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+\name{Conditional Inference Trees}
+\alias{ctree}
+\alias{conditionalTree}
+\title{ Conditional Inference Trees }
+\description{
+  Recursive partitioning for continuous, censored, ordered, nominal and
+  multivariate response variables in a conditional inference framework. 
+}
+\usage{
+ctree(formula, data, subset = NULL, weights = NULL, 
+      controls = ctree_control(), xtrafo = ptrafo, ytrafo = ptrafo, 
+      scores = NULL)
+}
+\arguments{
+  \item{formula}{ a symbolic description of the model to be fit. Note
+                  that symbols like \code{:} and \code{-} will not work
+                  and the tree will make use of all variables listed on the
+                  rhs of \code{formula}.}
+  \item{data}{ a data frame containing the variables in the model. }
+  \item{subset}{ an optional vector specifying a subset of observations to be
+                 used in the fitting process.}
+  \item{weights}{ an optional vector of weights to be used in the fitting
+                  process. Only non-negative integer valued weights are
+                  allowed.}
+  \item{controls}{an object of class \code{\link{TreeControl}}, which can be
+                  obtained using \code{\link{ctree_control}}.}
+  \item{xtrafo}{ a function to be applied to all input variables.
+                By default, the \code{\link{ptrafo}} function is applied.}
+  \item{ytrafo}{ a function to be applied to all response variables. 
+                By default, the \code{\link{ptrafo}} function is applied.}
+  \item{scores}{ an optional named list of scores to be attached to ordered
+               factors.}
+}
+\details{
+
+  Conditional inference trees estimate a regression relationship by binary recursive
+  partitioning in a conditional inference framework. Roughly, the algorithm
+  works as follows: 1) Test the global null hypothesis of independence between
+  any of the input variables and the response (which may be multivariate as well). 
+  Stop if this hypothesis cannot be rejected. Otherwise select the input
+  variable with strongest association to the resonse. This
+  association is measured by a p-value corresponding to a test for the
+  partial null hypothesis of a single input variable and the response.
+  2) Implement a binary split in the selected input variable. 
+  3) Recursively repeate steps 1) and 2). 
+
+  The implementation utilizes a unified framework for conditional inference,
+  or permutation tests, developed by Strasser and Weber (1999). The stop
+  criterion in step 1) is either based on multiplicity adjusted p-values 
+  (\code{testtype == "Bonferroni"}
+  or \code{testtype == "MonteCarlo"} in \code{\link{ctree_control}}),
+  on the univariate p-values (\code{testtype == "Univariate"}),
+  or on values of the test statistic
+  (\code{testtype == "Teststatistic"}). In both cases, the
+  criterion is maximized, i.e., 1 - p-value is used. A split is implemented 
+  when the criterion exceeds the value given by \code{mincriterion} as
+  specified in \code{\link{ctree_control}}. For example, when 
+  \code{mincriterion = 0.95}, the p-value must be smaller than
+  $0.05$ in order to split this node. This statistical approach ensures that
+  the right sized tree is grown and no form of pruning or cross-validation
+  or whatsoever is needed. The selection of the input variable to split in
+  is based on the univariate p-values avoiding a variable selection bias
+  towards input variables with many possible cutpoints.
+
+  Multiplicity-adjusted Monte-Carlo p-values are computed 
+  following a "min-p" approach. The univariate 
+  p-values based on the limiting distribution (chi-square
+  or normal) are computed for each of the random 
+  permutations of the data. This means that one should
+  use a quadratic test statistic when factors are in
+  play (because the evaluation of the corresponding
+  multivariate normal distribution is time-consuming).
+
+  By default, the scores for each ordinal factor \code{x} are
+  \code{1:length(x)}, this may be changed using \code{scores = list(x =
+  c(1,5,6))}, for example.
