arm (version 1.1-14)

model.matrix.bayes: Construct Design Matrices

Description

model.matrix.bayes creates a design matrix.

Usage

model.matrix.bayes(object, data = environment(object), 
    contrasts.arg = NULL, xlev = NULL, keep.order = FALSE, drop.baseline=FALSE,...)
    
model.matrix.bayes.h(object, data = environment(object), 
    contrasts.arg = NULL, xlev = NULL, keep.order = FALSE,  batch = NULL, ...)

Arguments

object

an object of an appropriate class. For the default method, a model formula or terms object.

data

a data frame created with model.frame. If another sort of object, model.frame is called first.

contrasts.arg

A list, whose entries are contrasts suitable for input to the contrasts replacement function and whose names are the names of columns of data containing

xlev

to be used as argument of model.frame if data has no "terms" attribute.

keep.order

a logical value indicating whether the terms should keep their positions. If FALSE the terms are reordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on. Effects of

drop.baseline

Drop the base level of categorical Xs, default is TRUE.

batch

Not implement yet!

...

further arguments passed to or from other methods.

Details

model.matrix.bayes is adapted from model.matrix in the stats pacakge and is designed for the use of bayesglm and bayesglm.hierachical (not yet implemented!). It is designed to keep baseline levels of all categorical varaibles and keep the variable names unodered in the output. The design matrices created by model.matrix.bayes are unidentifiable using classical regression methods, though; they can be identified using bayesglm and bayesglm.hierachical.

References

Andrew Gelman, Aleks Jakulin, Maria Grazia Pittau and Yu-Sung Su, A default prior distribution for logistic and other regression models, unpublished paper available at http://www.stat.columbia.edu/~gelman/standardize/

See Also

model.frame, model.extract, terms, terms.formula, bayesglm.

Examples

ff <- log(Volume) ~ log(Height) + log(Girth)
str(m <- model.frame(ff, trees))
(model.matrix(ff, m))
class(ff) <- c("bayesglm", "terms", "formula")
(model.matrix.bayes(ff, m))
class(ff) <- c("bayesglm.h", "terms", "formula")
(model.matrix.bayes(ff, m))