| glmD {Design} | R Documentation |
This function saves Design attributes with the fit object so that
anova.Design, plot.Design, etc. can be used just as with
ols and other fits. No validate or calibrate
methods exist for glmD though.
glmD(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 'glmD': print(x, digits=4, ...)
formula |
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family |
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data |
|
weights |
|
subset |
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na.action |
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start |
|
offset |
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control |
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model |
|
method |
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x |
|
y |
|
contrasts |
see glm; for print, x is
the result of glmD |
... |
ignored for print |
digits |
number of significant digits to print |
a fit object like that produced by glm but with
Design attributes and a class of "Design",
"glmD", 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 <- glmD(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'))