weighted.pred.env: Estimation or prediction using weighted partial envelope

In Renvlp: Computing Envelope Estimators

Estimation or prediction using weighted partial envelope

Description

Perform estimation or prediction through weighted partial envelope model.

Usage

weighted.pred.env(X, Y, Xnew)

Arguments

X	Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.
Y	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.
Xnew	The value of X with which to estimate or predict Y. A p dimensional vector.

Details

This function evaluates the envelope model at new value Xnew. It can perform estimation: find the fitted value when X = Xnew, or prediction: predict Y when X = Xnew. But it does not provide the estimation or prediction error. This function performs prediction using the same procedure as in pred2.env, except that the partial envelope estimator with dimension u is replaced by a weighted partial envelope estimator. The weights are decided based on BIC values.

Value

value

The fitted value or the predicted value evaluated at Xnew.

Examples

data(fiberpaper)
X <- fiberpaper[, 5:7]
Y <- fiberpaper[, 1:4]

## Not run: pred.res <- weighted.pred.env(X, Y, X[10, ])

Renvlp documentation built on Oct. 11, 2023, 1:06 a.m.