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.

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"`

.

```
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 Glm
print(x, digits=4, coefs=TRUE,
title='General Linear Model', …)

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.

title

a character string title to be passed to `prModFit`

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.

# NOT RUN { ## 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')) # }