+
+  Predictions can be computed using \code{\link{predict}} or
+  \code{\link{treeresponse}}.  The first function accepts arguments
+  \code{type = c("response", "node", "prob")} where \code{type = "response"}
+  returns predicted means, predicted classes or median predicted survival
+  times, \code{type = "node"} returns terminal node IDs (identical to
+  \code{\link{where}}) and \code{type = "prob"} gives more information about
+  the conditional distribution of the response, i.e., class probabilities or
+  predicted Kaplan-Meier curves and is identical to
+  \code{\link{treeresponse}}.  For observations with zero weights,
+  predictions are computed from the fitted tree when \code{newdata = NULL}.
+
+  For a general description of the methodology see Hothorn, Hornik and
+  Zeileis (2006) and Hothorn, Hornik, van de Wiel and Zeileis (2006). 
+  Introductions for novices can be found in Strobl et al. (2009) and
+  at \url{http://github.com/christophM/overview-ctrees.git}.
+
+}
+\value{
+  An object of class \code{\link{BinaryTree-class}}.
+}
+\references{ 
+
+   Helmut Strasser and Christian Weber (1999). On the asymptotic theory of permutation
+   statistics. \emph{Mathematical Methods of Statistics}, \bold{8}, 220--250.
+
+   Torsten Hothorn, Kurt Hornik, Mark A. van de Wiel and Achim Zeileis (2006).
+   A Lego System for Conditional Inference. \emph{The American Statistician},
+   \bold{60}(3), 257--263.
+
+   Torsten Hothorn, Kurt Hornik and Achim Zeileis (2006). Unbiased Recursive
+   Partitioning: A Conditional Inference Framework. \emph{Journal of
+   Computational and Graphical Statistics}, \bold{15}(3), 651--674. 
+   Preprint available
+   from \url{http://statmath.wu-wien.ac.at/~zeileis/papers/Hothorn+Hornik+Zeileis-2006.pdf}
+
+   Carolin Strobl, James Malley and Gerhard Tutz (2009).
+   An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of 
+   Classification and Regression Trees, Bagging, and Random forests.
+   \emph{Psychological Methods}, \bold{14}(4), 323--348. 
+
+}
+\examples{
+
+    set.seed(290875)
+
+    ### regression
+    airq <- subset(airquality, !is.na(Ozone))
+    airct <- ctree(Ozone ~ ., data = airq, 
+                   controls = ctree_control(maxsurrogate = 3))
+    airct
+    plot(airct)
+    mean((airq$Ozone - predict(airct))^2)
+    ### extract terminal node ID, two ways
+    all.equal(predict(airct, type = "node"), where(airct))
+
+    ### classification
+    irisct <- ctree(Species ~ .,data = iris)
+    irisct
+    plot(irisct)
+    table(predict(irisct), iris$Species)
+
+    ### estimated class probabilities, a list
+    tr <- treeresponse(irisct, newdata = iris[1:10,])
+
+    ### ordinal regression
+    data("mammoexp", package = "TH.data")
+    mammoct <- ctree(ME ~ ., data = mammoexp) 
+    plot(mammoct)
+
+    ### estimated class probabilities
+    treeresponse(mammoct, newdata = mammoexp[1:10,])
+
+    ### survival analysis
+    if (require("TH.data") && require("survival")) {
+        data("GBSG2", package = "TH.data")
+        GBSG2ct <- ctree(Surv(time, cens) ~ .,data = GBSG2)
+        plot(GBSG2ct)
+        treeresponse(GBSG2ct, newdata = GBSG2[1:2,])        
+    }
+
+    ### if you are interested in the internals:
+    ### generate doxygen documentation
+    \dontrun{
+
+        ### download src package into temp dir
+        tmpdir <- tempdir()
+        tgz <- download.packages("party", destdir = tmpdir)[2]
+        ### extract
+        untar(tgz, exdir = tmpdir)
+        wd <- setwd(file.path(tmpdir, "party"))
+        ### run doxygen (assuming it is there)
+        system("doxygen inst/doxygen.cfg")
+        setwd(wd)
+        ### have fun
+        browseURL(file.path(tmpdir, "party", "inst", 
+                            "documentation", "html", "index.html")) 
+    }
+}
+\keyword{tree}