Glm {rms} | R Documentation |
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.
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', ...) ## S3 method for class 'Glm' residuals(object, ...)
formula,family,data,weights,subset,na.action,start,offset,control,model,method,x,y,contrasts |
see |
... |
ignored for |
digits |
number of significant digits to print |
coefs |
specify |
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 |
object |
a fit object created by |
a fit object like that produced by glm
but with
rms
attributes and a class
of "rms"
,
"Glm"
, and "glm"
or "glm.null"
. The g
element of the fit object is the g-index.
glm
,rms
,GiniMd
,
prModFit
,residuals.glm
## 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'))