gen_additive_mod: Interface for Generalized Additive Models (GAM)
In business-science/gammodels: The 'tidymodels' backend for GAM models

Description Usage Arguments Details Value Engine Details Fit Details Examples

View source: R/parsnip-gam_mod.R

Interface for Generalized Additive Models (GAM)

gen_additive_mod(
  mode = "regression",
  select_features = NULL,
  adjust_deg_free = NULL
)

`mode`	A single character string for the type of model.
`select_features`	TRUE or FALSE. If this is TRUE then can add an extra penalty to each term so that it can be penalized to zero. This means that the smoothing parameter estimation that is part of fitting can completely remove terms from the model. If the corresponding smoothing parameter is estimated as zero then the extra penalty has no effect. Use `adjust_deg_free` to increase level of penalization.
`adjust_deg_free`	If `select_features = TRUE`, then acts as a multiplier for smoothness. Increase this beyond 1 to produce smoother models.

Available Engines:

gam: Connects to mgcv::gam()

Parameter Mapping:

modelgam	mgcv::gam
select_features	select (FALSE)
adjust_deg_free	gamma (1)

A parsnip model specification

gam

This engine uses mgcv::gam() and has the following parameters, which can be modified through the parsnip::set_engine() function.

## function (formula, family = gaussian(), data = list(), weights = NULL, 
##     subset = NULL, na.action, offset = NULL, method = "GCV.Cp", optimizer = c("outer", 
##         "newton"), control = list(), scale = 0, select = FALSE, knots = NULL, 
##     sp = NULL, min.sp = NULL, H = NULL, gamma = 1, fit = TRUE, paraPen = NULL, 
##     G = NULL, in.out = NULL, drop.unused.levels = TRUE, drop.intercept = NULL, 
##     discrete = FALSE, ...)

MGCV Formula Interface

Fitting GAMs is accomplished using parameters including:

mgcv::s(): GAM spline smooths
mgcv::te(): GAM tensor product smooths

These are applied in the fit() function:

fit(value ~ s(date_mon, k = 12) + s(date_num), data = df)

library(tidymodels)
library(modelgam)
library(modeltime)
library(tidyverse)
library(timetk)
library(lubridate)

m750_extended <- m750 %>%
    group_by(id) %>%
    future_frame(.length_out = 24, .bind_data = TRUE) %>%
    mutate(lag_24 = lag(value, 24)) %>%
    ungroup() %>%
    mutate(date_num = as.numeric(date)) %>%
    mutate(date_month = month(date))

m750_train  <- m750_extended %>% drop_na()
m750_future <- m750_extended %>% filter(is.na(value))

model_fit_gam <- gen_additive_mod(mode = "regression") %>%
    set_engine("gam", family=Gamma(link="log"), method = "REML") %>%
    fit(value ~ s(date_month, k = 12) 
        + s(date_num) 
        + s(lag_24) 
        + s(date_num, date_month), 
        data = m750_train)

model_fit_gam %>% predict(m750_future, type = "numeric") 

model_fit_gam %>% predict(m750_future, type = "raw")