TreeShapeCV

[matrix]: a data frame or a matrix of trajectories predictors. Each colunm codes for a trajectory predictor.

[vector]: a vector containing the output trajectories (same length as <code>nrow(X)</code>).

[vector (factor)]: identifier, one for each trajectory to attribute each measurement of <code>X</code> and <code>Y</code> to one of the trajectories (same length as <code>Y</code>).

[vector]: time measurements for the observations of both <code>X</code> and <code>Y</code> (same length as <code>Y</code>).

time

: optional parameters to be passed to the low level function

Builds a Frechet tree that is selected with cross validation

internal

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal heterogeneous data. In this package, we introduce Fr<c3><a9>chet trees and Fr<c3><a9>chet random forests, which allow to manage data for which input and output variables are curves. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized.

TreeShapeCV: Cross validated Frechet tree

Description

Usage

Arguments