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+++ b/partyMod/man/plot.mob.Rd
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+\name{plot.mob}
+\alias{plot.mob}
+
+\title{ Visualization of MOB Trees }
+
+\description{
+  \code{plot} method for \code{mob} objects with
+  extended facilities for plugging in panel functions.
+}
+
+\usage{
+\method{plot}{mob}(x, terminal_panel = node_bivplot, tnex = NULL, \dots)
+}
+
+\arguments{
+  \item{x}{an object of class \code{mob}.}
+  \item{terminal_panel}{a panel function or panel-generating function of
+    class \code{"grapcon_generator"}. See \code{\link{plot.BinaryTree}} for
+    more details.}
+  \item{tnex}{a numeric value giving the terminal node extension in relation
+    to the inner nodes.}
+  \item{\dots}{ further arguments passed to \code{\link{plot.BinaryTree}}.}
+}
+
+\details{
+  This \code{plot} method for \code{mob} objects simply calls the
+  \code{\link{plot.BinaryTree}} method, setting a different \code{terminal_panel}
+  function by default (\code{\link{node_bivplot}}) and \code{tnex} value.
+}
+
+
+\seealso{\code{\link{node_bivplot}}, \code{\link{node_scatterplot}},
+  \code{\link{plot.BinaryTree}}, \code{\link{mob}}}
+
+\examples{
+
+set.seed(290875)
+
+if(require("mlbench")) {
+
+## recursive partitioning of a linear regression model
+## load data
+data("BostonHousing", package = "mlbench")
+## and transform variables appropriately (for a linear regression)
+BostonHousing$lstat <- log(BostonHousing$lstat)
+BostonHousing$rm <- BostonHousing$rm^2
+## as well as partitioning variables (for fluctuation testing)
+BostonHousing$chas <- factor(BostonHousing$chas, levels = 0:1, 
+                             labels = c("no", "yes"))
+BostonHousing$rad <- factor(BostonHousing$rad, ordered = TRUE)
+
+## partition the linear regression model medv ~ lstat + rm
+## with respect to all remaining variables:
+fm <- mob(medv ~ lstat + rm | zn + indus + chas + nox + age + dis + 
+                              rad + tax + crim + b + ptratio,
+  control = mob_control(minsplit = 40), data = BostonHousing, 
+  model = linearModel)
+
+## visualize medv ~ lstat and medv ~ rm
+plot(fm)
+
+## visualize only one of the two regressors
+plot(fm, tp_args = list(which = "lstat"), tnex = 2)
+plot(fm, tp_args = list(which = 2), tnex = 2)
+
+## omit fitted mean lines
+plot(fm, tp_args = list(fitmean = FALSE))
+
+## mixed numerical and categorical regressors 
+fm2 <- mob(medv ~ lstat + rm + chas | zn + indus + nox + age + 
+                                      dis + rad,
+  control = mob_control(minsplit = 100), data = BostonHousing, 
+  model = linearModel)
+plot(fm2)
+
+## recursive partitioning of a logistic regression model
+data("PimaIndiansDiabetes", package = "mlbench")
+fmPID <- mob(diabetes ~ glucose | pregnant + pressure + triceps + 
+                                  insulin + mass + pedigree + age,
+  data = PimaIndiansDiabetes, model = glinearModel, 
+  family = binomial())
+## default plot: spinograms with breaks from five point summary
+plot(fmPID)
+## use the breaks from hist() instead
+plot(fmPID, tp_args = list(fivenum = FALSE))
+## user-defined breaks
+plot(fmPID, tp_args = list(breaks = 0:4 * 50))
+## CD plots instead of spinograms
+plot(fmPID, tp_args = list(cdplot = TRUE))
+## different smoothing bandwidth
+plot(fmPID, tp_args = list(cdplot = TRUE, bw = 15))
+
+}
+}
+\keyword{hplot}