(re)move vignettes

vignettes/introduction_comparisons_1.Rmd → WIP/introduction_comparisons_1.Rmd

@@ -147,7 +147,7 @@ By default, when all focal terms are categorical, a pairwise comparison is perfo
 test_predictions(model1, "episode") # same as test_predictions(my_predictions)
 ```
 
-For our quantity of interest, the contrast between episode `r ht1$episode[1]`, we see the value `r round(ht1$Contrast[1], 2)`, which is exactly the difference between the predicted outcome for `episode = 1` (`r round(my_predictions$predicted[1], 2)`) and `episode = 2` (`r round(my_predictions$predicted[2], 2)`). The related p-value is `r round(ht1$p.value[1], 3)`, indicating that the difference between the predicted values of our outcome at these two levels of the factor _episode_ is indeed statistically significant.
+For our quantity of interest, the contrast between episode `r ht1$episode[1]`, we see the value `r round(ht1$Difference[1], 2)`, which is exactly the difference between the predicted outcome for `episode = 1` (`r round(my_predictions$predicted[1], 2)`) and `episode = 2` (`r round(my_predictions$predicted[2], 2)`). The related p-value is `r round(ht1$p.value[1], 3)`, indicating that the difference between the predicted values of our outcome at these two levels of the factor _episode_ is indeed statistically significant.
 
 Since the `terms` argument in `test_predictions()` works in the same way as for `predict_response()`, you can directly pass "representative values" via that argument (for details, see [this vignette](https://strengejacke.github.io/ggeffects/articles/introduction_effectsatvalues.html)). For example, we could also specify the levels of `episode` directly, to simplify the output:
 
@@ -368,4 +368,4 @@ Thanks to the great *marginaleffects* package, it is now possible to have a powe
 
 While the current implementation in `test_predictions()` already covers many common use cases for testing contrasts and pairwise comparison, there still might be the need for more sophisticated comparisons. In this case, I recommend using the [*marginaleffects*](https://marginaleffects.com/) package directly. Some further related recommended readings are the vignettes about [Comparisons](https://marginaleffects.com/vignettes/comparisons.html) or [Hypothesis Tests](https://marginaleffects.com/vignettes/hypothesis.html).
 
-[Go to next vignette: **Significance Testing of Differences Between Predictions II: Comparisons of Slopes, Floodlight and Spotlight Analysis (Johnson-Neyman Intervals)**](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_2.html)
+[Go to next vignette: **Significance Testing of Differences Between Predictions II: Comparisons of Slopes, Floodlight and Spotlight Analysis (Johnson-Neyman Intervals)**](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_2.html)

vignettes/content.Rmd

@@ -27,15 +27,6 @@ The documentation of the *ggeffects* package, including many examples, is [avail
 - [Adjusted Predictions for Random Effects Models](https://strengejacke.github.io/ggeffects/articles/introduction_randomeffects.html)
 - [(Cluster) Robust Standard Errors](https://strengejacke.github.io/ggeffects/articles/practical_robustestimation.html)
 
-## Interpretation and Significance Testing
-
-- [Contrasts and Pairwise Comparisons](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_1.html)
-- [Floodlight and Spotlight Analysis (Johnson-Neyman Intervals)](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_2.html)
-- [Contrasts and Pairwise Comparisons for GLM](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_3.html)
-- [Contrasts and Pairwise Comparisons for Zero-Inflation Models](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_4.html)
-- [Different Backends for Pairwise Comparisons](https://strengejacke.github.io/ggeffects/articles/introduction_comparisons_5.html)
-- [Adding Partial Residuals to Effects Plots](https://strengejacke.github.io/ggeffects/articles/introduction_partial_residuals.html)
-
 ## Creating and customizing plots and tables
 
 - [Plotting Adjusted Predictions](https://strengejacke.github.io/ggeffects/articles/introduction_plotmethod.html)