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

In mvgam: Multivariate (Dynamic) Generalized Additive Models

Extract or compute hindcasts and forecasts for a fitted mvgam object

Description

Extract or compute hindcasts and forecasts for a fitted mvgam object

Usage

forecast(object, ...)

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

Arguments

object	list object returned from mvgam. See mvgam()
...	Ignored
newdata	Optional dataframe or list of test data containing at least 'series' and 'time' in addition to any other variables included in the linear predictor of the original formula. 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	integer specifying number of cores for generating forecasts in parallel
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

Details

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

Value

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

See Also

hindcast, score, ensemble

Examples

simdat <- sim_mvgam(n_series = 3, trend_model = AR())
mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
            trend_model = AR(),
            noncentred = TRUE,
            data = simdat$data_train,
            chains = 2)

# Hindcasts on response scale
hc <- hindcast(mod)
str(hc)
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)
plot(fc, series = 1)
plot(fc, series = 2)
plot(fc, series = 3)

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

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

mvgam documentation built on Sept. 11, 2024, 8:55 p.m.