ganova {lmreg} | R Documentation |
Prepares Analysis of Variance table for testing a general linear hypothesis in a linear model
ganova(y, X, A, xi, tol=sqrt(.Machine$double.eps))
y |
Responese vector in linear model. |
X |
Design matrix or matrix containing values of explanatory variables (generally including intercept). |
A |
Coefficient matrix (A.beta = xi is the null hypothesis to be tested). |
xi |
A vector (A.beta = xi is the null hypothesis to be tested). |
tol |
A relative tolerance to detect zero singular values while computing generalized inverse, in case the model matrix is rank deficient (default = sqrt(.Machine$double.eps)). |
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.
Debasis Sengupta <shairiksengupta@gmail.com>, Jinwen Qiu <qjwsnow_ctw@hotmail.com>
Sengupta and Jammalamadaka (2019), Linear Models and Regression with R: An Integrated Approach.
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)