summary.dglm {dglm} | R Documentation |
These functions are all methods
for class dglm
or
summary.glm
objects.
## S3 method for class 'dglm': summary(object, dispersion=NULL, correlation = FALSE, ...)
object |
an object of class "dglm" ,
usually, a result of a call to glm . |
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 |
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.
summary.dglm
returns an object of class
"summary.dglm"
, a list with 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 |
The anova method is questionable when applied to an dglm
object with
method="reml"
(stick to method="ml"
).
Gordon Smyth, ported to R by Peter Dunn (pdunn2@usc.edu.au)
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.
dglm.object
, dglm.control
,
anova.dglm
,
summary.glm
# Continuing the example from glm, but this time try # fitting a Gamma double generalized linear model also. 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) # Summarize the mean model as for a glm summary.glm(out2) # Summarize the dispersion model as for a glm summary(out2$dispersion.fit)