pred.FT

tree

[list]: A list that contains the input curves.

Curve

[list]: A list that contains the input scalars.

Scalar

[list]: A list that contains the input factors.

Factor

[list]: A list that contains the input shape.

Shape

[list]: A list that contains the input images.

Image

[logical]: <code>TRUE</code> if the input shapes are aligned and normalized (<code>aligned.shape=FALSE</code> by default)

aligned.shape

[numeric]: Time scale for the input and output curves (<code>timeScale=0.1</code> by default)

timeScale

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 data, shapes and images. 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 can either curves, scalars, factors, shapes or images. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized.

pred.FT: Predict Frechet tree

Description

Usage

Arguments