plot_mvgam_trend: Plot latent trend predictions from 'mvgam' models

In mvgam: Multivariate (Dynamic) Generalized Additive Models

Plot latent trend predictions from mvgam models

Description

Plot latent trend predictions from mvgam models

Usage

plot_mvgam_trend(
  object,
  series = 1,
  newdata,
  data_test,
  realisations = FALSE,
  n_realisations = 15,
  n_cores = 1,
  derivatives = 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	Deprecated. Parallel processing is no longer supported
derivatives	logical. If TRUE, an additional plot will be returned to show the estimated 1st derivative for the estimated trend
xlab	Label for x axis
ylab	Label for y axis

Value

A ggplot object

Author(s)

Nicholas J Clark

Examples

## Not run: 
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
)

# 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)
plot(trend_fc, series = 2)

## End(Not run)

mvgam documentation built on Jan. 21, 2026, 9:07 a.m.