model.matrix {base} | R Documentation |
model.matrix
creates a design matrix.
model.matrix (object, ...) model.matrix(object, data = environment(object), contrasts.arg = NULL, xlev = 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 . |
contrasts.arg |
A list, whose entries are contrasts suitable for
input to the contrasts 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. |
... |
further arguments passed to or from other methods. |
model.matrix
creates a design matrix from the description given
in terms(formula)
, using the data in data
which must
contain columns with the same names as would be created by a call to
model.frame(formula)
or, more precisely, by evaluating
attr(terms(formula), "variables")
. There may be other columns
and the order is not important. If contrasts
is specified it
overrides the default factor coding for that variable.
In interactions, the variable whose levels vary fastest is the first
one to appear in the formula (and not in the term), so in ~ a +
b + b:a
the interaction will have a
varying fastest.
The design matrix for a regression model with the specified formula and data.
Chambers, J. M. and Hastie, T. J. eds (1992) Statistical Models in S. Chapman & Hall, London.
model.frame
, model.extract
,
terms
data(trees) ff <- log(Volume) ~ log(Height) + log(Girth) str(m <- model.frame(ff, trees)) mat <- model.matrix(ff, m) dd <- data.frame(a = gl(3,4), b = gl(4,1,12))# balanced 2-way options("contrasts") model.matrix(~ a + b, dd) model.matrix(~ a + b, dd, contrasts = list(a="contr.sum")) model.matrix(~ a + b, dd, contrasts = list(a="contr.sum", b="contr.poly")) m.orth <- model.matrix(~a+b, dd, contrasts = list(a="contr.helmert")) crossprod(m.orth)# m.orth is ALMOST orthogonal