| 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. |
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(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.
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.
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
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