contrasts {stats} R Documentation

## Get and Set Contrast Matrices

### Description

Set and view the contrasts associated with a factor.

### Usage

contrasts(x, contrasts = TRUE, sparse = FALSE)
contrasts(x, how.many = NULL) <- value


### Arguments

 x a factor or a logical variable. contrasts logical. See ‘Details’. sparse logical indicating if the result should be sparse (of class dgCMatrix), using package Matrix. how.many integer number indicating how many contrasts should be made. Defaults to one less than the number of levels of x. This need not be the same as the number of columns of value. value either a numeric matrix (or a sparse or dense matrix of a class extending dMatrix from package Matrix) whose columns give coefficients for contrasts in the levels of x, or (the quoted name of) a function which computes such matrices.

### Details

If contrasts are not set for a factor the default functions from options("contrasts") are used.

A logical vector x is converted into a two-level factor with levels c(FALSE, TRUE) (regardless of which levels occur in the variable).

The argument contrasts is ignored if x has a matrix contrasts attribute set. Otherwise if contrasts = TRUE it is passed to a contrasts function such as contr.treatment and if contrasts = FALSE an identity matrix is returned. Suitable functions have a first argument which is the character vector of levels, a named argument contrasts (always called with contrasts = TRUE) and optionally a logical argument sparse.

If value supplies more than how.many contrasts, the first how.many are used. If too few are supplied, a suitable contrast matrix is created by extending value after ensuring its columns are contrasts (orthogonal to the constant term) and not collinear.

### References

Chambers, J. M. and Hastie, T. J. (1992) Statistical models. Chapter 2 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

C, contr.helmert, contr.poly, contr.sum, contr.treatment; glm, aov, lm.

### Examples

utils::example(factor)
fff <- ff[, drop = TRUE]  # reduce to 5 levels.
contrasts(fff) # treatment contrasts by default
contrasts(C(fff, sum))
contrasts(fff, contrasts = FALSE) # the 5x5 identity matrix

contrasts(fff) <- contr.sum(5); contrasts(fff)  # set sum contrasts
contrasts(fff, 2) <- contr.sum(5); contrasts(fff)  # set 2 contrasts
# supply 2 contrasts, compute 2 more to make full set of 4.
contrasts(fff) <- contr.sum(5)[, 1:2]; contrasts(fff)

## using sparse contrasts: % useful, once model.matrix() works with these :
ffs <- fff
contrasts(ffs) <- contr.sum(5, sparse = TRUE)[, 1:2]; contrasts(ffs)
stopifnot(all.equal(ffs, fff))
contrasts(ffs) <- contr.sum(5, sparse = TRUE); contrasts(ffs)


[Package stats version 4.3.0 Index]