anova.mcglm: Anova Tables

In mcglm: Multivariate Covariance Generalized Linear Models

Anova Tables

Description

Performs Wald tests of the significance for the linear predictor components by response variables. This function is useful for joint hypothesis tests of regression coefficients associated with categorical covariates with more than two levels. It is not designed for model comparison.

Usage

## S3 method for class 'mcglm'
anova(object, ...)

Arguments

object	an object of class mcglm, usually, a result of a call to mcglm() function.
...	additional arguments affecting the summary produced. Note that there is no extra options for mcglm object class.

Value

A data.frame with Chi-square statistic to test the null hypothesis of a parameter, or a set of parameters, be zero. Degree of freedom (Df) and p-values. The Wald test based on the observed covariance matrix of the parameters is used.

Author(s)

Wagner Hugo Bonat, [e-mail]

Examples

x1 <- seq(-1, 1, l = 100)
x2 <- gl(5, 20)
beta <- c(5, 0, -2, -1, 1, 2)
X <- model.matrix(~ x1 + x2)
set.seed(123)
y <- rnorm(100, mean = X%*%beta, sd = 1)
data = data.frame("y" = y, "x1" = x1, "x2" = x2)
fit.anova <- mcglm(c(y ~ x1 + x2), list(mc_id(data)), data = data)
anova(fit.anova)

mcglm documentation built on Sept. 16, 2022, 1:06 a.m.