R: Summarize Double Generalized Linear Model Fit

summary.dglm {dglm}

R Documentation

Summarize Double Generalized Linear Model Fit

Description

Summarize objects of class "dglm".

Usage

## S3 method for class 'dglm'
summary(object, dispersion=NULL, correlation = FALSE, ...)

Arguments

`object`	an object of class `"dglm"`.
`dispersion`	the dispersion parameter for the fitting family. By default it is obtained from `object`.
`correlation`	logical; if `TRUE`, the correlation matrix of the estimated parameters is returned and printed.
`...`	further arguments to be passed to `summary.glm`

Details

For more details, see summary.glm.

If more than one of etastart, start and mustart is specified, the first in the list will be used.

Value

An object of class "summary.dglm", which is a list with the following components:

`call`	the component from `object`
`terms`	the component from `object`
`family`	the component from `object`
`deviance`	the component from `object`
`aic`	`NULL` here
`constrasts`	(where relevant) the contrasts used. NOT WORKING??
`df.residual`	the component from `object`
`null.deviance`	the component from `object`
`df.null`	the residual degrees of freedom for the null model.
`iter`	the component from `object`
`deviance.resid`	the deviance residuals: see `residuals.glm`
`coefficients`	the matrix of coefficients, standard errors, `z`-values and `p`-values. Aliased coefficients are omitted.
`aliased`	named logical vector showing if the original coefficients are aliased.
`dispersion`	either the supplied argument or the estimated dispersion if the latter in `NULL`
`df`	a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of non-aliased coefficients.
`cov.unscaled`	the unscaled (`dispersion = 1`) estimated covariance matrix of the estimated coefficients.
`cov.scaled`	ditto, scaled by `dispersion`
`correlation`	(only if `correlation` is true.) The estimated correlations of the estimated coefficients.
`dispersion.summary`	the summary of the fitted dispersion model
`outer.iter`	the number of outer iteration of the alternating iterations
`m2loglik`	minus twice the log-likelihood of the fitted model

Note

The anova method is questionable when applied to an dglm object created with method="reml" (stick to method="ml").

Author(s)

Gordon Smyth, ported to R by Peter Dunn (pdunn2@usc.edu.au)

References

Smyth, G. K. (1989). Generalized linear models with varying dispersion. J. R. Statist. Soc. B, 51, 47–60. doi:10.1111/j.2517-6161.1989.tb01747.x

Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics, 10, 696-709. doi:10.1002/(SICI)1099-095X(199911/12)10:6<695::AID-ENV385>3.0.CO;2-M https://gksmyth.github.io/pubs/Ties98-Preprint.pdf

Smyth, G. K., and Verbyla, A. P. (1999). Double generalized linear models: approximate REML and diagnostics. In Statistical Modelling: Proceedings of the 14th International Workshop on Statistical Modelling, Graz, Austria, July 19-23, 1999, H. Friedl, A. Berghold, G. Kauermann (eds.), Technical University, Graz, Austria, pages 66-80. https://gksmyth.github.io/pubs/iwsm99-Preprint.pdf