tabset

book/articles/marginaleffects.qmd

@@ -14,22 +14,37 @@ n_support <- nrow(dat)
 
 ## Installation
 
+::: {.panel-tabset}
+### R
+
 Install the latest CRAN release:
 
-```{r, eval=FALSE}
+```{r}
+#| eval: false
 install.packages("marginaleffects")
 ```
 
 Install the development version:
 
-```{r, eval=FALSE}
+```{r}
+#| eval: false
 install.packages(
     c("marginaleffects", "insight"),
     repos = c("https://vincentarelbundock.r-universe.dev", "https://easystats.r-universe.dev"))
 ```
 
 *Restart `R` completely before moving on.*
 
+### Python
+
+Install from PyPI:
+
+```{python}
+#| eval: false
+pip install marginaleffects
+```
+:::
+
 
 ## Estimands: Predictions, Comparisons, and Slopes
 
@@ -68,14 +83,30 @@ The `marginaleffects` package includes functions to estimate, average, plot, and
 
 We now apply `marginaleffects` functions to compute each of the estimands described above. First, we fit a linear regression model with multiplicative interactions:
 
+::: {.panel-tabset}
+### R
 ```{r}
 library(marginaleffects)
 
 mod <- lm(mpg ~ hp * wt * am, data = mtcars)
 ```
+### Python
+```{python}
+import polars as pl
+import numpy as np
+import statsmodels.formula.api as smf
+from marginaleffects import *
+
+mtcars = pl.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/datasets/mtcars.csv")
+
+mod = smf.ols("mpg ~ hp * wt * am", data = mtcars).fit()
+```
+:::
 
 Then, we call the `predictions()` function. As noted above, predictions are unit-level estimates, so there is one specific prediction per observation. By default, the `predictions()` function makes one prediction per observation in the dataset that was used to fit the original model. Since `mtcars` has 32 rows, the `predictions()` outcome also has 32 rows:
 
+::: {.panel-tabset}
+### R
 ```{r}
 pre <- predictions(mod)
 
@@ -85,80 +116,179 @@ nrow(pre)
 
 pre
 ```
+### Python
+
+```{python}
+pre = predictions(mod)
+
+mtcars.shape
+
+pre.shape
+
+print(pre)
+```
+:::
 
 Now, we use the `comparisons()` function to compute the difference in predicted outcome when each of the predictors is incremented by 1 unit (one predictor at a time, holding all others constant). Once again, comparisons are unit-level quantities. And since there are 3 predictors in the model and our data has 32 rows, we obtain 96 comparisons:
 
+::: {.panel-tabset}
+### R
 ```{r}
 cmp <- comparisons(mod)
 
 nrow(cmp)
 
 cmp
 ```
+### Python
+```{python}
+cmp = comparisons(mod)
+
+cmp.shape
+
+print(cmp)
+```
+:::
 
 The `comparisons()` function allows customized queries. For example, what happens to the predicted outcome when the `hp` variable increases from 100 to 120?
 
+
+::: {.panel-tabset}
+### R
 ```{r}
 comparisons(mod, variables = list(hp = c(120, 100)))
 ```
+### Python
+```{python}
+cmp = comparisons(mod, variables = {"hp": [120, 100]})
+print(cmp)
+```
+:::
 
 What happens to the predicted outcome when the `wt` variable increases by 1 standard deviation about its mean?
 
+::: {.panel-tabset}
+### R
 ```{r}
 comparisons(mod, variables = list(hp = "sd"))
 ```
+### Python
+```{python}
+cmp = comparisons(mod, variables = {"hp": "sd"})
+print(cmp)
+```
+:::
 
 The `comparisons()` function also allows users to specify arbitrary functions of predictions, with the `comparison` argument. For example, what is the average ratio between predicted Miles per Gallon after an increase of 50 units in Horsepower?
 
