cond function

Approximate Conditional Inference - Generic Function

Performs approximate conditional inference.

cond(object, offset, ...)

Arguments

• object: a fitted model object. Families supported are binomial and Poisson with canonical link function (class glm), and regression-scale models (class rsm).

• offset: the covariate occurring in the model formula whose coefficient represents the parameter of interest. May be numerical or a two-level factor. In case of a two-level 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 exp, sin, ) or to a mathematical operator (\^, /, ) applied to a numerical variable. The call must always agree with the label used to identify the corresponding parameter in the fitted model object passed through the object argument. Beware that the label includes the identity function I() if an arithmetic operator was used. Other function types (e.g. factor) and interactions are not admitted.

• ...: absorbs any additional arguments. See cond.glm

  and cond.rsm for details.

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.

Returns

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 Small-Sample 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

## Urine Data 
## Not run:

data(urine)
urine.glm <- glm(r ~ gravity + ph + osmo + cond + 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 + cond + urea + calc, 
                 family = binomial, data = urine)
coef(urine.glm)
urine.glm <- glm(r ~ I(gravity * 100) + ph + osmo + cond + urea + calc, 
                 family = binomial, data = urine)
coef(urine.glm)
urine.cond <- cond(urine.glm, I(gravity * 100))
plot(urine.cond, which = 4)
## End(Not run)

## House Price Data
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)