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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"
.
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 Glm
print(x, digits=4, coefs=TRUE,
title='General Linear Model', …)
see glm
; for print
, x
is
the result of Glm
ignored
number of significant digits to print
specify coefs=FALSE
to suppress printing the table
of model coefficients, standard errors, etc. Specify coefs=n
to print only the first n
regression coefficients in the
model.
a character string title to be passed to prModFit
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
# 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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