View source: R/model.matrixBayes.R
model.matrixBayes | R Documentation |
model.matrixBayes
creates a design matrix.
model.matrixBayes(object, data = environment(object), contrasts.arg = NULL, xlev = NULL, keep.order = FALSE, drop.baseline=FALSE,...)
object |
an object of an appropriate class. For the default method, a model formula or terms object. |
data |
a data frame created with |
contrasts.arg |
A list, whose entries are contrasts suitable for
input to the |
xlev |
to be used as argument of |
keep.order |
a logical value indicating whether the terms should
keep their positions. If |
drop.baseline |
Drop the base level of categorical Xs, default is TRUE. |
... |
further arguments passed to or from other methods. |
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
.
Yu-Sung Su suyusung@tsinghua.edu.cn
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
model.frame
, model.extract
,
terms
, terms.formula
,
bayesglm
.
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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