Automate the creation of a Generalized Additive Model (GAM)

autogam() is a wrapper for 'mgcv::gam()' that makes it easier to create high-performing Generalized Additive Models (GAMs). 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.

Usage

autogam(data, y_col, ..., bs = "cr")

Arguments

data: dataframe. All the variables in data will be used to predict y_col. To exclude any variables, assign as data only the subset of variables desired.
y_col: character(1). Name of the y outcome variable.
...: Arguments passed on to mgcv::gam().
bs: character(1). The default basis function for GAM smooths. See ?mgcv::smooth.terms for details. Whereas the default bs in mgcv is 'tp', autogam's default is 'cr', which is much faster and comparably accurate.

Value

Returns an mgcv::gam object, the result of predicting y_col from all other variables in data.

Examples

autogam(mtcars, 'mpg')
#> Detecting distribution of `mpg`...
#> 
#> ✔ GAM successfully fit with 90.6% standardized accuracy.
#> 
#> 
#> Family: gaussian 
#> Link function: identity 
#> 
#> 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:
#> 5.45 1.00 1.79 1.00 1.00 1.00  total = 16.24 
#> 
#> fREML score: 49.8224     
#> 
#> MAE: 0.883; Std. accuracy: 90.6%