R: Construct Design Matrices

model.matrix {base}

R Documentation

Construct Design Matrices

Description

model.matrix creates a design matrix.

Usage

model.matrix   (object, ...)
model.matrix.lm(object, ...)
model.matrix.default(formula, data, contrasts.arg = NULL, xlev = NULL)

Arguments

`formula`	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.
`x`	a model frame.

Details

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.

Value

The design matrix for a regression model with the specified formula and data.

References

Chambers, J. M. and Hastie, T. J. eds (1992) Statistical Models in S. Chapman & Hall, London.

Examples

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

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