Generalised Additive Models

A wrapper for mgcv::gam() enabling multiple GAMs based on a grouping variable.

Usage

model_gam(
  data,
  yvar,
  family = gaussian(),
  neighbour = 0,
  s.vars,
  s.basedim = NULL,
  linear.vars = NULL,
  verbose = FALSE,
  ...
)

Arguments

data

Training data set on which models will be trained. Must be a data set of class tsibble.(Make sure there are no additional date or time related variables except for the index of the tsibble). If multiple models are fitted, the grouping variable should be the key of the tsibble. If a key is not specified, a dummy key with only one level will be created.

yvar

Name of the response variable as a character string.

family

A description of the error distribution and link function to be used in the model (see glm and family).

neighbour

If multiple models are fitted: Number of neighbours of each key (i.e. grouping variable) to be considered in model fitting to handle smoothing over the key. Should be an integer. If neighbour = x, x number of keys before the key of interest and x number of keys after the key of interest are grouped together for model fitting. The default is neighbour = 0 (i.e. no neighbours are considered for model fitting).

s.vars

A character vector of names of the predictor variables for which splines should be fitted (i.e. non-linear predictors).

s.basedim

Dimension of the bases used to represent the smooth terms corresponding to s.vars. (For more information refer mgcv::s().)

linear.vars

A character vector of names of the predictor variables that should be included linearly into the model (i.e. linear predictors).

verbose

Logical; controls whether progress messages (model indices) are printed during fitting. Defaults to FALSE.

...

Other arguments not currently used.

Value

An object of class gamFit. This is a tibble with two columns:

key

The level of the grouping variable (i.e. key of the training data set).

fit

Information of the fitted model corresponding to the key.

Each row of the column fit is an object of class gam. For details refer mgcv::gamObject.

See also

model_smimodel, model_backward, model_gaim, model_ppr, model_lm

Examples

library(dplyr)
library(tibble)
library(tidyr)
library(tsibble)

# Simulate data
n = 1005
set.seed(123)
sim_data <- tibble(x_lag_000 = runif(n)) |>
  mutate(
    # Add x_lags
    x_lag = lag_matrix(x_lag_000, 5)) |>
  unpack(x_lag, names_sep = "_") |>
  mutate(
    # Response variable
    y = (0.9*x_lag_000 + 0.6*x_lag_001 + 0.45*x_lag_003)^3 + rnorm(n, sd = 0.1),
    # Add an index to the data set
    inddd = seq(1, n)) |>
  drop_na() |>
  select(inddd, y, starts_with("x_lag")) |>
  # Make the data set a `tsibble`
  as_tsibble(index = inddd)

# Predictors taken as non-linear variables
s.vars <- colnames(sim_data)[3:6]

# Predictors taken as linear variables
linear.vars <- colnames(sim_data)[7:8]

# Model fitting
gamModel <- model_gam(data = sim_data,
                      yvar = "y",
                      s.vars = s.vars,
                      linear.vars = linear.vars)

# Fitted model
gamModel$fit[[1]]
#> 
#> Family: gaussian 
#> Link function: identity 
#> 
#> Formula:
#> y ~ s(x_lag_000, bs = "cr") + s(x_lag_001, bs = "cr") + s(x_lag_002, 
#>     bs = "cr") + s(x_lag_003, bs = "cr") + x_lag_004 + x_lag_005
#> 
#> Estimated degrees of freedom:
#> 4.62 3.51 1.39 2.41  total = 14.93 
#> 
#> REML score: 397.9157