mirror of
https://github.com/prise6/aVirtualTwins.git
synced 2024-05-07 20:36:32 +02:00
231 lines
7.9 KiB
R
231 lines
7.9 KiB
R
# TREES COMPUTATIONS ------------------------------------------------------
|
|
#' An abstract reference class to compute tree
|
|
#'
|
|
#' @include difft.R setClass.R
|
|
#'
|
|
#' @field vt.difft VT.difft object
|
|
#' @field outcome vector
|
|
#' @field threshold numeric Threshold for difft (c)
|
|
#' @field screening logical TRUE if using varimp (default is VT.object screening field)
|
|
#' @field sens character Sens can be ">" (default) or "<". Meaning : difft > threshold or difft < threshold
|
|
#' @field name character Names of the tree
|
|
#' @field tree rpart Rpart object to construct the tree
|
|
#' @field Ahat vector Indicator of beglonging to Ahat
|
|
#'
|
|
VT.tree <- setRefClass(
|
|
Class = "VT.tree",
|
|
|
|
fields = list(
|
|
vt.difft = "VT.difft",
|
|
outcome = "vector",
|
|
threshold = "numeric",
|
|
screening = "logical",
|
|
sens = "character",
|
|
name = "character",
|
|
tree = "rpart",
|
|
Ahat = "vector"
|
|
),
|
|
|
|
methods = list(
|
|
initialize = function(vt.difft = VT.difft(), threshold = 0.05, sens = ">", screening = NULL){
|
|
.self$vt.difft <- vt.difft
|
|
|
|
.self$threshold <- threshold
|
|
|
|
.self$sens <- sens
|
|
|
|
.self$screening <- ifelse(is.null(screening), vt.difft$vt.object$screening, screening)
|
|
|
|
},
|
|
|
|
getData = function(){
|
|
d <- .self$vt.difft$vt.object$data[, 3:ncol(.self$vt.difft$vt.object$data)]
|
|
|
|
if(.self$screening == T){
|
|
d.tmp <- d
|
|
d <- d.tmp[, colnames(d.tmp) %in% .self$vt.difft$vt.object$varimp] # To see later
|
|
}
|
|
|
|
d <- data.frame(.self$outcome, d)
|
|
names(d) <- c(.self$name, colnames(d)[-1])
|
|
|
|
return(d)
|
|
},
|
|
|
|
computeNameOfTree = function(type){
|
|
if(.self$threshold < 0 ){
|
|
threshold.chr <- paste0("m", -.self$threshold)
|
|
}else{
|
|
threshold.chr <- as.character(.self$threshold)
|
|
}
|
|
|
|
tmp = strsplit(threshold.chr, "[.]")[[1]]
|
|
return(paste(type, tmp[1], tmp[2], sep = ""))
|
|
},
|
|
|
|
run = function(){
|
|
if(length(.self$vt.difft$difft) == 0) stop("VT.difft::difft is an empty vector")
|
|
},
|
|
|
|
getInfos = function(){
|
|
cat("\n")
|
|
cat(sprintf("Threshold = %0.4f", .self$threshold))
|
|
cat("\n")
|
|
cat(sprintf("Delta = %0.4f", .self$vt.difft$vt.object$delta))
|
|
cat("\n")
|
|
cat(sprintf("Sens : %s", .self$sens))
|
|
cat("\n")
|
|
# cat(sprintf("Bounds = %0.4f", (.self$vt.difft$vt.object$delta + .self$threshold)))
|
|
# cat("\n")
|
|
cat(sprintf("Size of Ahat : %i", (sum(.self$Ahat))))
|
|
|
|
return(invisible(NULL))
