| cisngl {lmreg} | R Documentation |
Confidence interval for a linear parametric function in a linear model
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 <shairiksengupta@gmail.com>, Jinwen Qiu <qjwsnow_ctw@hotmail.com>
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)