# pois.env: Fit the envelope model in poisson regression In Renvlp: Computing Envelope Estimators

 pois.env R Documentation

## Fit the envelope model in poisson regression

### Description

Fit the envelope model in poisson regression with dimension u.

### Usage

pois.env(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 Response. An n by 1 matrix. The univariate response must be counts. 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 poisson regression. An p by u matrix. The default is the one generated by function pois.envMU.

### Details

This function fits the envelope model in poisson regression,

 Y = exp(\mu + \beta' X), \Sigma_{X}=\Gamma\Omega\Gamma'+\Gamma_{0}\Omega_{0}\Gamma'_{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. This model works the best when X is multivariate normal.

### Value

The output is a list that contains the following components:

 beta The envelope estimator of the canonical parameter. 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 beta of the canonical parameter with respect to Gamma. Omega The coordinates of SigmaX with respect to Gamma. Omega0 The coordinates of SigmaX with respect to Gamma0. mu The estimated intercept of the canonical parameter. 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., Zhang, X. (2015). Foundations for Envelope Models and Methods. Journal of the American Statistical Association 110, 599 - 611.

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

### Examples

data(horseshoecrab)
X1 <- as.numeric(horseshoecrab[ , 1] == 2)
X2 <- as.numeric(horseshoecrab[ , 1] == 3)
X3 <- as.numeric(horseshoecrab[ , 1] == 4)
X4 <- as.numeric(horseshoecrab[ , 2] == 2)
X5 <- as.numeric(horseshoecrab[ , 2] == 3)
X6 <- horseshoecrab[ , 3]
X7 <- horseshoecrab[ , 5]
X <- cbind(X1, X2, X3, X4, X5, X6, X7)
Y <- horseshoecrab[ , 4]

## Not run: u <- u.pois.env(X, Y)
## Not run: u

m <- pois.env(X, Y, 1)
m\$beta


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