remlscoregamma

0th

Percentile

Approximate REML for gamma regression with structured dispersion

Estimates structured dispersion effects using approximate REML with gamma responses.

Usage
remlscoregamma(y,X,Z,mlink="log",dlink="log",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
mlink
character string or numeric value specifying link for mean model
dlink
character string or numeric value specifying link for dispersion model
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:
  • betaVector of regression coefficients for predicting the mean
  • se.beta
  • gammaVector of regression coefficients for predicting the variance
  • se.gamStandard errors for gamma
  • muEstimated means
  • phiEstimated dispersions
  • devianceMinus twice the REML log-likelihood
  • hLeverages

item

details

eqn

$\mu_i=E(y_i)$

var

(y_i)$. We assume the heteroscedastic regression model $$\mm_i=\bold{x}_i^T\bold{\beta}$$ $$\log(\sigma^2_i=\bold{z}_i^T\bold{\gamma},$$ where $x_i$ and $z_i$ are vectors of covariates, and $$ and $$ are vectors of regression coefficients affecting the mean and variance respectively. Parameters are estimated by maximizing the REML likelihood using REML scoring as described in Smyth (2002). Smyth, G. K., and Verbyla, A. P. (2002). Leverage adjustments for dispersion modelling in generalized nonlinear models. data(welding) attach(welding) y <- Strength 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 <- remlscoregamma(y,X,Z) regression

Aliases
  • remlscoregamma
Documentation reproduced from package statmod, version 0.6, License: GPL version 2 or newer

Community examples

Looks like there are no examples yet.