ANOVA

Compute analysis of variance (or deviance) tables for two fitted, nested uRegress objects. The model with more parameters is referred to as the full model (or the larger model), and the model with fewer parameters is referred to as the null model (or the smaller model).

Usage

# S3 method for uRegress
anova(object, full_object, test = "LRT", robustSE = TRUE, useFdstn = TRUE, ...)

Arguments

object

an object of class uRegress, the model with fewer parameters (i.e. the null model).

full_object

an object of class uRegress, the model with more parameters (i.e. the full model).

test

a character string specifying the test statistic to be used. Can be one of 'Wald' or 'LRT', which corresponds to Wald or likelihood (partial likelihood for hazard regressions) ratio tests. Note that currently the Wald test is only supported for symbolically nested models; that is, when the larger model contains all the covariates (with the same names) in the smaller model.

robustSE

a logical value indicating whether or not to use robust standard errors in calculation. Defaults to TRUE. If TRUE, then robustSE must have been TRUE when reg was created.

useFdstn

a logical indicator that the F distribution should be used for test statistics instead of the chi squared distribution. Defaults to FALSE. This option is not supported when input reg is a hazard regression (i.e., fnctl="hazard").

...

argument to be passed in

Value

A list of class anova.uRegress with the following components:

printMat

A formatted table with inferential results (i.e., test statistics and p-values) for comparing two nested models.

null model

The null model in the comparison.

full model

The full model in the comparison.

Examples

# Loading required libraries
library(sandwich)
#> Warning: package ‘sandwich’ was built under R version 4.1.2

# Reading in a dataset
data(mri)

# Linear regression of LDL on age and stroke (with robust SE by default)
testReg_null <- regress ("mean", ldl~age+stroke, data = mri)

# Linear regression of LDL on age, stroke, and race (with robust SE by default)
testReg_full <- regress ("mean", ldl~age+stroke+race, data = mri)
# Comparing the two models using the Wald test with robust SE
anova(testReg_null, testReg_full, test = "Wald")
#> Analysis of Deviance Table 
#> Null model: ldl ~ age + stroke
#> <environment: 0x7fd50ef44710>
#> 
#> Full model: ldl ~ age + stroke + race
#> <environment: 0x7fd50ef44710>
#> 
#>      F stat num df den df p value
#> [1,] 0.1619 3.0000    719   0.922