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 gindex.
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'))

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.