R/vcov.R

In hojsgaard/gRc: Inference in Graphical Gaussian Models with Edge and Vertex Symmetries

## 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)
}