Plot mvgam latent trend for a specified series

Usage

plot_mvgam_trend(
  object,
  series = 1,
  newdata,
  data_test,
  realisations = FALSE,
  n_realisations = 15,
  n_cores = 1,
  derivatives = FALSE,
  hide_xlabels = FALSE,
  xlab,
  ylab,
  ...
)

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.
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, posterior trend realisations are shown as a spaghetti plot, making it easier to visualise the diversity of possible trend paths. If FALSE, the default, empirical quantiles of the posterior distribution are shown
n_realisations: integer specifying the number of posterior realisations to plot, if realisations = TRUE. Ignored otherwise
n_cores: integer specifying number of cores for generating trend forecasts in parallel
derivatives: logical. If TRUE, an additional plot will be returned to show the estimated 1st derivative for the estimated trend
hide_xlabels: logical. If TRUE, no xlabels are printed to allow the user to add custom labels using axis from base R. Ignored if derivatives = TRUE
xlab: label for x axis.
ylab: label for y axis.
...: further par graphical parameters.

Value

A base R graphics plot

Examples

# \donttest{
simdat <- sim_mvgam(n_series = 3, trend_model = 'AR1')
mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
            trend_model = AR(),
            noncentred = TRUE,
            data = simdat$data_train,
            chains = 2)
#> Compiling Stan program using cmdstanr
#> 
#> Start sampling
#> Running MCMC with 2 parallel chains...
#> 
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#> Chain 2 finished in 2.5 seconds.
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#> Chain 1 finished in 2.7 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 2.6 seconds.
#> Total execution time: 2.8 seconds.
#> 

# Plot estimated trends for some series
plot_mvgam_trend(mod)

plot_mvgam_trend(mod, series = 2)


# Extrapolate trends forward in time and plot on response scale
plot_mvgam_trend(mod, newdata = simdat$data_test)

plot_mvgam_trend(mod, newdata = simdat$data_test, series = 2)


# But it is recommended to compute extrapolations for all series
# first and then plot
trend_fc <- forecast(mod, newdata = simdat$data_test)
plot(trend_fc, series = 1)
#> Out of sample DRPS:
#> 23.048637

plot(trend_fc, series = 2)
#> Out of sample DRPS:
#> 10.984458

# }