confelps: Confidence ellipsiod for multiple parameters in a linear...

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

Description

Computes confidence ellipsiod for a vector of estimable functions in a linear model.

Usage

confelps(y, X, A, alpha, 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 is the vector for which confidence interval is needed).
alpha	The non-coverage probability of confidence ellipsoid.
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 a list of three objects:

CenterOfEllipse	Center of ellipsoid.
MatrixOfEllipse	Matrix of ellipsoid, for describing quadratic form in terms of the vector of deviations from center of ellipsoid.
threshold	Upper limit of quadratic form that completes specification of ellipsoid.

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(0,1,0,-1,0,0,0),c(0,0,1,-1,0,0,0))
confelps(Abrasion, X, A, 0.05,tol=1e-12)
detach(denim)

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