Plot posterior forecast predictions from mvgam models
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 mvgam_forecast
plot(
x,
series = 1,
realisations = FALSE,
n_realisations = 15,
hide_xlabels = FALSE,
xlab,
ylab,
ylim,
return_score = FALSE,
...
)
A base R graphics plot and an optional list containing the forecast distribution
and the out of sample probabilistic forecast score
list object of class mvgam. See mvgam()
integer specifying which series in the set is to be plotted
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
Deprecated. Still works in place of newdata but users are recommended to use
newdata instead for more seamless integration into R workflows
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
integer specifying the number of posterior realisations to plot, if
realisations = TRUE. Ignored otherwise
logical. If TRUE, no xlabels are printed to allow the user to add custom labels using
axis from base R
label for x axis.
label for y axis.
Optional vector of y-axis limits (min, max)
integer specifying number of cores for generating forecasts in parallel
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)
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.
Object of class mvgam_forecast
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.