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# $Id: ConditionalTree.R 630 2017-02-27 14:58:59Z thothorn $
### the fitting procedure
ctreefit <- function(object, controls, weights = NULL, ...) {
if (!extends(class(object), "LearningSample"))
stop(sQuote("object"), " is not of class ", sQuote("LearningSample"))
if (!extends(class(controls), "TreeControl"))
stop(sQuote("controls"), " is not of class ", sQuote("TreeControl"))
# if (is.null(fitmem))
# fitmem <- ctree_memory(object, TRUE)
# if (!extends(class(fitmem), "TreeFitMemory"))
# stop(sQuote("fitmem"), " is not of class ", sQuote("TreeFitMemory"))
if (is.null(weights))
weights <- object@weights
storage.mode(weights) <- "double"
if (length(weights) != object@nobs || storage.mode(weights) != "double")
stop(sQuote("weights"), " are not a double vector of ",
object@nobs, " elements")
if (max(abs(floor(weights) - weights)) > sqrt(.Machine$double.eps))
stop(sQuote("weights"), " contains real valued elements; currently
only integer values are allowed")
### grow the tree
tree <- .Call(R_TreeGrow, object, weights, controls)
where <- tree[[1]]
tree <- tree[[2]]
### create S3 classes and put names on lists
tree <- prettytree(tree, names(object@inputs@variables),
object@inputs@levels)
### prepare the returned object
RET <- new("BinaryTree")
RET@tree <- tree
RET@where <- where
RET@weights <- weights
RET@responses <- object@responses
if (inherits(object, "LearningSampleFormula"))
RET@data <- object@menv
RET@update <- function(weights = NULL) {
ctreefit(object = object, controls = controls,
weights = weights, ...)
}
### get terminal node numbers
RET@get_where <- function(newdata = NULL, mincriterion = 0, ...) {
if (is.null(newdata) && mincriterion == 0) {
if (all(where > 0)) return(where)
}
newinp <- newinputs(object, newdata)
.R_get_nodeID(tree, newinp, mincriterion)
}
### (estimated) conditional distribution of the response given the
### covariates
RET@cond_distr_response <- function(newdata = NULL, mincriterion = 0, ...) {
wh <- RET@get_where(newdata = newdata, mincriterion = mincriterion)
response <- object@responses
### survival: estimated Kaplan-Meier
if (any(response@is_censored)) {
swh <- sort(unique(wh))
# w <- .Call(R_getweights, tree, swh)
RET <- vector(mode = "list", length = length(wh))
resp <- response@variables[[1]]
for (i in 1:length(swh)) {
w <- weights * (where == swh[i])
RET[wh == swh[i]] <- list(mysurvfit(resp, weights = w))
}
return(RET)
}
### classification: estimated class probabilities
### regression: the means, not really a distribution
RET <- .Call(R_getpredictions, tree, wh)
return(RET)
}
### predict in the response space, always!
RET@predict_response <- function(newdata = NULL, mincriterion = 0,
type = c("response", "node", "prob"), ...) {
type <- match.arg(type)
if (type == "node")
return(RET@get_where(newdata = newdata,
mincriterion = mincriterion, ...))
cdresp <- RET@cond_distr_response(newdata = newdata,
mincriterion = mincriterion, ...)
if (type == "prob")
return(cdresp)
### <FIXME> multivariate responses, we might want to return
### a data.frame
### </FIXME>
if (object@responses@ninputs > 1)
return(cdresp)
response <- object@responses
### classification: classes
if (all(response@is_nominal || response@is_ordinal)) {
lev <- levels(response@variables[[1]])
RET <- factor(lev[unlist(lapply(cdresp, which.max))],
levels = levels(response@variables[[1]]))
return(RET)
}
### survival: median survival time
if (any(response@is_censored)) {
RET <- sapply(cdresp, mst)
return(RET)
}
### regression: mean (median would be possible)
RET <- unlist(cdresp)
RET <- matrix(unlist(RET),
nrow = length(RET), byrow = TRUE)
