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# type.R: plotmo functions for getting the default type arg for predict() and residuals()
# this is used when plotmo's argument "type" is NULL (the default)
# get the default type for predict()
plotmo.type <- function(object, ..., TRACE)
{
UseMethod("plotmo.type")
}
plotmo.type.default <- function(object, ..., TRACE)
{
"response"
}
plotmo.type.nnet <- function(object, ..., TRACE)
{
"raw"
}
plotmo.type.knn3 <- function(object, ..., TRACE)
{
"prob"
}
plotmo.type.tree <- function(object, ..., TRACE) # tree package
{
"vector"
}
plotmo.type.fda <- function(object, ..., TRACE) # mda package
{
"class"
}
# get the type for residuals()
plotmo.residtype <- function(object, ..., TRACE)
{
UseMethod("plotmo.residtype")
}
plotmo.residtype.default <- function(object, ..., TRACE)
{
plotmo.type(object, ..., TRACE=TRACE) # use the predict type
}
# TRUE if we are predicting probabilities.
# This is used for setting the default ylim to c(0,1).
# Not always reliable (but if wrong, the user can override with explicit ylim arg).
# It can save a call get.ylim.by.dummy.plots, and also works for objects
# for which get.ylim.by.dummy.plots doesn't automatically figure out c(0,1)
is.yaxis.a.probability.aux <- function(object, type, trace)
{
if(inherits(object, "WrappedModel")) { # mlr package
# will be we be predicting probabilities?
# TODO this will be wrong if use say nresponse="response" in call to plotmo
call <- object[["call"]]
if(!is.null(call)) {
# TODO assumes environment for learner is available and correct
learner <- eval(call[["learner"]])
if(!is.null(learner)) {
predict.type <- mlr::getLearnerPredictType(learner)
if(substr(predict.type[1], 1, 1) == "p") # prob
return(TRUE)
}
}
# continue processing, but use the learner.model
object <- object$learner.model
}
type.firstchar <- substr(type[1], 1, 1) # type argument to predict()
substr(type[1], 1, 4) == "prob" || # catchall
(inherits(object, "rpart") &&
object$method[1] == "class" &&
type.firstchar == "p") ||
# following not strictly necessary for earth models because
# get.ylim.by.dummy.plots can also figure this out
# The "r" below is for "response"
(inherits(object, "earth") &&
is.nomial(object$glm.list[[1]]) && type.firstchar == "r") ||
# the "r" below is for "response"
(inherits(object, c("glm", "glmnet", "pre")) &&
is.nomial(object) && type.firstchar == "r") ||
(inherits(object, "cv.glmnet") &&
!is.null(object$glmnet.fit$classnames)) ||
(inherits(object, "randomForest") &&
is.character(object$type) &&
object$type[1] == "classification" &&
type.firstchar == "p") ||
(inherits(object, "C5.0") &&
type.firstchar == "p")
}
is.yaxis.a.probability <- function(object, type, trace)
{
# This wrapper exists because we don't want plotmo to completely stop
# (issue an error) if a package changes the model fields. (This function is
# vulnerable to changes because it accessess internal fields in multiple
# different models.)
is.prob <- try(is.yaxis.a.probability.aux(object, type, trace), silent=trace < 2)
if(is.try.err(is.prob))
FALSE
else
is.prob
}
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