partykit-master/R/utils.R

In tulliapadellini/energytree: Conditional Inference trees for mixed type data

### length(x) == 1 will lead to sample.int instead of sample;
### see example(sample)
.resample <- function(x, ...) x[sample.int(length(x), ...)]   

.median_survival_time <- function(x) {
    minmin <- function(y, xx) {
        if (any(!is.na(y) & y==.5)) {
            if (any(!is.na(y) & y <.5))
                .5*(min(xx[!is.na(y) & y==.5]) + min(xx[!is.na(y) & y<.5]))
            else
                .5*(min(xx[!is.na(y) & y==.5]) + max(xx[!is.na(y) & y==.5]))
        } else   min(xx[!is.na(y) & y<=.5])
    }
    med <- suppressWarnings(minmin(x$surv, x$time))
    return(med)
}

get_paths <- function(obj, i) {

    id0 <- nodeids(obj)
    if (inherits(obj, "party")) obj <- node_party(obj)
    if (!inherits(obj, "partynode"))
        stop(sQuote("obj"), " is not an object of class partynode")

    i <- as.integer(i)
    if (!all(i %in% id0))
        stop(sQuote("i"), " does not match node identifiers of ", 
             sQuote("obj"))

    lapply(i, function(id) {
        if (id == 1L) return(1L)
        .get_path(obj, id)
    })
}
    
### get the recursive index
### obj is of class "partynode"
.get_path <- function(obj, i) {

    idx <- c()
    recFun <- function(node, i) {
        if (id_node(node) == i) return(NULL)
        idx <<- c(idx, which(names(unclass(node)) == "kids"))
        kid <- sapply(kids_node(node), id_node)
        nextid <- max(which(kid <= i))
        idx <<- c(idx, nextid)
        return(recFun(node[[nextid]], i))
    }
    out <- recFun(obj, i)
    return(idx)
}

### <TH> shall we export this functionality?
"nodeids<-" <- function(obj, value) UseMethod("nodeids<-")

"nodeids<-.party" <- function(obj, value) {

  id0 <- nodeids(obj)
  id1 <- as.integer(value)
  stopifnot(identical(id1, 1:length(id0)))

  idxs <- lapply(id0, .get_path, obj = node_party(obj))
  x <- unclass(obj)
  ni <- which(names(x) == "node")
  nm <- x$names
  for (i in 1:length(idxs))
      x[[c(ni, idxs[[i]])]]$id <- id1[i]
  class(x) <- class(obj)
  if (!is.null(nm)) 
      names(x) <- nm[id0]

  return(x)
}

"nodeids<-.constparty" <- function(obj, value) {

  id0 <- nodeids(obj)
  cls <- class(obj)
  class(obj) <- "party"
  nodeids(obj) <- value
  id1 <- nodeids(obj)
  obj$fitted[["(fitted)"]] <- 
      id1[match(fitted(obj)[["(fitted)"]], id0)]
  class(obj) <- cls
  obj
}
### </TH>

## determine all possible splits for a factor, both nominal and ordinal
.mob_grow_getlevels <- function(z) {
  nl <- nlevels(z)
  if(inherits(z, "ordered")) {
    indx <- diag(nl)
    indx[lower.tri(indx)] <- 1
    indx <- indx[-nl, , drop = FALSE]
    rownames(indx) <- levels(z)[-nl]
  } else {
    mi <- 2^(nl - 1L) - 1L
    indx <- matrix(0, nrow = mi, ncol = nl)
    for (i in 1L:mi) {
      ii <- i
      for (l in 1L:nl) {
        indx[i, l] <- ii %% 2L
        ii <- ii %/% 2L   
      }
    }
    rownames(indx) <- apply(indx, 1L, function(x) paste(levels(z)[x > 0], collapse = "+"))
  }
  colnames(indx) <- as.character(levels(z))
  storage.mode(indx) <- "logical"
  indx
}

.rfweights <- function(fdata, fnewdata, rw, scale)
    w <- .Call(R_rfweights, fdata, fnewdata, rw, scale)