mvgam (version 1.1.51)

plot_mvgam_trend: 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
)

Value

A ggplot object

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.

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)

# 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)
# }