u.stenv

This function outputs dimensions selected by Akaike information criterion (AIC), Bayesian information criterion (BIC) and likelihood ratio testing with specified significance level for the simultaneous envelope model.

Provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

Minji Lee

Renvlp

Computing Envelope Estimators

u.stenv function

<dl> <dt>X</dt>
<dd>Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.</dd> <dt>Y</dt>
<dd>Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.</dd> <dt>alpha</dt>
<dd>Significance level for testing. The default is 0.01.</dd></dl>

Arguments

Select the dimension of stenv — u.stenv

<dl>

 <dt>X</dt>
<dd>Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.</dd>

 <dt>Y</dt>
<dd>Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.</dd>

 <dt>alpha</dt>
<dd>Significance level for testing. The default is 0.01.</dd>

</dl>

u.stenv: Select the dimension of stenv

Description

Usage

Value

Arguments

Examples