dfgam {flexOR}

dfgam: Degrees of Freedom Selection for GAM Models

Description

Computes the degrees of freedom for specified non-linear predictors in a GAM model. The user can choose between AIC (Akaike Information Criterion), AICc (AIC corrected for small sample sizes), or BIC (Bayesian Information Criterion) as the selection criteria. This function is useful for determining the appropriate degrees of freedom for smoothing terms in GAMs.

Usage

dfgam(
  response,
  nl.predictors,
  other.predictors = NULL,
  smoother = "s",
  method = "AIC",
  data,
  step = NULL
)

Arguments

response

The response variable as a formula.

nl.predictors

A character vector specifying the non-linear predictors.

other.predictors

A character vector specifying other predictors if needed.

smoother

The type of smoothing term, currently only "s" is supported.

method

The selection method, one of "AIC", "AICc", or "BIC".

data

The data frame containing the variables.

step

The step size for grid search when there are multiple non-linear predictors.

Value

A list containing the following components:

• fit: The fitted GAM model.

• df: A numeric vector of degrees of freedom for each non-linear predictor.

• method: The selection method used (AIC, AICc, or BIC).

• nl.predictors: The non-linear predictors used in the model.

• other.predictors: Other predictors used in the model if specified.

Examples

# Load dataset
library(gam)
data(PimaIndiansDiabetes2, package="mlbench");

# Calculate degrees of freedom using AIC
df2 <- dfgam(
  response="diabetes",
  nl.predictors=c("age", "mass"),
  other.predictors=c("pedigree"),
  smoother="s",
  method="AIC",
  data=PimaIndiansDiabetes2
);

print(df2$df);