## calculateVCOV <- function(m, K, vcov="boot", nboot=250){
## vn <- unlist(lapply(getcc(m), names))
## switch(vcov,
## "boot"={
## #cat(".bootstrapVCOV", nboot, "\n")
## V <- .bootstrapVCOV(m, K, nboot)
## },
## "inf" ={
## #cat("scoreVar", nboot, "\n")
## V <- solve(getScore(m,K=K)$J)
## }
## )
## dimnames(V) <- list(vn,vn)
## V
## }
## calculateVCOV <- function(m, K, vcov="boot", nboot=250){
## vn <- unlist(lapply(getcc(m),names))
## V <- solve(getScore(m, K=K)$J)
## # switch(vcov,
## # "boot"={
## # #cat(".bootstrapVCOV", nboot, "\n")
## # V <- .bootstrapVCOV(m,K,nboot)
## # },
## # "inf" ={
## # #cat("scoreVar", nboot, "\n")
## # V <- solve(getScore(m,K=K)$J)
## # }
## # )
## dimnames(V) <- list(vn,vn)
## V
## }
## .bootstrapVCOV <- function(m, K, nboot=250){
## ##cat("bootstrapVar", nboot, "\n")
## res <- matrix(NA, ncol=length(c(getSlot(m,"vcc"),getSlot(m,"ecc"))), nrow=nboot)
## Sf <- solve(K)
## n <- dataRep(m, "n")
## for(i in 1:nboot){
## d<- mvrnorm(n, mu=rep(0, nrow(Sf)),
## Sigma=Sf, tol = 1e-6, empirical = FALSE)
## m$dataRep$S <- cov(d)
## if (class(m)[1]=="rcon")
## th <- rconScoreTheta(m)
## else
## th <- rcorScoreTheta(m)
## res[i,] <- th
## }
## V <- cov(res)
## }
#' @export
vcov.rcox <- function(object, ...){
f1 <- fitInfo(object)
solve(f1$J)
}
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