| model.matrix {stats} | R Documentation |
model.matrix creates a design (or model) 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 a |
data |
a data frame created with |
contrasts.arg |
A list, whose entries are values (numeric
matrices or character strings naming functions) to be used
as replacement values for the |
xlev |
to be used as argument of |
... |
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
supply 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 data is a data
frame, there may be other columns and the order of columns is not
important. Any character variables are coerced to factors, with a
warning. After coercion, all the variables used on the right-hand
side of the formula must be logical, integer, numeric or factor.
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-like 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. Value 0 corresponds to the intercept
(if any), and positive values to terms in the order given by the
term.labels attribute of the terms structure
corresponding to object.
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)
utils::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