predict.gamFit: Obtaining forecasts on a test set from a fitted 'gamFit'

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Obtaining forecasts on a test set from a fitted gamFit

Description

Gives forecasts on a test set.

Usage

## S3 method for class 'gamFit'
predict(
  object,
  newdata,
  exclude.trunc = NULL,
  recursive = FALSE,
  recursive_colRange = NULL,
  ...
)

Arguments

object	A gamFit object.
newdata	The set of new data on for which the forecasts are required (i.e. test set; should be a tsibble).
exclude.trunc	The names of the predictor variables that should not be truncated for stable predictions as a character string. (Since the nonlinear functions are estimated using splines, extrapolation is not desirable. Hence, if any predictor variable in newdata that is treated non-linearly in the estimated model, will be truncated to be in the in-sample range before obtaining predictions. If any variables are listed here will be excluded from such truncation.)
recursive	Whether to obtain recursive forecasts or not (default - FALSE).
recursive_colRange	If recursive = TRUE, the range of column numbers in newdata to be filled with forecasts. Recursive/autoregressive forecasting is required when the lags of the response variable itself are used as predictor variables into the model. Make sure such lagged variables are positioned together in increasing lag order (i.e. lag_1, lag_2, ..., lag_m, lag_m = maximum lag used) in newdata, with no break in the lagged variable sequence even if some of the intermediate lags are not used as predictors.
...	Other arguments not currently used.

Value

A tsibble with forecasts on test set.

Examples

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

# Simulate data
n = 1015
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)
  
# Training set
sim_train <- sim_data[1:1000, ]
# Test set
sim_test <- sim_data[1001:1010, ]

# 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_train,
                      yvar = "y",
                      s.vars = s.vars,
                      linear.vars = linear.vars)

predict(object = gamModel, newdata = sim_test)

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