data

<p>A generated data set containing two multivariate data sets: X and Y, which
represent the set of covariates and responses, respectively. The covariance
matrix of Y has a compound symmetry structure with heterogeneous variances.
Both variances and correlations are functions of the covariates. X variables
are generated from the standard normal distribution. The correlations are
generated with a logit model and the variances are functions of these
generated correlations. The sample size is 200. There are 3 covariates and 3
response variables. x1 and x2 are the importantvariables for the varying
covariance matrix of Y. x3 is the noise variable.</p>

datasets

Covariance Regression with Random Forests (CovRegRF) is a
random forest method for estimating the covariance matrix of a
multivariate response given a set of covariates. Random forest trees
are built with a new splitting rule which is designed to maximize the
distance between the sample covariance matrix estimates of the child
nodes. The method is described in Alakus et al. (2023)
<doi:10.1186/s12859-023-05377-y>. 'CovRegRF' uses 'randomForestSRC' package
(Ishwaran and Kogalur, 2022)
<https://cran.r-project.org/package=randomForestSRC> by freezing at the
version 3.1.0. The custom splitting rule feature is utilised to apply the
proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP'
by default, contingent upon the support provided by the target architecture
and operating system. In this package, 'LAPACK' and 'BLAS' libraries are
used for matrix decompositions.

Cansu Alakus

CovRegRF

Covariance Regression with Random Forests

Denis Larocque

Aurelie Labbe

Hemant Ishwaran

Udaya B. Kogalur

Intel Corporation 

Keita Teranishi

data function

<p>A list with two elements namely X and Y. Each element has 200 rows.
  X has 3 columns and Y has 3 columns.</p>

Format

Generated example data — data

Generated example data

data: Generated example data

Description

Usage

Arguments

Format

Examples