This function saves rms attributes with the fit object so that anova.rms, Predict, etc. can be used just as with ols
and other fits. No validate or calibrate methods exist for Glm though.
Glm( formula, family = gaussian, data = environment(formula), weights, subset, na.action = na.delete, start =NULL, offset =NULL, control = glm.control(...), model =TRUE, method ="glm.fit", x =FALSE, y =TRUE, contrasts =NULL,...)
Arguments
formula, family, data, weights, subset, na.action, start, offset, control, model, method, x, y, contrasts: see stats::glm(); for printx is the result of Glm
...: ignored
Returns
a fit object like that produced by stats::glm() but with rms attributes and a class of "rms", "Glm", "glm", and "lm". The g element of the fit object is the g-index.
Details
For the print method, format of output is controlled by the user previously running options(prType="lang") where lang is "plain" (the default), "latex", or "html".
Examples
## Dobson (1990) Page 93: Randomized Controlled Trial :counts <- c(18,17,15,20,10,20,25,13,12)outcome <- gl(3,1,9)treatment <- gl(3,3)f <- glm(counts ~ outcome + treatment, family=poisson())f
anova(f)summary(f)f <- Glm(counts ~ outcome + treatment, family=poisson())# could have had rcs( ) etc. if there were continuous predictorsf
anova(f)summary(f, outcome=c('1','2','3'), treatment=c('1','2','3'))