plot_mvgam_forecasts: Plot mvgam posterior predictions for a specified series
In mvgam: Multivariate (Dynamic) Generalized Additive Models

plot_mvgam_forecasts

R Documentation

Plot mvgam posterior predictions for a specified series

Description

Plot mvgam posterior predictions for a specified series

Usage

plot_mvgam_fc(
  object,
  series = 1,
  newdata,
  data_test,
  realisations = FALSE,
  n_realisations = 15,
  hide_xlabels = FALSE,
  xlab,
  ylab,
  ylim,
  n_cores = 1,
  return_forecasts = FALSE,
  return_score = FALSE,
  ...
)

## S3 method for class 'mvgam_forecast'
plot(
  x,
  series = 1,
  realisations = FALSE,
  n_realisations = 15,
  hide_xlabels = FALSE,
  xlab,
  ylab,
  ylim,
  return_score = FALSE,
  ...
)

Arguments

`object`	`list` object returned from `mvgam`. See `mvgam()`
`series`	`integer` specifying which series in the set is to be plotted
`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 as `data` in the original model (i.e. we assume there is no time gap between the last observation of series 1 in `data` and the first observation for series 1 in `newdata`). If `newdata` contains observations in column `y`, these observations will be used to compute a Discrete Rank Probability Score for the forecast distribution
`data_test`	Deprecated. Still works in place of `newdata` but users are recommended to use `newdata` instead for more seamless integration into `R` workflows
`realisations`	`logical`. If `TRUE`, forecast realisations are shown as a spaghetti plot, making it easier to visualise the diversity of possible forecasts. If `FALSE`, the default, empirical quantiles of the forecast distribution are shown
`n_realisations`	`integer` specifying the number of posterior realisations to plot, if `realisations = TRUE`. Ignored otherwise
`hide_xlabels`	`logical`. If `TRUE`, no xlabels are printed to allow the user to add custom labels using `axis` from base `R`
`xlab`	label for x axis.
`ylab`	label for y axis.
`ylim`	Optional `vector` of y-axis limits (min, max)
`n_cores`	`integer` specifying number of cores for generating forecasts in parallel
`return_forecasts`	`logical`. If `TRUE`, the function will plot the forecast as well as returning the forecast object (as a `matrix` of dimension `n_samples` x `horizon`)
`return_score`	`logical`. If `TRUE` and out of sample test data is provided as `newdata`, a probabilistic score will be calculated and returned. The score used will depend on the observation family from the fitted model. Discrete families (`poisson`, `negative binomial`, `tweedie`) use the Discrete Rank Probability Score. Other families use the Continuous Rank Probability Score. The value returned is the `sum` of all scores within the out of sample forecast horizon
`...`	further `par` graphical parameters.
`x`	Object of class `mvgam_forecast`

Details

plot_mvgam_fc generates posterior predictions from an object of class mvgam, calculates posterior empirical quantiles and plots them against the observed data. If realisations = FALSE, the returned plot shows 90, 60, 40 and 20 percent posterior quantiles (as ribbons of increasingly darker shades or red) as well as the posterior median (as a dark red line). If realisations = FALSE, a set of n_realisations posterior draws are shown.

plot.mvgam_forecast takes an object of class mvgam_forecast, in which forecasts have already been computed, and plots the resulting forecast distribution.

If realisations = FALSE, these posterior quantiles are plotted along with the true observed data that was used to train the model. Otherwise, a spaghetti plot is returned to show possible forecast paths.