% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/longitree.R
\name{longitrees}
\alias{longitrees}
\title{Construction of Multiple Decision Trees for Longitudinal Data}
\usage{
longitrees(
  formula,
  time,
  random,
  weight = "w",
  data,
  alpha = "no",
  gamma = "no",
  cv = "no",
  maxdepth = 5,
  minbucket = 5,
  minsplit = 20,
  xval = 10,
  bootsize,
  trees = 100,
  mins = 40
)
}
\arguments{
\item{formula}{A formula specifying the model.
The response variable should be on the left side and covariates on the
right side.  Use \code{response ~ .} to include all covariates except the
time variable and the random effect, or select specific covariates such as
\code{response ~ x1 + x2}.  Time-invariant (baseline) covariates are
assumed.}

\item{time}{Character string giving the column name of the time variable.
All individuals are assumed to be observed at the same time points.}

\item{random}{Character string giving the column name of the random effect
(subject identifier).}

\item{weight}{Weight for balancing the main effect of a covariate and
its interaction with time.  A value in
\eqn{\{0.0, 0.1, \ldots, 1.0\}}: \code{1.0} evaluates only the
mean difference in the response variable between the two groups and
\code{0.0} evaluates only the difference in change over time of the
response variable between the two groups.
Set \code{weight = "w"} (the default) to select the optimal weight
from the same grid at each node.}

\item{data}{A data frame containing the variables in \code{formula} together
with the time and random-effect variables.}

\item{alpha}{Significance level used as the stopping rule for tree
growth.  A smaller value produces a more conservative (smaller) tree.
Specify a numeric value or \code{"no"} (default) if not used.
Corresponds to ST2.}

\item{gamma}{Complexity parameter for pruning.  A larger value prunes
more aggressively, yielding a smaller and simpler tree; a smaller
value retains more branches.  Specify a numeric value or \code{"no"}
(default) if not used.  Corresponds to ST3.}

\item{cv}{Set \code{"yes"} to construct the decision tree using
cross-validation, or \code{"no"} (default) otherwise.
Corresponds to ST1.}

\item{maxdepth}{Maximum depth of the tree (default 5).}

\item{minbucket}{Minimum number of subjects in a terminal node (default 5).}

\item{minsplit}{Minimum number of subjects required to attempt a split
(default 20).}

\item{xval}{Number of cross-validation folds (default 10).  Used to
compute the cross-validated coefficient of determination
(\eqn{R^2_{\mathrm{CV}}}); when \code{cv = "yes"}, also used for
final tree selection.}

\item{bootsize}{Number of subjects in each bootstrap sample.}

\item{trees}{Number of bootstrap trees to grow (default 100).}

\item{mins}{Number of top-ranking candidate three-tree subsets to retain
(default 40).}
}
\value{
An object of class \code{"longitrees"}.  Pass to
\code{\link{selectionplot}} to select the optimal three-tree combination.
}
\description{
Generates multiple trees from bootstrap samples and evaluates
all three-tree combinations based on two criteria: cross-validated
prediction error and tree diversification measured by the adjusted Rand
index (ARI).  Bootstrap sampling is performed at the subject level to
preserve longitudinal structure.
}
\details{
See \code{\link{longitree}} for a description of the three single-tree
construction procedures (ST1, ST2, ST3) corresponding to \code{cv},
\code{alpha}, and \code{gamma}.
}
\references{
Obata, R. and Sugimoto, T. (2026). A decision tree analysis for
longitudinal measurement data and its applications.
\emph{Advances in Data Analysis and Classification}.
\doi{10.1007/s11634-025-00665-2}
}
\seealso{
\code{\link{longitree}}, \code{\link{selectionplot}},
\code{\link{threetrees}}, \code{\link{treeplot}}
}
