ganova: ANOVA table for linear hypothesis in a linear model

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

Description

Prepares Analysis of Variance table for testing a general linear hypothesis in a linear model

Usage

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

Arguments

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

Value

Returns analysis of variance table for testing A.beta = xi in the linear model with response vector y and matrix of explanatory variables/factors X.

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))
xi <- c(0, 0)
ganova(Abrasion, X, A, xi)
detach(denim)

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