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## conditional logistic regression
##
## case ~ exposure + strata(matching)
##
clogit <- function(formula, data, weights, subset, na.action,
method=c("exact","approximate", "efron", "breslow"),
... ) {
Call <- match.call() # how we were called
# Create a call to model.frame() that contains the formula (required)
# and the data argument (if present).
# It's only job is to find out the number of rows in the data
# before subset or na.action are applied.
indx <- match(c("formula", "data"), names(Call), nomatch=0)
if (indx[1]==0) stop("A formula argument is required")
mf <- Call[c(1,indx)]
mf[[1L]] <- quote(stats::model.frame)
mf$na.action <- "na.pass"
nrows<-NROW(eval(mf, parent.frame()))
# Now build a call to coxph with the formula fixed up to have
# our special left hand side.
coxcall <- Call
coxcall[[1]] <- as.name("coxph")
newformula <- formula
newformula[[2]] <- substitute(Surv(rep(1,nn),case),
list(case=formula[[2]],nn=nrows))
environment(newformula) <- environment(formula)
coxcall$formula<-newformula
# Set the method, with "approximate" matched to "breslow"
method <- match.arg(method)
coxcall$method <- switch(method, exact="exact",
efron="efron",
"breslow")
# If the method is "exact", then case weights nor robust variance are
# possible
if (method =="exact") {
if (missing(data)) temp <- terms(formula, specials='cluster')
else temp <- terms(formula, specials="cluster", data=data)
if (!is.null(attr(temp, 'specials')$cluster) && method=="exact")
stop("robust variance plus the exact method is not supported")
if (!is.null(coxcall$weights)) {
coxcall$weights <- NULL
warning("weights ignored: not possible for the exact method")
}
}
coxcall<-eval(coxcall, sys.frame(sys.parent()))
coxcall$userCall<-sys.call()
class(coxcall)<-c("clogit","coxph")
coxcall
}
print.clogit <- function(x,...){
x$call<-x$userCall
NextMethod()
}
survfit.clogit <- function(formula, ...)
stop("predicted survival curves are not defined for a clogit model")
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