boot.eppls

Compute bootstrap standard error for the Envelope-based Partial Partial Least Squares estimator.

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

boot.eppls function

<dl> <dt>X1</dt>
<dd>An \(n\) by \(p1\) matrix of continuous predictors, where \(p1\) is the number of continuous predictors with \(p1 &lt; n\).</dd> <dt>X2</dt>
<dd>An \(n\) by \(p2\) matrix of categorical predictors, where \(p2\) is the number of categorical predictors with \(p2 &lt; n\).</dd> <dt>Y</dt>
<dd>An \(n\) by \(r\) matrix of multivariate responses, where \(r\) is the number of responses.</dd> <dt>u</dt>
<dd>A given dimension of the Envelope-based Partial Partial Least Squares. It should be an interger between \(0\) and \(p1\).</dd>
 <dt>B</dt>
<dd>Number of bootstrap samples. A positive integer.</dd></dl>

Arguments

Bootstrap for eppls — boot.eppls

<dl>

 <dt>X1</dt>
<dd>An \(n\) by \(p1\) matrix of continuous predictors, where \(p1\) is the number of continuous predictors with \(p1 &lt; n\).</dd>

 <dt>X2</dt>
<dd>An \(n\) by \(p2\) matrix of categorical predictors, where \(p2\) is the number of categorical predictors with \(p2 &lt; n\).</dd>

 <dt>Y</dt>
<dd>An \(n\) by \(r\) matrix of multivariate responses, where \(r\) is the number of responses.</dd>

 <dt>u</dt>
<dd>A given dimension of the Envelope-based Partial Partial Least Squares. It should be an interger between \(0\) and \(p1\).</dd>


 <dt>B</dt>
<dd>Number of bootstrap samples. A positive integer.</dd>

</dl>

boot.eppls: Bootstrap for eppls

Description

Usage

Value

Arguments

Details

Examples