fitted.mvgam: Expected Values of the Posterior Predictive Distribution

Description

This method extracts posterior estimates of the fitted values (i.e. the actual predictions, included estimates for any trend states, that were obtained when fitting the model). It also includes an option for obtaining summaries of the computed draws.

Usage

# S3 method for mvgam
fitted(
  object,
  process_error = TRUE,
  scale = c("response", "linear"),
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

Value

An array of predicted mean response values. If summary = FALSE the output resembles those of posterior_epred.mvgam and predict.mvgam.

If summary = TRUE the output is an n_observations x E

matrix. The number of summary statistics E is equal to 2 + length(probs): The Estimate column contains point estimates (either mean or median depending on argument robust), while the Est.Error column contains uncertainty estimates (either standard deviation or median absolute deviation depending on argument robust). The remaining columns starting with Q contain quantile estimates as specified via argument probs.

Arguments

object: An object of class mvgam
process_error: Logical. If TRUE and a dynamic trend model was fit, expected uncertainty in the process model is accounted for by using draws from the latent trend SD parameters. If FALSE, uncertainty in the latent trend component is ignored when calculating predictions
scale: Either "response" or "linear". If "response", results are returned on the scale of the response variable. If "linear", results are returned on the scale of the linear predictor term, that is without applying the inverse link function or other transformations.
summary: Should summary statistics be returned instead of the raw values? Default is TRUE..
robust: If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.
probs: The percentiles to be computed by the quantile function. Only used if summary is TRUE.
...: Further arguments passed to prepare_predictions that control several aspects of data validation and prediction.

Details

This method gives the actual fitted values from the model (i.e. what you will see if you generate hindcasts from the fitted model using hindcast.mvgam with type = 'expected'). These predictions can be overly precise if a flexible dynamic trend component was included in the model. This is in contrast to the set of predict functions (i.e. posterior_epred.mvgam or predict.mvgam), which will assume any dynamic trend component has reached stationarity when returning hypothetical predictions

Examples

Run this code

if (FALSE) {
# Simulate some data and fit a model
simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
mod <- mvgam(y ~ s(season, bs = 'cc'),
            trend_model = 'AR1',
            data = simdat$data_train,
            chains = 2,
            burnin = 300,
            samples = 300)

# Extract fitted values (posterior expectations)
expectations <- fitted(mod)
str(expectations)
}