Arguments
Value
@@ -96,10 +84,11 @@ Value
Details
- A total of four ggplot plots are generated to examine posterior median
+
A total of four ggplot plots are generated to examine posterior
Dunn-Smyth residuals for the specified series. Plots include a residuals vs fitted values plot,
a Q-Q plot, and two plots to check for any remaining temporal autocorrelation in the residuals.
-Note, all plots use only posterior medians of fitted values / residuals, so uncertainty is not represented.
+Note, all plots use only report statistics from a sample of up to 20 posterior
+draws (to save computational time), so uncertainty in these relationships may not be adequately represented.
Author
@@ -109,56 +98,19 @@ Author<
Examples
- # \donttest{
+ if (FALSE) {
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)
-#> Compiling Stan program using cmdstanr
-#>
-#> Start sampling
-#> Running MCMC with 2 parallel chains...
-#>
-#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
-#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup)
-#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
-#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup)
-#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
-#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
-#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup)
-#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
-#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup)
-#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
-#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup)
-#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
-#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup)
-#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling)
-#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
-#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling)
-#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
-#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling)
-#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
-#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling)
-#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
-#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling)
-#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
-#> Chain 1 finished in 2.2 seconds.
-#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling)
-#> Chain 2 finished in 2.2 seconds.
-#>
-#> Both chains finished successfully.
-#> Mean chain execution time: 2.2 seconds.
-#> Total execution time: 2.7 seconds.
-#>
+ chains = 2,
+ silent = 2)
# Plot Dunn Smyth residuals for some series
plot_mvgam_resids(mod)
-
plot_mvgam_resids(mod, series = 2)
-
-# }
+}
Details
-A total of four ggplot plots are generated to examine posterior median +
A total of four ggplot plots are generated to examine posterior Dunn-Smyth residuals for the specified series. Plots include a residuals vs fitted values plot, a Q-Q plot, and two plots to check for any remaining temporal autocorrelation in the residuals. -Note, all plots use only posterior medians of fitted values / residuals, so uncertainty is not represented.
+Note, all plots use only report statistics from a sample of up to 20 posterior +draws (to save computational time), so uncertainty in these relationships may not be adequately represented.Author
@@ -109,56 +98,19 @@Author<
Examples
- # \donttest{
+ if (FALSE) {
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)
-#> Compiling Stan program using cmdstanr
-#>
-#> Start sampling
-#> Running MCMC with 2 parallel chains...
-#>
-#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
-#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup)
-#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
-#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup)
-#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
-#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
-#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup)
-#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
-#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup)
-#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
-#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup)
-#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
-#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup)
-#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling)
-#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
-#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling)
-#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
-#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling)
-#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
-#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling)
-#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
-#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling)
-#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
-#> Chain 1 finished in 2.2 seconds.
-#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling)
-#> Chain 2 finished in 2.2 seconds.
-#>
-#> Both chains finished successfully.
-#> Mean chain execution time: 2.2 seconds.
-#> Total execution time: 2.7 seconds.
-#>
+ chains = 2,
+ silent = 2)
# Plot Dunn Smyth residuals for some series
plot_mvgam_resids(mod)
-
plot_mvgam_resids(mod, series = 2)
-
-# }
+}
Examples
-# \donttest{
+ if (FALSE) {
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)
-#> Compiling Stan program using cmdstanr
-#>
-#> Start sampling
-#> Running MCMC with 2 parallel chains...
-#>
-#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
-#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup)
-#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
-#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup)
-#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
-#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
-#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup)
-#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
-#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup)
-#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
-#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup)
-#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
-#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup)
-#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling)
-#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
-#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling)
-#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
-#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling)
-#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
-#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling)
-#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
-#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling)
-#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
-#> Chain 1 finished in 2.2 seconds.
-#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling)
-#> Chain 2 finished in 2.2 seconds.
-#>
-#> Both chains finished successfully.
-#> Mean chain execution time: 2.2 seconds.
-#> Total execution time: 2.7 seconds.
-#>
+ chains = 2,
+ silent = 2)
# Plot Dunn Smyth residuals for some series
plot_mvgam_resids(mod)
-
plot_mvgam_resids(mod, series = 2)
-
-# }
+}