ganova: ANOVA table for linear hypothesis in a linear model

In marutter/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)

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).

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)

marutter/lmreg documentation built on May 8, 2019, 1:51 p.m.