forecast.mvgam: Extract or compute hindcasts and forecasts for a fitted `mvgam` object

Description

Extract or compute hindcasts and forecasts for a fitted mvgam object

Usage

# S3 method for mvgam
forecast(object, newdata, data_test, n_cores = 1, type = "response", ...)

Value

An object of class mvgam_forecast containing hindcast and forecast distributions. See mvgam_forecast-class for details.

Arguments

object: list object of class mvgam or jsdgam. See mvgam()
newdata: Optional dataframe or list of test data containing the same variables that were included in the original data used to fit the model. If included, the covariate information in newdata will be used to generate forecasts from the fitted model equations. If this same newdata was originally included in the call to mvgam, then forecasts have already been produced by the generative model and these will simply be extracted and plotted. However if no newdata was supplied to the original model call, an assumption is made that the newdata supplied here comes sequentially after the data supplied in the original model (i.e. we assume there is no time gap between the last observation of series 1 in the original data and the first observation for series 1 in newdata)
data_test: Deprecated. Still works in place of newdata but users are recommended to use newdata instead for more seamless integration into R workflows
n_cores: Deprecated. Parallel processing is no longer supported
type: When this has the value link (default) the linear predictor is calculated on the link scale. If expected is used, predictions reflect the expectation of the response (the mean) but ignore uncertainty in the observation process. When response is used, the predictions take uncertainty in the observation process into account to return predictions on the outcome scale. When variance is used, the variance of the response with respect to the mean (mean-variance relationship) is returned. When type = "terms", each component of the linear predictor is returned separately in the form of a list (possibly with standard errors, if summary = TRUE): this includes parametric model components, followed by each smooth component, but excludes any offset and any intercept. Two special cases are also allowed: type latent_N will return the estimated latent abundances from an N-mixture distribution, while type detection will return the estimated detection probability from an N-mixture distribution
...: Ignored

Details

Posterior predictions are drawn from the fitted mvgam and used to simulate a forecast distribution

Examples

Run this code

if (FALSE) {
  # Simulate data with 3 series and AR trend model
  simdat <- sim_mvgam(n_series = 3, trend_model = AR())

  # Fit mvgam model
  mod <- mvgam(
    y ~ s(season, bs = 'cc', k = 6),
    trend_model = AR(),
    noncentred = TRUE,
    data = simdat$data_train,
    chains = 2,
    silent = 2
  )

  # Hindcasts on response scale
  hc <- hindcast(mod)
  str(hc)

  # Use summary() to extract hindcasts / forecasts for custom plotting
  head(summary(hc), 12)

  # Or just use the plot() function for quick plots
  plot(hc, series = 1)
  plot(hc, series = 2)
  plot(hc, series = 3)

  # Forecasts on response scale
  fc <- forecast(
    mod,
    newdata = simdat$data_test
  )
  str(fc)
  head(summary(fc), 12)
  plot(fc, series = 1)
  plot(fc, series = 2)
  plot(fc, series = 3)

  # Forecasts as expectations
  fc <- forecast(
    mod,
    newdata = simdat$data_test,
    type = 'expected'
  )
  head(summary(fc), 12)
  plot(fc, series = 1)
  plot(fc, series = 2)
  plot(fc, series = 3)

  # Dynamic trend extrapolations
  fc <- forecast(
    mod,
    newdata = simdat$data_test,
    type = 'trend'
  )
  head(summary(fc), 12)
  plot(fc, series = 1)
  plot(fc, series = 2)
  plot(fc, series = 3)
}