remlscore {statmod}R Documentation

REML for heteroscedastic regression

Description

Fits a heteroscedastic regression model using residual maximum likelihood (REML).

Usage

remlscore(y, X, Z, trace=FALSE, tol=1e-5, maxit=40)

Arguments

y numeric vector of responses
X design matrix for predicting the mean
Z design matrix for predicting the variance
trace Logical variable. If true then output diagnostic information at each iteration.
tol Convergence tolerance
maxit Maximum number of iterations allowed

Value

List with the following components:

beta Vector of regression coefficients for predicting the mean
se.beta <Standard errors for beta
gamma Vector of regression coefficients for predicting the variance
se.gam Standard errors for gamma
mu Estimated means
phi Estimated variances
deviance Minus twice the REML log-likelihood
h Leverages

References

Smyth, G. K. (2002). An efficient algorithm for REML in heteroscedastic regression. Journal of Computational and Graphical Statistics 11, 1-12.

Examples

data(welding)
attach(welding)
y <- Strength
# Reproduce results from Table 1 of Smyth (2002)
X <- cbind(1,(Drying+1)/2,(Material+1)/2)
colnames(X) <- c("1","B","C")
Z <- cbind(1,(Material+1)/2,(Method+1)/2,(Preheating+1)/2)
colnames(Z) <- c("1","C","H","I")
out <- remlscore(y,X,Z)
cbind(Estimate=out$gamma,SE=out$se.gam)

[Package statmod version 1.0.6 Index]