cisngl: Confidence interval for a linear parametric function in a...

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

Description

Computes point estimate and confidence interval for a single linear parametric function in a linear model.

Usage

cisngl(y, X, p, alpha, type, 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).
p	Coefficient vector of linear parametric function for which confidence interval is needed.
alpha	Non-coverage probability of confidence interval.
type	Type of confidence interval ("lower", "upper", "both").
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 two objects:

estimate	Point estimate.
ci	Confidence interval.

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

library(MASS)
data(birthwt)
attach(birthwt)
X <- cbind(1, smoke, binaries(race))
p <- c(0,1,0,0,0)
cisngl(bwt, X, p, 0.05, type = "upper", tol = 1e-10)
cisngl(bwt, X, p, 0.05, type = "both", tol = 1e-10) 
detach(birthwt)

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