# xenv: Fit the predictor envelope model In Renvlp: Computing Envelope Estimators

 xenv R Documentation

## Fit the predictor envelope model

### Description

Fit the predictor envelope model in linear regression with dimension u.

### Usage

xenv(X, Y, u, asy = TRUE, init = NULL)


### Arguments

 X Predictors. An n by p matrix, p is the number of predictors and n is number of observations. The predictors must be continuous variables. Y Responses. An n by r matrix, r is the number of responses. The response can be univariate or multivariate and must be continuous variable. u Dimension of the envelope. An integer between 0 and p. asy Flag for computing the asymptotic variance of the envelope estimator. The default is TRUE. When p and r are large, computing the asymptotic variance can take much time and memory. If only the envelope estimators are needed, the flag can be set to asy = FALSE. init The user-specified value of Gamma for the envelope subspace in the predictor space. An p by u matrix. The default is the one generated by function envMU.

### Details

This function fits the envelope model in the predictor space,

Y = μ + η'Ω^{-1}Γ' X +\varepsilon, Σ_{X}=ΓΩΓ'+Γ_{0}Ω_{0}Γ'_{0}

using the maximum likelihood estimation. When the dimension of the envelope is between 1 and p-1, the starting value and blockwise coordinate descent algorithm in Cook et al. (2016) is implemented. When the dimension is p, then the envelope model degenerates to the standard multivariate linear regression. When the dimension is 0, it means that X and Y are uncorrelated, and the fitting is different.

### Value

The output is a list that contains the following components:

 beta The envelope estimator of the regression coefficients. SigmaX The envelope estimator of the covariance matrix of X. Gamma An orthonormal basis of the envelope subspace. Gamma0 An orthonormal basis of the complement of the envelope subspace. eta The estimated eta. According to the envelope parameterization, beta = Gamma * Omega^-1 * eta. Omega The coordinates of SigmaX with respect to Gamma. Omega0 The coordinates of SigmaX with respect to Gamma0. mu The estimated intercept. SigmaYcX The estimated conditional covariance matrix of Y given X. loglik The maximized log likelihood function. covMatrix The asymptotic covariance of vec(beta). The covariance matrix returned are asymptotic. For the actual standard errors, multiply by 1 / n. asySE The asymptotic standard error for elements in beta under the envelope model. The standard errors returned are asymptotic, for actual standard errors, multiply by 1 / sqrt(n). ratio The asymptotic standard error ratio of the standard multivariate linear regression estimator over the envelope estimator, for each element in beta. n The number of observations in the data.

### References

Cook, R. D., Helland, I. S. and Su, Z. (2013). Envelopes and Partial Least Squares Re- gression. Journal of the Royal Statistical Society: Series B 75, 851 - 877.

Cook, R. D., Forzani, L. and Su, Z. (2016) A Note on Fast Envelope Estimation. Journal of Multivariate Analysis. 150, 42-54.

simpls.fit for partial least squares (PLS).

### Examples

## Fit the envelope in the predictor space
data(wheatprotein)
X <- wheatprotein[, 1:6]
Y <- wheatprotein[, 7]
u <- u.xenv(X, Y)
u

m <- xenv(X, Y, 4)
m
m$beta ## Fit the partial least squares ## Not run: m1 <- pls::simpls.fit(X, Y, 4) ## Not run: m1$coefficients


Renvlp documentation built on Jan. 8, 2023, 1:08 a.m.