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.
USAGE:
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, ...)
ARGUMENTS:
formula
family
data
weights
subset
na.action
start
offset
control
model
method
x
y
contrasts
see
; for
print,
x is
the result of
glmD
...
ignored for
print
digits
number of significant digits to print
VALUE:
a fit object like that produced by
but with
Design
attributes and a
class of
"Design",
"glmD"
, and
"glm" or
"glm.null".
SEE ALSO:
,
EXAMPLES:
## 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'))