augment.gamFit: Augment function for class 'gamFit'

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Augment function for class gamFit

Description

Generates residuals and fitted values of a fitted gamFit object.

Usage

## S3 method for class 'gamFit'
augment(x, ...)

Arguments

x	A gamFit object.
...	Other arguments not currently used.

Value

A tibble.

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)

# Obtain residuals and fitted values
augment(gamModel)

smimodel documentation built on April 8, 2026, 5:06 p.m.