stats (version 3.2.1)

effects: Effects from Fitted Model

Description

Returns (orthogonal) effects from a fitted model, usually a linear model. This is a generic function, but currently only has a methods for objects inheriting from classes "lm" and "glm".

Usage

effects(object, ...)
"effects"(object, set.sign = FALSE, ...)

Arguments

object

an R object; typically, the result of a model fitting function such as lm.

set.sign

logical. If TRUE, the sign of the effects corresponding to coefficients in the model will be set to agree with the signs of the corresponding coefficients, otherwise the sign is arbitrary.

...

arguments passed to or from other methods.

Value

A (named) numeric vector of the same length as residuals, or a matrix if there were multiple responses in the fitted model, in either case of class "coef".The first $r$ rows are labelled by the corresponding coefficients, and the remaining rows are unlabelled. Note that in rank-deficient models the corresponding coefficients will be in a different order if pivoting occurred.

Details

For a linear model fitted by lm or aov, the effects are the uncorrelated single-degree-of-freedom values obtained by projecting the data onto the successive orthogonal subspaces generated by the QR decomposition during the fitting process. The first $r$ (the rank of the model) are associated with coefficients and the remainder span the space of residuals (but are not associated with particular residuals).

Empty models do not have effects.

References

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

See Also

coef

Examples

y <- c(1:3, 7, 5)
x <- c(1:3, 6:7)
( ee <- effects(lm(y ~ x)) )
c( round(ee - effects(lm(y+10 ~ I(x-3.8))), 3) )
# just the first is different