Note

marginaleffects now includes experimental support for everything in this package, and more! 🚀

Check out the documentation.

marginaleffectsJAX

A JAX backend for marginaleffects. Under construction!

Installation

install.packages("remotes") # if `remotes` is not installed
remotes::install_github("arcruz0/marginaleffectsJAX")

Benchmarks (very preliminary)

Setting: lm() models with $N$ observations, $y \sim \text{N}(0,1)$, and $K$ regressors, half of which are $x_k \sim \text{N}(0,1)$ and half of which are $x_k \sim \text{Bernoulli}(0.5)$. by = "var" predictions are for one of the latter. Median times over 10 replications. See benchmarks/benchmark_predictions.R for the code.

Note: Loading marginaleffectsJAX takes a few seconds. Calling a function for the first time takes more time than subsequent runs; see plot for maximum times.

Usage (very preliminary)

library(marginaleffects)
library(marginaleffectsJAX)
enable_JAX_backend()
#> JAX is now a backend for `marginaleffects`. Run `disable_JAX_backend()` to disable.

mod <- lm(mpg ~ hp + am, mtcars)

predictions(mod) |> head()
#> 
#>  Estimate Std. Error    z Pr(>|z|)     S 2.5 % 97.5 %
#>      25.4      0.818 31.0   <0.001 700.5  23.8   27.0
#>      25.4      0.818 31.0   <0.001 700.5  23.8   27.0
#>      26.4      0.850 31.1   <0.001 701.1  24.7   28.1
#>      20.1      0.775 25.9   <0.001 490.0  18.6   21.6
#>      16.3      0.677 24.0   <0.001 421.6  15.0   17.6
#>      20.4      0.796 25.6   <0.001 478.6  18.8   22.0
#> 
#> Type: response

predictions(mod, by = TRUE)
#> 
#>  Estimate Std. Error    z Pr(>|z|)   S 2.5 % 97.5 %
#>      20.1      0.514 39.1   <0.001 Inf  19.1   21.1
#> 
#> Type: response

predictions(mod, by = "am")
#> 
#>  am Estimate Std. Error    z Pr(>|z|)     S 2.5 % 97.5 %
#>   0     17.1      0.667 25.7   <0.001 481.2  15.8   18.5
#>   1     24.4      0.807 30.2   <0.001 664.5  22.8   26.0
#> 
#> Type: response

Supported marginaleffects calls (very preliminary)

(Only models of class lm are supported).

Functionality	Example call(s)	Supported?
Predictions: unit-level	predictions(mod)	✅
Predictions: aggregate	predictions(mod, by = TRUE) avg_predictions(mod)	✅
Predictions: marginal	predictions(mod, by = "var") avg_predictions(mod, by = "var") plot_predictions(mod, by = "var")	✅
Predictions: custom grid	predictions(mod, newdata = data_grid(...))	✅
Predictions: counterfactual grid	predictions(mod, newdata = data_grid(..., grid_type = "counterfactual"))	✅
Predictions: mean-or-mode grid	predictions(mod, newdata = "mean")	✅
Predictions: balanced grid	predictions(mod, newdata = "balanced"))	✅
Predictions: weighted	predictions(mod, by = "var", wt = "wvar")	🔜
Predictions: summed	predictions(mod, by = ..., byfun = sum)	🔜
Comparisons	comparisons(mod, ...)	🔜