Compare the predictive strength of two independent variables in a minimal linear (mixed effects) regression model. The function creates two identical
`lm`

or `lmer`

objects, only differing in fixed effects structure. Then, using the Akaike Information Criterion, the better predictor is determined. A model
is assumed to have a better fit, if its AIC is 2 points lower than the other's.

```
predictor_competition2(
data,
dependent,
independent1,
independent2,
random.intercept = NULL,
random.slope = 1
)
```

## Arguments

- data
The original data set for both models.

- dependent
The dependent variable for both models.

- independent1
The independent variable(s), i.e. the fixed effects, of the 1st model.

- independent2
The independent variable(s), i.e. the fixed effects, of the 2nd model.

- random.intercept
The random intercept for both models. If not random intercept is specified, regular linear models are fitted.

- random.slope
The random slope for both models. The default assumes no random slope.

## Value

A dataframe containing df and AIC. Usually used without variable assignment.

## References

Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.

## Author

D. Schmitz & J. Esser

## Examples

```
data("data_s")
predictor_competition2(data = data_s, dependent = "sDur", independent1 = "pauseBin", independent2 = "typeOfS")
#> ℹ We have a winner - it's pauseBin!
#> df AIC
#> mdl1 3 -484.5026
#> mdl2 4 -462.1716
```