anova2: Comparison of nested Generalized Linear Models
In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

anova2

R Documentation

Comparison of nested Generalized Linear Models

Description

Allows to compare nested generalized linear models using Wald, score, gradient, and likelihood ratio tests.

Usage

anova2(
  object,
  ...,
  test = c("wald", "lr", "score", "gradient"),
  verbose = TRUE
)

Arguments

`object`	an object of the class glm which is obtained from the fit of a generalized linear model.
`...`	another objects of the class glm which are obtained from the fit of generalized linear models.
`test`	an (optional) character string indicating the required type of test. The available options are: Wald ("wald"), Rao's score ("score"), Terrell's gradient ("gradient"), and likelihood ratio ("lr") tests. By default, `test` is set to be "wald".
`verbose`	an (optional) logical indicating if should the report of results be printed. By default, `verbose` is set to be TRUE.

Details

The Wald, Rao's score and Terrell's gradient tests are performed using the expected Fisher information matrix.

Value

A matrix with three columns which contains the following:

Chi: The value of the statistic of the test.
Df: The number of degrees of freedom.
Pr(>Chi): The p-value of the test computed using the Chi-square distribution.

References

Buse A. (1982) The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note. The American Statistician 36, 153-157.

Terrell G.R. (2002) The gradient statistic. Computing Science and Statistics 34, 206 – 215.

Examples

## Example 1
Auto <- ISLR::Auto
fit1 <- glm(mpg ~ weight, family=inverse.gaussian("log"), data=Auto)
fit2 <- update(fit1, . ~ . + horsepower)
fit3 <- update(fit2, . ~ . + horsepower:weight)
anova2(fit1, fit2, fit3, test="lr")
anova2(fit1, fit2, fit3, test="score")
anova2(fit1, fit2, fit3, test="wald")
anova2(fit1, fit2, fit3, test="gradient")

## Example 2
burn1000 <- aplore3::burn1000
mod <- death ~ age + tbsa + inh_inj
fit1 <- glm(mod, family=binomial("logit"), data=burn1000)
fit2 <- update(fit1, . ~ . + inh_inj + age*inh_inj + tbsa*inh_inj)
anova2(fit1, fit2, test="lr")
anova2(fit1, fit2, test="score")
anova2(fit1, fit2, test="wald")
anova2(fit1, fit2, test="gradient")

## Example 3
data(aucuba)
fit <- glm(lesions ~ 1 + time, family=poisson("log"), data=aucuba)
anova2(fit, test="lr")
anova2(fit, test="score")
anova2(fit, test="wald")
anova2(fit, test="gradient")

glmtoolbox documentation built on Oct. 10, 2023, 9:06 a.m.