View source: R/check_heteroscedasticity.R
check_heteroscedasticity | R Documentation |
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.
check_heteroscedasticity(x, ...)
check_heteroskedasticity(x, ...)
x |
A model object. |
... |
Currently not used. |
This test of the hypothesis of (non-)constant error is also called Breusch-Pagan test (1979).
The p-value of the test statistics. A p-value < 0.05 indicates a non-constant variance (heteroskedasticity).
There is also a plot()
-method
implemented in the see-package.
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()
m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
check_heteroscedasticity(m)
# plot results
if (require("see")) {
x <- check_heteroscedasticity(m)
plot(x)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.