coeff: Computation of the ordinary confidence intervals and P-values...

In rqlm: Modified Poisson, Logistic and Least-Squares Regressions for Binary Outcome and Their Generalizations

Computation of the ordinary confidence intervals and P-values using the model variance estimator

Description

Confidence intervals and P-values for the linear regression model and the generalized linear model can be calculated using the ordinary model variance estimators. Through simply entering the output objects of lm or glm, the inference results are fastly computed. For the linear regression model, the exact confidence intervals and P-values based on the t-distribution are calculated. Also, for the generalized linear model, the Wald-type confidence intervals and P-values based on the asymptotic normal approximation are computed. The resultant coefficients and confidence limits can be transformed to exponential scales by specifying eform.

Usage

coeff(gm, eform=FALSE, cl=0.95, digits=4)

Arguments

gm	An output object of lm or glm.
eform	A logical value that specify whether the outcome should be transformed by exponential function (default: FALSE)
cl	Confidence level for calculating confidence intervals (default: 0.95)
digits	Number of decimal places in the output (default: 4).

Value

Results of inferences of the regression coefficients using the ordinary model variance estimators.

• coef: Coefficient estimates; transformed to the exponential scale if eform=TRUE.

• SE: Robust standard error estimates for coef.

• CL: Lower limits of confidence intervals.

• CU: Upper limits of confidence intervals.

• P-value: P-values for the coefficient tests.

Examples

data(exdata02)

gm1 <- glm(y ~ x1 + x2 + x3 + x4, data=exdata02, family=binomial)
coeff(gm1,eform=TRUE)
# Logistic regression analysis
# Coefficient estimates are translated to odds ratio scales

lm1 <- lm(x1 ~ x2 + x3 + x4, data=exdata02)
coeff(lm1)
# Linear regression analysis

data(mch)

if(requireNamespace("lme4", quietly = TRUE)) {
    lmr1 <- lme4::lmer(Y ~ x + (1|SOUM), data=mch)
    coeff(lmr1)
}
# Linear mixed-effects model analysis

if(requireNamespace("lme4", quietly = TRUE)) {
	gmr1 <- lme4::glmer(y ~ x + (1|SOUM), nAGQ=25, family=binomial, data=mch)
	coeff(gmr1, eform=TRUE)
}
# Logistic mixed-effects model analysis
# Coefficient estimates are translated to odds ratio scales

rqlm documentation built on Nov. 23, 2025, 5:06 p.m.