The total deviance of the fitted generalizrd linear model
Computes the sum of the vector deviance and other intermediate results
glmdev(y, ni, ci, wa, vtheta, offset = 0, icase = .dFvGet()$ics)
y |
The vector of observations |
ni |
The number of trial at xi in the binomial case (ics=2). Otherwise ni=1 for each xi. |
ci |
The constants ci |
wa |
The vector of ai=b/|Axi| |
vtheta |
The vector of xi^T |
offset |
Optional offset added to the linear predictor. |
icase |
Set ics=1 for Bernoulli case, ics=2 for Binomial case and ics=3 for Poisson case |
A list with the following components:
dev |
2*sum_i abs(Li-Ti) |
thetas |
The estimates of theta_i |
li |
The values of Li |
sc |
The alues of Ti |
Kuensch, H.R., Stefanski L.A., Carroll R.J. (1989). Conditionally unbiased bounded-influence estimation in general regression models, with application to generalized linear models. Journal of the American Statistical Association, 84, 460-466.
Marazzi, A. (1993). Algorithms, Routines, and S-functions for robust Statistics. Chapman and Hall, New York.
Marazzi A. (1997). Object oriented S-plus functions for robust discrete generalized linear models available in the doc folder of this package.
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