summary.dglm: Summarize Double Generalized Linear Model Fit

Description

Summarize objects of class "dglm".

Usage

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

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

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

Author

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

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.

References

Smyth, G. K. (1989). Generalized linear models with varying dispersion. J. R. Statist. Soc. B, 51, 47--60. tools:::Rd_expr_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. tools:::Rd_expr_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