cisimult: Simultaneous confidence intervals in a linear model

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

Description

Produces two-sided Bonferroni and Scheffe simultaneous confidence intervals, together with corresponding single confidence intervals, for any vector of estimable functions A.beta in a linear model.

Usage

cisimult(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	Collective non-coverage probability of confidence intervals.
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

The three sets of confidence intervals listed as below:

BFCB	Two-sided Bonferroni simultaneous confidence intervals.
SFCB	Two-sided Scheffe simultaneous confidence intervals.
SNCB	The single confidence intervals.

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))
cisimult(Abrasion, X, A, 0.05, tol = 1e-10)
detach(denim)

Example output

sh: 1: cannot create /dev/null: Permission denied
Loading required package: MASS
           [,1]       [,2]       [,3]       [,4]       [,5]       [,6]
[1,]  0.6775213  1.2118120  0.6721353  1.2171980  0.7271829  1.1621504
[2,]  0.1661913  0.7004820  0.1608053  0.7058680  0.2158529  0.6508204
[3,] -0.7784753 -0.2441847 -0.7838614 -0.2387986 -0.7288138 -0.2938462

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