# check_heteroscedasticity: Check model for (non-)constant error variance In performance: Assessment of Regression Models Performance

 check_heteroscedasticity R Documentation

## Check model for (non-)constant error variance

### Description

Significance testing for linear regression models assumes that the model errors (or residuals) have constant variance. If this assumption is violated the p-values from the model are no longer reliable.

### Usage

``````check_heteroscedasticity(x, ...)

check_heteroskedasticity(x, ...)
``````

### Arguments

 `x` A model object. `...` Currently not used.

### Details

This test of the hypothesis of (non-)constant error is also called Breusch-Pagan test (1979).

### Value

The p-value of the test statistics. A p-value < 0.05 indicates a non-constant variance (heteroskedasticity).

### Note

There is also a `plot()`-method implemented in the see-package.

### References

Breusch, T. S., and Pagan, A. R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287-1294.

Other functions to check model assumptions and and assess model quality: `check_autocorrelation()`, `check_collinearity()`, `check_convergence()`, `check_homogeneity()`, `check_model()`, `check_outliers()`, `check_overdispersion()`, `check_predictions()`, `check_singularity()`, `check_zeroinflation()`

### Examples

``````m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
check_heteroscedasticity(m)

# plot results
if (require("see")) {
x <- check_heteroscedasticity(m)
plot(x)
}
``````

performance documentation built on Nov. 2, 2023, 5:48 p.m.