Compute an analysis of deviance table for one or more double generalized linear model fits.
objects of class
Specifying a single object gives sequential and adjusted likelihood ratio tests for the mean and dispersion model components of the fit. The aim is to test overall significance for the mean and dispersion components of the double generalized linear model fit. The sequential tests (i) set both mean and dispersion models constant, add the mean model and (ii) sequentially add the dispersion model. The adjusted tests determine whether the mean and dispersion models can be set constant separately.
An object of class
"anova" inheriting from class
The anova method is questionable when applied to an
"dglm" object with
method="reml" (stick to
Gordon Smyth, ported to R\ by Peter Dunn (firstname.lastname@example.org)
Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.
Smyth, G. K. (1989). Generalized linear models with varying dispersion. J. R. Statist. Soc. B, 51, 47–60.
Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics, 10, 696-709.
Verbyla, A. P., and Smyth, G. K. (1998). Double generalized linear models: approximate residual maximum likelihood and diagnostics. Research Report, Department of Statistics, University of Adelaide.
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# Continuing the example from glm, but this time try # fitting a Gamma double generalized linear model also. library(statmod) clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,42,35,27,25,21,19,18), lot2 = c(69,35,26,21,18,16,13,12,12)) # The same example as in glm: the dispersion is modelled as constant out <- dglm(lot1 ~ log(u), ~1, data=clotting, family=Gamma) summary(out) # Try a double glm out2 <- dglm(lot1 ~ log(u), ~u, data=clotting, family=Gamma) summary(out2) anova(out2)
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