I'm fitting a model with a 2-level categorical and a numerical predictor. As far as I'm concerned, I cannot use ```car::LeveneTest()``` to assess homoscedasticity since I have a numerical predictor, right? **What should I use instead to assess homoscedasticity on both numerical and categorical predictors?**

* expected error: 

```
car::leveneTest(lmer(SCORE ~ EXAM_GRADE * TASK_TYPE + (1|ID), data = dfModels1, REML = F))

Error in leveneTest.default(lmer(SCORE ~ EXAM_GRADE  * TASK_TYPE + (1 |  : 
  lmer(SCORE ~ EXAM_GRADE  * TASK_TYPE + (1 | ID), data = dfModels1, REML = F) is not a numeric variable
```

* head(dfModels1): 

Each ```ID``` has a ```SCORE``` on two different ```TASKs```. They also have a score on another ```Exam Grade```. The basic idea is that ```Exam Grade``` predicts ```SCORE``` independenly of the task type. 

```
ID      SCORE    EXAM_GRADE    TASK_TYPE    
  <chr>  <dbl>    <dbl>            <fct>         
1 1_101   7.1      100             A          
2 1_101   4.92     100             N   
1 2_101   7.4      36              A         
2 2_101   7        36              N   
```

* Maybe the [Breusch-Pagan test][1] (such as ```performance::check_heteroscedasticity()``` does?) 

* Obs: I'm aware that I should prefer visual inspection instead, I'm also doing that, but **I'd like to find a way to assess the assumption numerically as well just in case**. 

  [1]: https://stats.stackexchange.com/questions/280628/differences-between-breusch-pagan-test-and-levenes-test