| model.matrix {stats} | R Documentation |
model.matrix creates a design matrix.
model.matrix(object, ...)
## Default S3 method:
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. 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. |
... |
further arguments passed to or from other methods. |
model.matrix creates a design matrix from the description given
in terms(object), using the data in data which must
contain variables with the same names as would be created by a call to
model.frame(object) or, more precisely, by evaluating
attr(terms(object), "variables"). If it is a data frame,
there may be other columns and the order of columns is not important.
If contrasts.arg is specified for a factor it overrides the
default factor coding for that variable and any "contrasts"
attribute set by C or contrasts.
In an interaction term, 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.
By convention, if the response variable also appears on the right-hand side of the formula it is dropped (with a warning), although interactions involving the term are retained.
The design matrix for a regression model with the specified formula
and data.
There is an attribute "assign", an integer vector with an entry
for each column in the matrix giving the term in the formula which
gave rise to the column.
If there are any factors in terms in the model, there is an attribute
"contrasts", a named list with an entry for each factor. This
specifies the contrasts that would be used in terms in which the
factor is coded by contrasts (in some terms dummy coding may be used),
either as a character vector naming a function or as a numeric matrix.
Chambers, J. M. (1992) Data for models. Chapter 3 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
model.frame, model.extract,
terms
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