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

See Also

dglm, dglm-class, summary.glm


[Package dglm version 1.8.6 Index]