model.matrix.bayes {arm} | R Documentation |
model.matrix.bayes
creates a design matrix.
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, ...)
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
factor s. |
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. |
batch |
Not implement yet! |
... |
further arguments passed to or from other methods. |
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
.
Yu-Sung Su ys463@columbia.edu
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.matrix.bayes(ff, m)) class(ff) <- c("bayesglm.h", "terms", "formula") (model.matrix.bayes(ff, m))