Nothing
multistate <- function(Ri = Ri, Ci = Ci){
## #########################################################
## negative log likelihood of a cox type multistate model ##
## a.k.a. partial likelihood loss function ##
## #########################################################
plloss <- function(y, f, w) {
event <- y[, 3]
n <- length(event)
if (length(f) == 1) {f <- rep(f, n)}
ef <- exp(f)
risk <- do.call(c, lapply(X = Ri, FUN = helpfunctionmultistate1, ef = ef))
lpl <- sum(event * (f - log(risk)))
return(lpl)}
## #################################
## construct new boosting family ##
## #################################
Family(
## #########################################
## negative gradient of the loss function ##
## #########################################
ngradient = function(y, f, w) {
event <- y[, 3]
n <- length(event)
if(length(f) == 1){f <- rep(f, n)}
ef <- exp(f)
dummy <- secondpart <- rep(0, n)
dummy <- do.call(c, lapply(X = Ri, FUN = helpfunctionmultistate1, ef = ef))
dummy[which(dummy==0)] <- 1e-05
secondpart <- do.call(c, lapply(X = Ci, FUN = helpfunctionmultistate2, dummy = dummy))
gradients <- event - (ef*secondpart)
return(gradients)},
## ################
## risk function ##
## ################
risk = risk <- function(y, f, w) -sum(plloss(y, f, w)),
## ##################
## offset function ##
## ##################
offset = function(y, w) 0,
## ################
## loss function ##
## ################
#loss = loss <- function(y, f) {
# event <- y[, 3]
# n <- length(event)
# if (length(f) == 1) {f <- rep(f, n)}
# ef <- exp(f)
# risk <- rep(0, n)
# for (i in 1:n) {
# risk[i] <- sum(ef[riskset[[i]]])
# }
# lpl <- sum(event * (f - log(risk)))
# return(-mean(lpl))},
name = "family for multistate models (2014-10-21).")}
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