| model.matrix {stats} | R Documentation |
Construct Design Matrices
Description
model.matrix creates a design (or model) matrix, e.g., by
expanding factors to a set of dummy variables (depending on the
contrasts) and expanding interactions similarly.
Usage
model.matrix(object, ...)
## Default S3 method:
model.matrix(object, data = environment(object),
contrasts.arg = NULL, xlev = NULL, ...)
## S3 method for class 'lm'
model.matrix(object, ...)
Arguments
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, |
xlev |
to be used as argument of |
... |
further arguments passed to or from other methods. |
Details
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. 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.
Whereas invalid contrasts.args have been ignored always, they are
warned about since R version 3.6.0.
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.
Value
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.
References
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.
See Also
model.frame, model.extract,
terms
sparse.model.matrix from package
Matrix for creating sparse model matrices, which may
be more efficient in large dimensions.
Examples
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") # typically 'treatment' (for unordered factors)
model.matrix(~ a + b, dd)
model.matrix(~ a + b, dd, contrasts.arg = list(a = "contr.sum"))
model.matrix(~ a + b, dd, contrasts.arg = list(a = "contr.sum", b = contr.poly))
m.orth <- model.matrix(~a+b, dd, contrasts.arg = list(a = "contr.helmert"))
crossprod(m.orth) # m.orth is ALMOST orthogonal
# invalid contrasts.. ignored with a warning:
stopifnot(identical(
model.matrix(~ a + b, dd),
model.matrix(~ a + b, dd, contrasts.arg = "contr.FOO")))