rms (version 6.2-0)

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.

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.

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".

See Also

stats::glm(),Hmisc::GiniMd(), prModFit(), stats::residuals.glm

Examples

Run this code
# 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'))

# }

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