|
|
},
|
|
|
|
getRules = function(only.leaf = F, only.fav = F, tables = T, verbose = T){
|
|
|
|
# On supprime le root node, inutile pour les stats d'incidences et autres...
|
|
full.frame <- .self$tree$frame[-1, ]
|
|
|
|
if (only.fav == T){
|
|
if(inherits(.self, "VT.tree.reg")){
|
|
if(.self$sens == ">"){
|
|
frm.only.fav <- full.frame[full.frame$yval >= (.self$threshold), ]
|
|
} else {
|
|
frm.only.fav <- full.frame[full.frame$yval <= (.self$threshold), ]
|
|
}
|
|
}else if(inherits(.self, "VT.tree.class")){
|
|
frm.only.fav <- full.frame[full.frame$yval == 2, ]
|
|
}
|
|
frm <- frm.only.fav
|
|
}
|
|
|
|
if (only.leaf == T){
|
|
if(inherits(.self, "VT.tree.reg")){
|
|
frm.only.leaf <- full.frame[full.frame$var == "<leaf>", ]
|
|
}else if(inherits(.self, "VT.tree.class")){
|
|
frm.only.leaf <- full.frame[full.frame$var == "<leaf>", ]
|
|
}
|
|
frm <- frm.only.leaf
|
|
}
|
|
|
|
if (only.fav == T & only.leaf == T){
|
|
frm <- frm.only.leaf[ intersect(rownames(frm.only.leaf), rownames(frm.only.fav)) , ]
|
|
}else if (only.fav == F & only.leaf == F){
|
|
frm <- full.frame
|
|
}
|
|
|
|
# Le cas où l'arbre est vide ou n'existe pas:
|
|
if (length(frm) == 0) stop("VT.tree : no tree");
|
|
if (ncol(frm)==0) stop("VT.tree : no rules");
|
|
|
|
pth <- rpart::path.rpart(.self$tree, nodes = row.names(frm), print.it = F)
|
|
# Delete 'root' node des règles
|
|
pth <- lapply(pth, FUN = function(d) return(d[-1]))
|
|
|
|
depth <- 0
|
|
nodes <- names(pth)
|
|
rules <- data.frame(replicate(6, character(0), simplify = T), replicate(2, numeric(0), simplify = T), stringsAsFactors = F)
|
|
|
|
colnames(rules) <- c("Subgroup", "Subgroup size", "Treatement event rate", "Control event rate",
|
|
"Treatment sample size", "Control sample size", "RR (resub)", "RR (snd)")
|
|
for(i in nodes){
|
|
pth.text <- paste(pth[[i]], collapse = " & ")
|
|
incid <- .self$getIncidences(pth.text)
|
|
|
|
rules[i, 1] <- pth.text
|
|
rules[i, 2] <- incid$table.selected$table[3, 3] #size subgroupg
|
|
rules[i, 3] <- incid$table.selected$table[4, 2] #treatment event rate
|
|
rules[i, 4] <- incid$table.selected$table[4, 1] #control event rate
|
|
rules[i, 5] <- incid$table.selected$table[3, 2] #treatment sample size
|
|
rules[i, 6] <- incid$table.selected$table[3, 1] #control sample size
|
|
rules[i, 7] <- round(incid$table.selected$rr, digits = 3) # rr (resub)
|
|
rules[i, 8] <- round(incid$table.selected$rr.snd, digits = 3) # rr (snd)
|
|
|
|
if(isTRUE(verbose)){
|
|
cat("----------------------------\n")
|
|
cat(sprintf("| Rule number %s : ", i))
|
|
|
|
|
|
if(inherits(.self, "VT.tree.reg")){
|
|
cat(sprintf("Y val = %0.3f \n", frm[i, ]$yval))
|
|
}else{
|
|
cat(sprintf("Y val = %i \n", frm[i, ]$yval))
|
|
}
|
|
|
|
cat("----------------------------\n")
|
|
|
|
cat(sprintf("[n = %i", frm[i, ]$n))
|
|
cat(sprintf(", loss = %s, prob = %0.2f",
|
|
frm[i, ]$dev,
|
|
frm[i, ]$yval2[, 5]))
|
|
|
|
cat("] \n")
|
|
cat("\t\t")
|
|
cat(pth[[i]], sep="\n\t\t")
|
|
|
|
if(isTRUE(tables)){
|
|
cat("\n")
|
|
cat(sprintf("Incidence dans la selection \n"))
|
|
print(incid$table.selected$table)
|
|
cat("\n")
|
|
cat(sprintf("Risque relatif (resub) : %0.3f \n", incid$table.selected$rr))
|
|
cat(sprintf("Risque relatif (snd) : %0.3f \n\n", incid$table.selected$rr.snd))
|
|
|
|
cat(sprintf("Incidence dans le complementaire\n"))
|
|
print(incid$table.not.selected$table)
|
|
cat("\n")
|
|
cat(sprintf("Risque relatif (resub) : %0.3f \n", incid$table.not.selected$rr))
|
|
cat(sprintf("Risque relatif (snd) : %0.3f \n\n", incid$table.not.selected$rr.snd))
|
|
}
|
|
|
|
cat("\n\n")
|
|
}
|
|
}
|
|
|
|
return(invisible(rules))
|
|
},
|
|
|
|
getIncidences = function(rule, rr.snd = T){
|
|
return(VT.incidences(.self$vt.difft, rule, rr.snd))
|
|
},
|
|
|
|
getAhatIncidence = function(){
|
|
if(sum(.self$Ahat)!=0){
|
|
|
|
table.inc <- VT.incidences(vt.object = .self$vt.difft$vt.object, select = .self$Ahat)
|
|
|
|
table.A <- table.inc$table.selected
|
|
table.A.cmpl <- table.inc$table.not.selected
|
|
|
|
cat(sprintf("Incidence dans le sous groupe A\n"))
|
|
print(table.A$table)
|
|
cat("\n")
|
|
cat(sprintf("Risque relatif : %0.3f \n\n", table.A$risque_relatif))
|
|
|
|
cat(sprintf("Incidence dans le sous groupe A complementaire\n"))
|
|
print(table.A.cmpl$table)
|
|
cat("\n")
|
|
cat(sprintf("Risque relatif : %0.3f \n\n", table.A.cmpl$risque_relatif))
|
|
}else{
|
|
return("Empty set")
|
|
}
|
|
},
|
|
|
|
getAhatQuality = function(){
|
|
|
|
resub <- vt.getQAOriginal(.self$Ahat, response = .self$vt.difft$vt.object$getY(), trt = .self$vt.difft$vt.object$data[, 2])
|
|
|
|
snd <- vt.getQAOriginal(.self$Ahat, response = .self$vt.difft$twin1, trt = .self$vt.difft$vt.object$data[, 2])
|
|
|
|
# on ajoute la taille de Ahat
|
|
size <- sum(.self$Ahat)
|
|
|
|
res <- cbind(size, resub, snd)
|
|
names(res) <- c("size", "resub", "snd")
|
|
|
|
return(res)
|
|
}
|
|
)
|
|
)
|