rms: Glm – R documentation

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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 = 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 `print` `x` is the result of `Glm`
`...`	ignored model coefficients, standard errors, etc. Specify `coefs=n` to print only the first `n` regression coefficients in the model.

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".

Value

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.

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'))

rms

Regression Modeling Strategies

v6.2-0

GPL (>= 2)

Authors

Frank E Harrell Jr <fh@fharrell.com>

Initial release

2021-03-17

Glm