### <FIXME> what about multivariate responses?
if (response@ninputs == 1)
colnames(RET) <- names(response@variables)
### </FIXME>
return(RET)
}
RET@prediction_weights <- function(newdata = NULL,
mincriterion = 0, ...) {
wh <- RET@get_where(newdata = newdata, mincriterion = mincriterion)
swh <- sort(unique(wh))
# w <- .Call(R_getweights, tree, swh)
RET <- vector(mode = "list", length = length(wh))
for (i in 1:length(swh))
RET[wh == swh[i]] <- list(weights * (where == swh[i]))
return(RET)
}
return(RET)
}
### data pre-processing (ordering, computing transformations etc)
ctreedpp <- function(formula, data = list(), subset = NULL,
na.action = NULL, xtrafo = ptrafo, ytrafo = ptrafo,
scores = NULL, ...) {
dat <- ModelEnvFormula(formula = formula, data = data,
subset = subset, designMatrix = FALSE,
responseMatrix = FALSE, ...)
inp <- initVariableFrame(dat@get("input"), trafo = xtrafo,
scores = scores)
response <- dat@get("response")
if (any(is.na(response)))
stop("missing values in response variable not allowed")
resp <- initVariableFrame(response, trafo = ytrafo, response = TRUE,
scores = scores)
RET <- new("LearningSampleFormula", inputs = inp, responses = resp,
weights = rep(1, inp@nobs), nobs = inp@nobs,
ninputs = inp@ninputs, menv = dat)
RET
}
### the unfitted conditional tree, an object of class `StatModel'
### see package `modeltools'
conditionalTree <- new("StatModel",
capabilities = new("StatModelCapabilities"),
name = "unbiased conditional recursive partitioning",
dpp = ctreedpp,
fit = ctreefit,
predict = function(object, ...)
object@predict_response(...) )
### we need a `fit' method for data = LearningSample
setMethod("fit", signature = signature(model = "StatModel",
data = "LearningSample"),
definition = function(model, data, ...)
model@fit(data, ...)
)
### control the hyper parameters
ctree_control <- function(teststat = c("quad", "max"),
testtype = c("Bonferroni", "MonteCarlo", "Univariate", "Teststatistic"),
mincriterion = 0.95, minsplit = 20, minbucket = 7, stump = FALSE,
nresample = 9999, maxsurrogate = 0, mtry = 0,
savesplitstats = TRUE, maxdepth = 0, remove_weights = FALSE) {
teststat <- match.arg(teststat)
testtype <- match.arg(testtype)
RET <- new("TreeControl")
if (teststat %in% levels(RET@varctrl@teststat)) {
RET@varctrl@teststat <- factor(teststat,
levels = levels(RET@varctrl@teststat))
} else {
stop(sQuote("teststat"), teststat, " not defined")
}
if (testtype %in% levels(RET@gtctrl@testtype))
RET@gtctrl@testtype <- factor(testtype,
levels = levels(RET@gtctrl@testtype))
else
stop(testtype, " not defined")
if (RET@gtctrl@testtype == "Teststatistic")
RET@varctrl@pvalue <- as.logical(FALSE)
RET@gtctrl@nresample <- as.integer(nresample)
RET@gtctrl@mincriterion <- as.double(mincriterion)
if (all(mtry > 0)) {
RET@gtctrl@randomsplits <- as.logical(TRUE)
RET@gtctrl@mtry <- as.integer(mtry)
}
RET@tgctrl@savesplitstats <- as.logical(savesplitstats)
RET@splitctrl@minsplit <- as.double(minsplit)
RET@splitctrl@maxsurrogate <- as.integer(maxsurrogate)
RET@splitctrl@minbucket <- as.double(minbucket)
RET@tgctrl@stump <- as.logical(stump)
RET@tgctrl@maxdepth <- as.integer(maxdepth)
RET@tgctrl@savesplitstats <- as.logical(savesplitstats)
RET@tgctrl@remove_weights <- as.logical(remove_weights)
if (!validObject(RET))
stop("RET is not a valid object of class", class(RET))
RET
}
### the top-level convenience function
ctree <- function(formula, data = list(), subset = NULL, weights = NULL,
controls = ctree_control(), xtrafo = ptrafo,
ytrafo = ptrafo, scores = NULL) {
### setup learning sample
ls <- dpp(conditionalTree, formula, data, subset, xtrafo = xtrafo,
ytrafo = ytrafo, scores = scores)
# ### setup memory
# fitmem <- ctree_memory(ls, TRUE)
### fit and return a conditional tree
fit(conditionalTree, ls, controls = controls, weights = weights)
}
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