Obtaining forecasts on a test set from a fitted gaimFit

Gives forecasts on a test set.

Usage

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

Arguments

object: A gaimFit 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 index variables
index.vars <- colnames(sim_data)[3:7]

# Assign group indices for each predictor
index.ind = c(rep(1, 3), rep(2, 2))

# Predictors taken as non-linear variables not entering indices
s.vars = "x_lag_005"

# Model fitting
gaimModel <- model_gaim(data = sim_train,
                        yvar = "y",
                        index.vars = index.vars,
                        index.ind = index.ind,
                        s.vars = s.vars)
                        
predict(object = gaimModel, newdata = sim_test)
#> # A tsibble: 10 x 10 [1]
#> # Key:       dummy_key [1]
#>    inddd       y x_lag_000 x_lag_001 x_lag_002 x_lag_003 x_lag_004 x_lag_005
#>    <int>   <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
#>  1  1006  1.13      0.478     0.848     0.853     0.160     0.594     0.274 
#>  2  1007  2.72      0.774     0.478     0.848     0.853     0.160     0.594 
#>  3  1008  1.54      0.295     0.774     0.478     0.848     0.853     0.160 
#>  4  1009  0.0723    0.0656    0.295     0.774     0.478     0.848     0.853 
#>  5  1010  0.401     0.441     0.0656    0.295     0.774     0.478     0.848 
#>  6  1011  0.524     0.462     0.441     0.0656    0.295     0.774     0.478 
#>  7  1012  0.0927    0.341     0.462     0.441     0.0656    0.295     0.774 
#>  8  1013  0.310     0.185     0.341     0.462     0.441     0.0656    0.295 
#>  9  1014  0.279     0.507     0.185     0.341     0.462     0.441     0.0656
#> 10  1015 -0.0138    0.0192    0.507     0.185     0.341     0.462     0.441 
#> # ℹ 2 more variables: dummy_key <dbl>, .predict <dbl>