predict_gam: Obtaining recursive forecasts on a test set from a fitted...

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Obtaining recursive forecasts on a test set from a fitted mgcv::gam

Description

Gives recursive forecasts on a test set.

Usage

predict_gam(
  object,
  newdata,
  exclude.trunc = NULL,
  recursive = FALSE,
  recursive_colRange = NULL,
  ...
)

Arguments

object	A gam 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 tibble with forecasts on test set.

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