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"`

.

1 2 3 4 5 6 7 8 | ```
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,
title='General Linear Model', ...)
``` |

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

`...` |
ignored |

`digits` |
number of significant digits to print |

`coefs` |
specify |

`title` |
a character string title to be passed to |

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.

`glm`

,`rms`

,`GiniMd`

,
`prModFit`

,`residuals.glm`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## 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'))
``` |

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