Title:	Automate the Creation of Generalized Additive Models (GAMs)
Version:	0.1.0
Language:	en-US
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.
License:	MIT + file LICENSE
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 4.2.0)
Imports:	cli, dplyr, methods, mgcv, purrr, rlang, staccuracy, stats, stringr, univariateML
Suggests:	testthat (≥ 3.0.0)
URL:	https://github.com/tripartio/autogam, https://tripartio.github.io/autogam/
BugReports:	https://github.com/tripartio/autogam/issues
NeedsCompilation:	no
Packaged:	2025-02-24 17:45:53 UTC; chitu.okoli
Author:	Chitu Okoli [aut, cre]
Maintainer:	Chitu Okoli <Chitu.Okoli@skema.edu>
Repository:	CRAN
Date/Publication:	2025-02-24 19:20:06 UTC

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.

Author(s)

Chitu Okoli Chitu.Okoli@skema.edu

See Also

Useful links:

• https://github.com/tripartio/autogam

• https://tripartio.github.io/autogam/

• Report bugs at https://github.com/tripartio/autogam/issues

Automate the creation of a Generalized Additive Model (GAM)

Description

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

Value

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

Examples

autogam(mtcars, 'mpg')

Generic autogam methods passed on to mgcv::gam methods

Description

An autogam object contains a gam element that is simply an mgcv::gam object. So, it supports all mgcv::gam methods by, in most cases, simply passing the gam element on to their corresponding mgcv::gam methods. Only the following methods have special specifications for autogam (see their dedicated documentation files for details): print.autogam().

Usage

## S3 method for class 'autogam'
anova(object, ...)

## S3 method for class 'autogam'
coef(object, ...)

## S3 method for class 'autogam'
cooks.distance(model, ...)

## S3 method for class 'autogam'
formula(x, ...)

## S3 method for class 'autogam'
influence(model, ...)

## S3 method for class 'autogam'
logLik(object, ...)

## S3 method for class 'autogam'
model.matrix(object, ...)

## S3 method for class 'autogam'
predict(object, ...)

## S3 method for class 'autogam'
residuals(object, ...)

## S3 method for class 'autogam'
vcov(object, ...)

Arguments

Value

Returns the return object of the corresponding mgcv::gam method.

Plot Method for autogam Objects

Description

This function plots an autogam object. It calls the mgcv::gam object mgcv::plot.gam() method.

Usage

## S3 method for class 'autogam'
plot(x, ...)

Arguments

Value

Same return object as mgcv::print.gam().

Print Method for autogam Objects

Description

This function prints an autogam object. It calls the mgcv::gam object print() method and then adds basic performance metrics from the autogam object:

• For models that predict numeric outcomes, it prints "MAE", the mean absolute error, and "Std. accuracy", the standardized accuracy (staccuracy) of the winsorized MAE relative to the mean absolute deviation.

• For models that predict binary outcomes, it prints "AUC", the area under the ROC curve.

Usage

## S3 method for class 'autogam'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

Create a character string for a mgcv::gam formula

Description

Create a character string that wraps appropriate variables in a dataframe with s() smooth functions. Based on the datatype of each variable, it determines whether it is a numeric variable to be smoothed:

• Non-numeric: no smoothing.

• Numeric: determine knots based on the number of unique values for that variable:

  • ⁠<= 4⁠: no smoothing

  • ⁠5 to 19⁠ (inclusive): smooth function with knots equal to the floored half of the number of unique values. E.g., 6 unique values receive 3 knots, 7 will receive 3 knots, and 8 will receive 4 knots.

  • ⁠>= 20⁠: smooth function with no specified number of knots, allowing the gam() function to detect the appropriate number.

Usage

smooth_formula_string(
  data,
  y_col,
  smooth_fun = "s",
  bs = "cr",
  expand_parametric = TRUE
)

Arguments

Value

Returns a single character string that represents a formula with y_col on the left and all other variables in data on the right, each formatted with an appropriate s() function when applicable.

Examples

smooth_formula_string(mtcars, 'mpg')

Summary Method for autogam Objects

Description

This function returns a summary of an autogam object. It calls the mgcv::gam object mgcv::summary.gam() method.

Usage

## S3 method for class 'autogam'
summary(object, ...)

Arguments

Value

Same return object as mgcv::summary.gam().