Automate the Creation of Generalized Additive Models (GAMs)

This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.

autogam

AutoGAM is a wrapper package for mgcv that makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, AutoGAM tries to automate as much as possible the procedure of configuring a highly accurate GAM at reasonably high speed, even for large datasets.

Installation

You can install the development version of autogam like so:

# install.packages("devtools")
devtools::install_github("tripartio/autogam")

Example

Here’s a simple example using the mtcars dataset to predict mpg:

library(autogam)

autogam(mtcars, 'mpg')
#> Detecting distribution of `mpg`...
#> Loading required package: intervals
#> 
#> Fitting GAM with `Inverse Gaussian` distribution...
#> ✔ GAM successfully fit with 86.1% standardized accuracy.
#> 
#> Family: gaussian 
#> Link function: inverse 
#> 
#> Formula:
#> mpg ~ cyl + s(disp, bs = "cr") + s(hp, bs = "cr") + s(drat, bs = "cr") + 
#>     s(wt, bs = "cr") + s(qsec, bs = "cr") + vs + am + gear + 
#>     s(carb, k = 3, bs = "cr")
#> 
#> Estimated degrees of freedom:
#> 1.00 1.00 1.00 1.00 1.37 1.00  total = 11.37 
#> 
#> fREML score: 114.8354     
#> 
#> MAE: 1.307; Std. accuracy: 86.1%