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Usage 
code(object)
 
 # S3 method for mvgam_prefit
-stancode(object, ...)
+stancode(object, ...)
 
 # S3 method for mvgam
-stancode(object, ...)
+stancode(object, ...)
 
 # S3 method for mvgam_prefit
-standata(object, ...)
+standata(object, ...)
@@ -98,14 +98,14 @@

Value

Examples

simdat <- sim_mvgam()
-mod <- mvgam(y ~ s(season) +
-               s(time, by = series),
+mod <- mvgam(y ~ s(season) +
+               s(time, by = series),
              family = poisson(),
              data = simdat$data_train,
              run_model = FALSE)
 
 # View Stan model code
-stancode(mod)
+stancode(mod)
 #> // Stan model code generated by package mvgam
 #> data {
 #>   int<lower=0> total_obs; // total number of observations
@@ -186,7 +186,7 @@ 
Examples#> 
 
 # View Stan model data
-sdata <- standata(mod)
+sdata <- standata(mod)
 str(sdata)
 #> List of 21
 #>  $ y           : num [1:75, 1:3] 1 1 3 0 0 0 0 0 0 2 ...
@@ -195,7 +195,7 @@ 
Examples#>   ..- attr(*, "dimnames")=List of 2
 #>   .. ..$ : NULL
 #>   .. ..$ : chr [1:37] "X.Intercept." "V2" "V3" "V4" ...
-#>  $ S1          : num [1:9, 1:18] 3.758 -0.625 -1.816 0.191 2.357 ...
+#>  $ S1          : num [1:9, 1:18] 3.819 0.796 1.727 -0.323 2.686 ...
 #>  $ zero        : num [1:37] 0 0 0 0 0 0 0 0 0 0 ...
 #>  $ S2          : num [1:9, 1:18] 8.555 -1.22 4.352 0.822 -5.594 ...
 #>  $ S3          : num [1:9, 1:18] 8.555 -1.22 4.352 0.822 -5.594 ...
diff --git a/docs/reference/ensemble.mvgam_forecast-2.png b/docs/reference/ensemble.mvgam_forecast-2.png
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diff --git a/docs/reference/ensemble.mvgam_forecast.html b/docs/reference/ensemble.mvgam_forecast.html
index 4fce3a98..cd779799 100644
--- a/docs/reference/ensemble.mvgam_forecast.html
+++ b/docs/reference/ensemble.mvgam_forecast.html
@@ -128,13 +128,26 @@ 
Examples
 m1 <- mvgam(y ~ 1,
             trend_formula = ~ time +
-              s(season, bs = 'cc', k = 9),
+              s(season, bs = 'cc', k = 9),
             trend_model = AR(p = 1),
             noncentred = TRUE,
             data = simdat$data_train,
             newdata = simdat$data_test)
 #> Compiling Stan program using cmdstanr
 #> 
+#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
+#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
+#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:359,
+#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
+#>                  from stan/lib/stan_math/stan/math/rev.hpp:16,
+#>                  from stan/lib/stan_math/stan/math.hpp:19,
+#>                  from stan/src/stan/model/model_header.hpp:4,
+#>                  from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f869091ff5.hpp:2:
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
+#>   194 |       if (cdf_n < 0.0)
+#>       | 
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory
 #> Start sampling
 #> Running MCMC with 4 parallel chains...
 #> 
@@ -143,57 +156,57 @@ 
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+#> Chain 1 finished in 7.0 seconds.
 #> 
 #> All 4 chains finished successfully.
-#> Mean chain execution time: 13.0 seconds.
-#> Total execution time: 14.4 seconds.
+#> Mean chain execution time: 6.0 seconds.
+#> Total execution time: 7.1 seconds.
 #> 
 