+
+::: {.panel-tabset}
+### R
 ```{r}
 comparisons(
   mod,
   variables = list(hp = 50),
   comparison = "ratioavg")
 ```
+### Python
+```{python}
+cmp = comparisons(
+  mod,
+  variables = {"hp": 50},
+  comparison = "ratioavg")
+print(cmp)
+```
+:::
 
 See the [Comparisons vignette for detailed explanations and more options.](comparisons.html)
 
 The `slopes()` function allows us to compute the partial derivative of the outcome equation with respect to each of the predictors. Once again, we obtain a data frame with 96 rows:
 
+::: {.panel-tabset}
+### R
 ```{r}
 mfx <- slopes(mod)
 
 nrow(mfx)
 
 mfx
 ```
+### Python
+```{python}
+mfx = slopes(mod)
+
+mfx.shape
+
+print(mfx)
+```
+:::
 
 ## Grid
 
 Predictions, comparisons, and slopes are typically "conditional" quantities which depend on the values of all the predictors in the model. By default, `marginaleffects` functions estimate quantities of interest for the empirical distribution of the data (i.e., for each row of the original dataset). However, users can specify the exact values of the predictors they want to investigate by using the `newdata` argument.
 
 `newdata` accepts data frames, shortcut strings, or a call to the `datagrid()` function. For example, to compute the predicted outcome for a hypothetical car with all predictors equal to the sample mean or median, we can do:
 
+::: {.panel-tabset}
+### R
 ```{r}
 predictions(mod, newdata = "mean")
 
 predictions(mod, newdata = "median")
 ```
 
+### Python
+```{python}
+p = predictions(mod, newdata = "mean")
+print(p)
+
+p = predictions(mod, newdata = "median")
+print(p)
+```
+:::
+
 The [`datagrid` function gives us a powerful way to define a grid of predictors.](https://vincentarelbundock.github.io/marginaleffects/reference/datagrid.html) All the variables not mentioned explicitly in `datagrid()` are fixed to their mean or mode:
 
+::: {.panel-tabset}
+### R
 ```{r}
 predictions(
   mod,
   newdata = datagrid(
     am = c(0, 1),
     wt = range))
 ```
+### Python
+```{python}
+p = predictions(
+  mod,
+  newdata = datagrid(
+    mod,
+    am = [0, 1],
+    wt = [mtcars["wt"].min(), mtcars["wt"].max()]))
+print(p)
+```
+:::
 
 The same mechanism is available in `comparisons()` and `slopes()`. To estimate the partial derivative of `mpg` with respect to `wt`, when `am` is equal to 0 and 1, while other predictors are held at their means:
 
+::: {.panel-tabset}
+### R
 ```{r}
 slopes(
   mod,
   variables = "wt",
   newdata = datagrid(am = 0:1))
 ```
+### Python
+```{python}
+s = slopes(
+  mod,
+  variables = "wt",
+  newdata = datagrid(mod, am = [0, 1]))
+print(s)
+```
+:::
 
 We can also plot how predictions, comparisons, or slopes change across different values of the predictors using [three powerful plotting functions:](plot.html)
 
@@ -188,21 +318,44 @@ Since predictions, comparisons, and slopes are conditional quantities, they can
 
 To marginalize (average over) our unit-level estimates, we can use the `by` argument or the one of the convenience functions: `avg_predictions()`, `avg_comparisons()`, or `avg_slopes()`. For example, both of these commands give us the same result: the average predicted outcome in the `mtcars` dataset:
 
+::: {.panel-tabset}
+### R
 ```{r}
 avg_predictions(mod)
 ```
+### Python
+```{python}
+p = avg_predictions(mod)
+print(p)
+```
+:::
 
 This is equivalent to manual computation by:
 
+::: {.panel-tabset}
+### R
 ```{r}
 mean(predict(mod))
 ```
+### Python
+```{python}
+np.mean(mod.predict())
+```
+:::
 
 The main `marginaleffects` functions all include a `by` argument, which allows us to marginalize within sub-groups of the data. For example,
 
+::: {.panel-tabset}
+### R
 ```{r}
 avg_comparisons(mod, by = "am")
 ```
+### Python
+```{python}
+cmp = avg_comparisons(mod, by = "am")
+print(cmp)
+```
+:::
 
 Marginal Means are a special case of predictions, which are marginalized (or averaged) across a balanced grid of categorical predictors. To illustrate, we estimate a new model with categorical predictors: