) * se[[1]]
pred$lwr2 <- pred$cprob2 + qnorm(a) * se[[2]]
pred$upr1 <- pred$cprob1 - qnorm(a) * se[[1]]
pred$upr2 <- pred$cprob2 - qnorm(a) * se[[2]]
}
}
return(pred)
}
get.se <- function(rho, cov, type=c("lp", "gamma", "prob")) {
### Computes standard errors of predicted probabilities (prob),
### cumulative probabilities (gamma) or values of the linear
### predictor (lp) for linear (k<=0) or location-scale models
### (k>0).
rho$xcovtx <- function(x, chol.cov) {
## Compute 'diag(x %*% cov %*% t(x))'
diag(x %*% crossprod(chol.cov) %*% t(x))
## colSums(tcrossprod(chol.cov, x)^2)
}
rho$type <- match.arg(type)
ind <- seq_len(rho$n.psi + rho$k)
rho$chol.cov <- try(chol(cov[ind, ind]), silent=TRUE)
if(inherits(rho$chol.cov, "try-error"))
stop(gettext("VarCov matrix of model parameters is not positive definite:\n cannot compute standard errors of predictions"),
call.=FALSE)
clm.nll(rho) ## just to be safe
with(rho, {
### First compute d[eta, gamma, prob] / d par; then compute variance
### covariance matrix of the observations and extract SEs as the
### square root of the diagonal elements:
if(type %in% c("lp", "gamma")) {
D1 <- B1
D2 <- B2
if(k > 0) {
D1 <- cbind(D1/sigma, -S*eta1)
D2 <- cbind(D2/sigma, -S*eta2)
}
if(type == "gamma") {
p1 <- if(!nlambda) dfun(eta1) else dfun(eta1, lambda)
p2 <- if(!nlambda) dfun(eta2) else dfun(eta2, lambda)
D1 <- D1*p1
D2 <- D2*p2
}
se <- list(se1=sqrt(xcovtx(D1, chol.cov)),
se2=sqrt(xcovtx(D2, chol.cov)))
}
if(type == "prob") {
p1 <- if(!nlambda) dfun(eta1) else dfun(eta1, lambda)
p2 <- if(!nlambda) dfun(eta2) else dfun(eta2, lambda)
C2 <- if(k <= 0) B1*p1 - B2*p2 else
cbind(B1*p1/sigma - B2*p2/sigma,
-(eta1 * p1 - eta2 * p2) * S)
se <- sqrt(xcovtx(C2, chol.cov))
}
})
rho$se
}
|
