Description
Automate the Creation of Generalized Additive Models (GAMs).
Description
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.
README.md
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%