`R/check_heteroscedasticity.R`

`check_heteroscedasticity.Rd`

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. |

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

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

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.

```
m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
check_heteroscedasticity(m)
#> Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.042).
#>
# plot results
if (require("see")) {
x <- check_heteroscedasticity(m)
plot(x)
}
#> Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.042).
#>
```