# \donttest{
# Simulate 4 time series with hierarchical seasonality
# and a VAR(1) dynamic process
set.seed(0)
simdat <- sim_mvgam(seasonality = 'hierarchical',
trend_model = VAR(cor = TRUE),
family = gaussian())
# Fit an appropriate model
mod1 <- mvgam(y ~ s(season, bs = 'cc', k = 6),
data = simdat$data_train,
family = gaussian(),
trend_model = VAR(cor = TRUE),
chains = 2,
silent = 2)
how_to_cite(mod1)
# For a GP example, simulate data using the mgcv package
dat <- mgcv::gamSim(1, n = 30, scale = 2)
# Fit a model that uses an approximate GP from the brms package
mod2 <- mvgam(y ~ gp(x2, k = 12),
data = dat,
family = gaussian(),
chains = 2,
silent = 2)
how_to_cite(mod2)
# }
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