| 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.fail, 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, ...)
formula |
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family |
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data |
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weights |
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subset |
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na.action |
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start |
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offset |
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control |
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model |
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method |
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x |
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y |
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contrasts |
see glm; for print, x is
the result of Glm |
... |
ignored for print |
digits |
number of significant digits to print |
a fit object like that produced by glm but with
rms attributes and a class of "rms",
"Glm", and "glm" or "glm.null".
## 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'))