check_heteroscedasticity: 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, ...)
Value
The p-value of the test statistics. A p-value < 0.05 indicates a
non-constant variance (heteroskedasticity).
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).
References
Breusch, T. S., and Pagan, A. R. (1979) A simple test for heteroscedasticity
and random coefficient variation. Econometrica 47, 1287-1294.
See Also
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()