Approximate Conditional Inference  Generic Function
Description
Performs approximate conditional inference.
Usage
1 
Arguments
object 
a fitted model object. Families supported are binomial and
Poisson with canonical link function (class 
offset 
the covariate occurring in the model formula whose coefficient
represents the parameter of interest. May be numerical or a
twolevel factor. In case of a twolevel factor, it must be
coded by contrasts and not appear as two dummy variables in the
model. Can also be a call to a mathematical function (such as

... 
absorbs any additional arguments. See 
Details
This function is generic (see methods
); method
functions can be written to handle specific classes of data.
Classes which already have methods for this function include:
glm
and rsm
.
Value
The returned value is an approximate conditional inference
object. Classes already supported are cond
and
marg
depending on whether the fitted model object passed
through the object
argument has class glm
or
rsm
. See cond.object
or
marg.object
for more details.
References
Brazzale, A. R. (2000) Practical SmallSample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne. Chapter 6.
See Also
cond.glm
, cond.rsm
,
cond.object
, marg.object
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  ## Urine Data
data(urine)
urine.glm < glm(r ~ gravity + ph + osmo + conduct + urea + log(calc),
family = binomial, data = urine)
##
## function call as offset variable
labels(coef(urine.glm))
cond(urine.glm, log(calc))
##
## large estimate of regression coefficient
urine.glm < glm(r ~ gravity + ph + osmo + conduct + urea + calc,
family = binomial, data = urine)
coef(urine.glm)
urine.glm < glm(r ~ I(gravity * 100) + ph + osmo + conduct + urea + calc,
family = binomial, data = urine)
coef(urine.glm)
urine.cond < cond(urine.glm, I(gravity * 100))
plot(urine.cond, which = 4)
## House Price Data
## Not run:
data(houses)
houses.rsm < rsm(price ~ ., family = student(5), data = houses)
##
## parameter of interest: scale parameter
houses.marg < cond(houses.rsm, scale)
plot(houses.marg, which = 2)
## End(Not run)
