multcomp: Multiple comparison tests

In lmreg: Data and Functions Used in Linear Models and Regression with R: An Integrated Approach

Description

Produces p-values of Bonferroni and Scheffe multiple comparison tests of several testable linear hypotheses.

Usage

multcomp(y, X, A, xi, tol=sqrt(.Machine$double.eps))

Arguments

y	Responese vector in linear model.
X	Design/model matrix or matrix containing values of explanatory variables (generally including intercept).
A	Coefficient matrix (A.beta=xi is the set of multiple hypotheses that has to be tested).
xi	A vector of values (A.beta=xi is the set of multiple hypotheses that has to be tested).
tol	A relative tolerance to detect zero singular values while computing generalized inverse, in case X is rank deficient (default = sqrt(.Machine$double.eps)).

Details

Normal distribution of response (given explanatory variables and/or factors) is assumed.

Value

Returns F statistics and p-values of Bonferroni and Scheffe multiple comparison tests of the set of linear hypotheses. A set of five vectors:

A	Specified coefficient matrix.
xi	Specified values of A.beta.
Fstat	Set of F-ratios for each hypothesis.
Bonferroni.p	Set of Bonferroni p-values for different hypotheses.
Scheffe.p	Set of Scheffe p-values for different hypotheses.

Author(s)

Debasis Sengupta <[e-mail]>, Jinwen Qiu <[e-mail]>

References

Sengupta and Jammalamadaka (2019), Linear Models and Regression with R: An Integrated Approach.

Examples

data(denim)
attach(denim)
X <- cbind(1,binaries(Denim),binaries(Laundry))
A <- rbind(c(0,1,-1,0,0,0,0),c(0,1,0,-1,0,0,0),c(0,0,1,-1,0,0,0))
xi <- c(0,0,0)
multcomp(Abrasion, X, A, xi, tol=1e-14)
detach(denim)

lmreg documentation built on May 2, 2019, 9:29 a.m.