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```
#' Compute model-based forest from model.
#'
#' Input a parametric model and get a forest.
#'
#' @param model a model object. The model can be a parametric model with a single binary covariate.
#' @param data data. If \code{NULL} the data from the model object are used.
#' @param zformula formula describing which variable should be used for partitioning.
#' Default is to use all variables in data that are not in the model (i.e. \code{~ .}).
#' @param ntree number of trees.
#' @param perturb a list with arguments replace and fraction determining which type of
#' resampling with \code{replace = TRUE} referring to the n-out-of-n bootstrap and
#' \code{replace = FALSE} to sample splitting. fraction is the number of observations
#' to draw without replacement.
#' @param mtry number of input variables randomly sampled as candidates at each
#' node (Default \code{NULL} corresponds to \code{ceiling(sqrt(nvar))}).
#' Bagging, as special case of a random forest without random input variable
#' sampling, can be performed by setting mtry either equal to Inf or
#' equal to the number of input variables.
#' @param applyfun see \code{\link[partykit]{cforest}}.
#' @param cores see \code{\link[partykit]{cforest}}.
#' @param control control parameters, see \code{\link[partykit]{ctree_control}}.
#' @param trace a logical indicating if a progress bar shall be printed while
#' the forest grows.
#' @param ... additional parameters passed on to model fit such as weights.
#'
#' @example inst/examples/ex-pmodel.R
#'
#' @return cforest object
#'
#' @export
#' @importFrom partykit ctree_control
pmforest <- function(model, data = NULL, zformula = ~., ntree = 500L,
perturb = list(replace = FALSE, fraction = 0.632),
mtry = NULL, applyfun = NULL, cores = NULL,
control = ctree_control(teststat = "quad", testtype = "Univ",
mincriterion = 0, saveinfo = FALSE,
lookahead = TRUE, ...),
trace = FALSE, ...) {
### nmax not possible because data come from model
stopifnot(all(!is.finite(control$nmax)))
cl <- match.call()
args <- .prepare_args(model = model, data = data, zformula = zformula,
control = control, ntree = ntree, perturb = perturb,
applyfun = applyfun, cores = cores,
trace = trace)
## call cforest
args$ytrafo <- function(data, weights, control, ...)
.modelfit(data = data, weights = weights, control = control, model = model, ...)
args$mtry <- mtry
ret <- do.call("cforest", args)
ret$info$model <- model
ret$info$zformula <- zformula
ret$info$call <- cl
class(ret) <- c("pmforest", class(ret))
return(ret)
}
#' @rdname pmforest
#'
#' @param object an object returned by pmforest.
#' @param tree an integer, the number of the tree to extract from the forest.
#' @param saveinfo logical. Should the model info be stored in terminal nodes?
#' @param coeffun function that takes the model object and returns the coefficients.
#' Useful when coef() does not return all coefficients (e.g. survreg).
#'
#' @seealso \code{\link[partykit]{gettree}}
#' @export
gettree.pmforest <- function(object, tree = 1L, saveinfo = TRUE, coeffun = coef, ...) {
ret <- gettree.cforest(object = object, tree = tree, ...)
cl <- class(ret)
if(saveinfo) {
which_terminals <- nodeids(ret, terminal = TRUE)
ret <- .add_modelinfo(ret, nodeids = which_terminals, data = object$data,
model = object$info$model, coeffun = coeffun)
}
class(ret) <- c("pmtree", cl)
return(ret)
}
```

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