model.matrixBayes: Construct Design Matrices

Description Usage Arguments Details Author(s) References See Also Examples

Description

model.matrixBayes creates a design matrix.

Usage

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model.matrixBayes(object, data = environment(object),
    contrasts.arg = NULL, xlev = NULL, keep.order = FALSE, drop.baseline=FALSE,...)

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 factors.

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 a given order are kept in the order specified.

drop.baseline

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

...

further arguments passed to or from other methods.

Details

model.matrixBayes is adapted from model.matrix in the stats pacakge and is designed for the use of bayesglm. 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.matrixBayes are unidentifiable using classical regression methods, though; they can be identified using bayesglm.

Author(s)

Yu-Sung Su suyusung@tsinghua.edu.cn

References

Andrew Gelman, Aleks Jakulin, Maria Grazia Pittau and Yu-Sung Su. (2009). “A Weakly Informative Default Prior Distribution For Logistic And Other Regression Models.” The Annals of Applied Statistics 2 (4): 1360–1383. http://www.stat.columbia.edu/~gelman/research/published/priors11.pdf

See Also

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

Examples

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


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