From 50351a3a4e574de64065e4d4522798d016e58211 Mon Sep 17 00:00:00 2001
From: Nicholas Clark Authors
Scott Pease. Contributor. +
See ??mvgam_families
for more information. Below is a simple example for simulating and modelling proportional data with Beta
observations over a set of seasonal series with independent Gaussian Process dynamic trends:
-data <- sim_mvgam(family = betar(),
+set.seed(100)
+data <- sim_mvgam(family = betar(),
T = 80,
trend_model = GP(),
- trend_rel = 0.5,
+ prop_trend = 0.5,
seasonality = 'shared')
Plot the series to see how they evolve over time
plot_mvgam_series(data = data$data_train, series = 'all')
Fit a State-Space GAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics
+ +Fit a State-Space GAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics
mod <- mvgam(y ~ s(season, bs = 'cc', k = 7) +
s(season, by = series, m = 1, k = 5),
diff --git a/docs/reference/figures/README-unnamed-chunk-12-1.png b/docs/reference/figures/README-unnamed-chunk-12-1.png
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