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.
You can install the development version of autogam like so:
# install.packages("devtools")
devtools::install_github("tripartio/autogam")Here’s a simple example using the mtcars dataset to
predict mpg:
library(autogam)
ag <- autogam(mtcars, 'mpg')
summary(ag)
#>
#> Family: gaussian
#> Link function: identity
#>
#> Formula:
#> mpg ~ cyl + s(disp) + s(hp) + s(drat) + s(wt) + s(qsec) + vs +
#> am + gear + s(carb, k = 3)
#>
#> Parametric coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 7.3453 5.3267 1.379 0.2671
#> cyl 0.5814 0.5264 1.104 0.3547
#> vs 10.3131 1.7012 6.062 0.0107 *
#> am 4.9605 0.8490 5.842 0.0118 *
#> gear 0.7107 0.7857 0.905 0.4362
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Approximate significance of smooth terms:
#> edf Ref.df F p-value
#> s(disp) 1.000 1.000 4.984 0.1117
#> s(hp) 8.739 8.868 17.975 0.0170 *
#> s(drat) 1.987 2.220 16.275 0.0395 *
#> s(wt) 1.764 2.083 2.669 0.1891
#> s(qsec) 8.904 8.970 28.950 0.0089 **
#> s(carb) 1.785 1.876 1.382 0.4412
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> R-sq.(adj) = 0.996 Deviance explained = 100%
#> GCV = 1.7279 Scale est. = 0.1523 n = 32