remlscore {statmod} | R Documentation |
Fits a heteroscedastic regression model using residual maximum likelihood (REML).
remlscore(y, X, Z, trace=FALSE, tol=1e-5, maxit=40)
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 |
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 |
Smyth, G. K. (2002). An efficient algorithm for REML in heteroscedastic regression. Journal of Computational and Graphical Statistics 11, 1-12.
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)