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waldtest <- function(object, ...) {
UseMethod("waldtest")
}
waldtest.formula <- function(object, ..., data = list()) {
object <- if(length(data) < 1) eval(call("lm", formula = as.formula(deparse(substitute(object))),
environment(object)))
else eval(call("lm", formula = as.formula(deparse(substitute(object))),
data = as.name(deparse(substitute(data))), environment(data)))
waldtest.default(object, ...)
}
waldtest.survreg <- function(object, ..., test = c("Chisq", "F"))
waldtest.default(object, ..., test = match.arg(test))
waldtest.default <- function(object, ..., vcov = NULL, test = c("F", "Chisq"), name = NULL)
{
## methods needed:
## - terms()
## - update()
## - formula()
## - residuals() -> only for determining number of observations
## - df.residual()
## - coef() -> needs to be named, matching names in terms() and vcov()
## - vcov(), potentially user-supplied
## avoid name clashes of vcov argument and vcov() function
vcov. <- vcov
## model class
cls <- class(object)[1]
## convenience functions:
## 1. extract number of observations
nobs <- function(x) NROW(residuals(x))
## 2. extracts term labels
tlab <- function(x) attr(terms(x), "term.labels")
## 3. extracts model name
if(is.null(name)) name <- function(x) paste(deparse(formula(x)), collapse="\n")
## 3. compute an updated model object
modelUpdate <- function(fm, update) {
## if `update' is numeric or character, then assume that the
## corresponding variables (i.e., terms) are redundant (i.e., should be omitted)
if(is.numeric(update)) {
## sanity checking of numeric update specification
if(any(update < 1)) {
warning("for numeric model specifications all values have to be >=1")
update <- abs(update)[abs(update) > 0]
}
if(any(update > length(tlab(fm)))) {
warning(paste("more terms specified than existent in the model:",
paste(as.character(update[update > length(tlab(fm))]), collapse = ", ")))
update <- update[update <= length(tlab(fm))]
}
## finally turn numeric into character update specification
update <- tlab(fm)[update]
}
if(is.character(update)) {
## sanity checking of character update specification
if(!all(update %in% tlab(fm))) {
warning(paste("terms specified that are not in the model:",
paste(dQuote(update[!(update %in% tlab(fm))]), collapse = ", ")))
update <- update[update %in% tlab(fm)]
}
if(length(update) < 1) stop("empty model specification")
## finally turn character into formula update specification
update <- as.formula(paste(". ~ . -", paste(update, collapse = " - ")))
}
if(inherits(update, "formula")) update <- update(fm, update)
if(!inherits(update, cls)) stop(paste("original model was of class \"", cls,
"\", updated model is of class \"", class(update)[1], "\"", sep = ""))
return(update)
}
## 4. compare two fitted model objects
modelCompare <- function(fm, fm.up, vfun = NULL) {
q <- length(coef(fm)) - length(coef(fm.up))
if(q > 0) {
fm0 <- fm.up
fm1 <- fm
} else {
fm0 <- fm
fm1 <- fm.up
}
k <- length(coef(fm1))
n <- nobs(fm1)
## determine omitted variables
if(!all(tlab(fm0) %in% tlab(fm1))) stop("models are not nested")
ovar <- which(!(names(coef(fm1)) %in% names(coef(fm0))))
## get covariance matrix estimate
vc <- if(is.null(vfun)) vcov(fm1)
else if(is.function(vfun)) vfun(fm1)
else vfun
## compute Chisq statistic
stat <- t(coef(fm1)[ovar]) %*% solve(vc[ovar,ovar]) %*% coef(fm1)[ovar]
return(c(-q, stat))
}
## recursively fit all objects (if necessary)
objects <- list(object, ...)
nmodels <- length(objects)
if(nmodels < 2) {
objects <- c(objects, . ~ 1)
nmodels <- 2
}
# remember which models are already fitted and which are described
# by an update mechanism
no.update <- sapply(objects, function(obj) inherits(obj, cls))
## updating
for(i in 2:nmodels) objects[[i]] <- modelUpdate(objects[[i-1]], objects[[i]])
## check responses
responses <- as.character(lapply(objects, function(x) deparse(terms(x)[[2]])))
sameresp <- responses == responses[1]
if(!all(sameresp)) {
objects <- objects[sameresp]
warning("models with response ", deparse(responses[!sameresp]),
" removed because response differs from ", "model 1")
}
## check sample sizes
ns <- sapply(objects, nobs)
if(any(ns != ns[1])) {
for(i in 2:nmodels) {
if(ns[1] != ns[i]) {
if(no.update[i]) stop("models were not all fitted to the same size of dataset")
else {
commonobs <- row.names(model.frame(objects[[i]])) %in% row.names(model.frame(objects[[i-1]]))
objects[[i]] <- eval(substitute(update(objects[[i]], subset = commonobs),
list(commonobs = commonobs)))
if(nobs(objects[[i]]) != ns[1]) stop("models could not be fitted to the same size of dataset")
}
}
}
}
## check vcov.
if(nmodels > 2 && !is.null(vcov.) && !is.function(vcov.))
stop("to compare more than 2 models `vcov.' needs to be a function")
## setup ANOVA matrix
test <- match.arg(test)
rval <- matrix(rep(NA, 4 * nmodels), ncol = 4)
colnames(rval) <- c("Res.Df", "Df", test, paste("Pr(>", test, ")", sep = ""))
rownames(rval) <- 1:nmodels
rval[,1] <- as.numeric(sapply(objects, df.residual))
for(i in 2:nmodels) rval[i, 2:3] <- modelCompare(objects[[i-1]], objects[[i]], vfun = vcov.)
if(test == "Chisq") {
rval[,4] <- pchisq(rval[,3], round(abs(rval[,2])), lower.tail = FALSE)
} else {
df <- rval[,1]
for(i in 2:nmodels) if(rval[i,2] < 0) df[i] <- rval[i-1,1]
rval[,3] <- rval[,3]/abs(rval[,2])
rval[,4] <- pf(rval[,3], abs(rval[,2]), df, lower.tail = FALSE)
}
variables <- lapply(objects, name)
title <- "Wald test\n"
topnote <- paste("Model ", format(1:nmodels),": ", variables, sep="", collapse="\n")
structure(as.data.frame(rval), heading = c(title, topnote),
class = c("anova", "data.frame"))
}
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