Interface for Generalized Additive Models (GAM)

Interface for Generalized Additive Models (GAM)

gen_additive_reg(
  mode = "regression",
  markov_chains = NULL,
  chain_iter = NULL,
  warmup_iter = NULL,
  adapt_delta = NULL
)

Arguments

mode	A single character string for the type of model.
markov_chains	Number of Markov chains (defaults to 4).
chain_iter	Number of total iterations per chain (including warmup; defaults to 2000).
warmup_iter	A positive integer specifying number of warmup (aka burnin) iterations. This also specifies the number of iterations used for stepsize adaptation, so warmup samples should not be used for inference. The number of warmup should not be larger than iter and the default is iter/2.
adapt_delta	The thin of the jumps in a HMC method.

Value

A parsnip model specification

A model spec

Details

Available Engines:

• stan: Connects to brms::brm()

Engine Details

stan

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

## function (formula, data, family = gaussian(), prior = NULL, autocor = NULL, data2 = NULL, cov_ranef = NULL, sample_prior = "no", 
##     sparse = NULL, knots = NULL, stanvars = NULL, stan_funs = NULL, fit = NA, save_pars = NULL, save_ranef = NULL, save_mevars = NULL, 
##     save_all_pars = NULL, inits = "random", chains = 4, iter = 2000, warmup = floor(iter/2), thin = 1, cores = getOption("mc.cores", 
##         1), threads = NULL, normalize = getOption("brms.normalize", TRUE), control = NULL, algorithm = getOption("brms.algorithm", 
##         "sampling"), backend = getOption("brms.backend", "rstan"), future = getOption("future", FALSE), silent = 1, seed = NA, save_model = NULL, 
##     stan_model_args = list(), file = NULL, file_refit = "never", empty = FALSE, rename = TRUE, ...)

Fit Details

BRMS Formula Interface

Fitting GAMs is accomplished using parameters including:

• brms::s(): GAM spline smooths

• brms::t2(): GAM tensor product smooths

These are applied in the fit() function:

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

Examples

if (FALSE) {
library(tidymodels)
library(bayesmodels)
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_reg(mode = "regression", markov_chains = 2) %>%
    set_engine("stan", family=Gamma(link="log")) %>%
    fit(value ~  date + s(date_month, k = 12)
        + s(lag_24),
        data = m750_train) 
 }