coeff {rqlm} | R Documentation |
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 |
eform |
A logical value that specify whether the outcome should be transformed by exponential function (default: |
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 ifeform=TRUE
. -
SE
: Robust standard error estimates forcoef
. -
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