rms (version 4.1-3)

Glm: rms Version of glm

Description

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.

Usage

Glm(formula, family = gaussian, data = list(), weights = NULL, subset =
NULL, na.action = na.delete, start = NULL, offset = NULL, control =
glm.control(...), model = TRUE, method = "glm.fit", x = FALSE, y = TRUE,
contrasts = NULL, ...)
## S3 method for class 'Glm':
print(x, digits=4, coefs=TRUE, latex=FALSE,
title='General Linear Model', \dots)

Arguments

formula,family,data,weights,subset,na.action,start,offset,control,model,method,x,y,contrasts

see glm; for print, x is the result of Glm

...

ignored

digits

number of significant digits to print

coefs

specify coefs=FALSE to suppress printing the table of model coefficients, standard errors, etc. Specify coefs=n to print only the first n regression coefficients in the model.

latex

a logical value indicating whether information should be formatted as plain text or as LaTeX markup

title

a character string title to be passed to prModFit

Value

• a fit object like that produced by 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.

See Also

glm,rms,GiniMd, prModFit,residuals.glm

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 predictors
f
anova(f)
summary(f, outcome=c('1','2','3'), treatment=c('1','2','3'))