| family | character giving the family name. |
| link | character giving the link name. |
| linkfun | link function with argument mu that transforms from the scale of the mean to the scale of the linear or additive predictor eta. |
| linkinv | function with argument eta that defines the inverse of the link. |
| variance | function with argument mu that defines the variance function. |
| dev.resids | function with three arguments: y, mu, wt that gives deviance residuals. |
| aic | function with arguments: y, n, mu, wt, dev that gives the AIC value. |
| mu.eta | function with argument eta that defines the derivative of the linkinv function. |
| initialize |
expression to initialize the fitted values mu in the body of
glm and gam.
Other values can also be initialized, including the prior weights
w and the maximum number of iterations maxit.
Modifying the w and maxit expressions should be done with care, and is only recommended for experienced users. Other variables local to glm and gam can be initialized as well; see binomial()\$initialize for an example. The initialize expression can be used to transform a response variable having specialized structure into the required vector response y. Once again, the binomial family serves as an example. |
| validmu | logical function with argument mu that indicates the validity of mu in variance. |
| valideta | logical function with argument eta that indicates the validity of eta in linkinv. |
| simulate |
simulate function with argument object, nsim, where object is a glm.object or lm.object and
nsim is the number of simulations.
A list or vector is returned that contains simulated responses based on the fitted model. This component is optional and is only available in family functions: binomial, gaussian, Gamma, inverse.gaussian, and poisson |
| varfun |
character name of variance function. This component is optional and only available in family function quasi.
The character could be "constant", "mu(1-mu)", "mu", "mu^2", and "mu^3". |
attributes(binomial()) gaussian()$initialize attributes(Gamma()) inverse.gaussian() poisson()$simulate quasi(variance="mu^2")$varfun attributes(quasibinomial(link="logit")) attributes(quasipoisson(link="log"))