 m2 <- mvgam(y ~ time,
@@ -203,6 +216,19 @@ 
Examples            newdata = simdat$data_test)
 #> Compiling Stan program using cmdstanr
 #> 
+#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
+#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
+#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:359,
+#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
+#>                  from stan/lib/stan_math/stan/math/rev.hpp:16,
+#>                  from stan/lib/stan_math/stan/math.hpp:19,
+#>                  from stan/src/stan/model/model_header.hpp:4,
+#>                  from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f8254fb78.hpp:2:
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
+#>   194 |       if (cdf_n < 0.0)
+#>       | 
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory
 #> Start sampling
 #> Running MCMC with 4 parallel chains...
 #> 
@@ -219,49 +245,49 @@ 
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+#> Mean chain execution time: 1.7 seconds.
+#> Total execution time: 2.1 seconds.
 #> 
 
 # Calculate forecast distributions for each model
@@ -274,188 +300,188 @@ 
Examples# Plot forecasts
 plot(fc1)
 #> Out of sample DRPS:
-#> 54.0632615
+#> 53.167638
 
 plot(fc2)
 #> Out of sample DRPS:
-#> 51.26894275
+#> 50.908428
 
 plot(ensemble_fc)
 #> Out of sample DRPS:
-#> 46.72201688
+#> 45.89366292
 
 
 # Score forecasts
 score(fc1)
 #> $series_1
 #>        score in_interval interval_width eval_horizon score_type
-#> 1  0.8143275           1            0.9            1       crps
-#> 2  0.4279998           1            0.9            2       crps
-#> 3  1.6240035           1            0.9            3       crps
-#> 4  0.9180020           1            0.9            4       crps
-#> 5  1.8852135           1            0.9            5       crps
-#> 6  4.0027665           1            0.9            6       crps
-#> 7  3.3081108           1            0.9            7       crps
-#> 8  2.5482525           1            0.9            8       crps
-#> 9  1.2880938           1            0.9            9       crps
-#> 10 2.5379395           1            0.9           10       crps
-#> 11 2.2626678           1            0.9           11       crps
-#> 12 0.6289923           1            0.9           12       crps
-#> 13 0.8154938           1            0.9           13       crps
-#> 14 0.7501920           1            0.9           14       crps
-#> 15 0.8248095           1            0.9           15       crps
-#> 16 1.6553963           1            0.9           16       crps
-#> 17 4.3406725           1            0.9           17       crps
-#> 18 1.2399425           1            0.9           18       crps
-#> 19 3.1618508           1            0.9           19       crps
-#> 20 6.2502788           1            0.9           20       crps
-#> 21 3.7396068           1            0.9           21       crps
-#> 22 5.7143398           1            0.9           22       crps
-#> 23 1.1730595           1            0.9           23       crps
-#> 24 1.3452073           1            0.9           24       crps
-#> 25 0.8060430           1            0.9           25       crps
+#> 1  0.8890420           1            0.9            1       crps
+#> 2  0.4492823           1            0.9            2       crps
+#> 3  1.5185570           1            0.9            3       crps
+#> 4  0.8989273           1            0.9            4       crps
+#> 5  1.8055245           1            0.9            5       crps
+#> 6  3.9993945           0            0.9            6       crps
+#> 7  3.4538378           1            0.9            7       crps
+#> 8  2.4664340           1            0.9            8       crps
+#> 9  1.3232315           1            0.9            9       crps
+#> 10 2.5988078           1            0.9           10       crps
+#> 11 2.2445383           1            0.9           11       crps
+#> 12 0.5999212           1            0.9           12       crps
+#> 13 0.8349463           1            0.9           13       crps
+#> 14 0.7671573           1            0.9           14       crps
+#> 15 0.8676385           1            0.9           15       crps
+#> 16 1.7038020           1            0.9           16       crps
+#> 17 4.3611705           1            0.9           17       crps
+#> 18 1.2247887           1            0.9           18       crps
+#> 19 3.0207840           1            0.9           19       crps
+#> 20 6.0145475           1            0.9           20       crps
+#> 21 3.5215620           1            0.9           21       crps
+#> 22 5.4205837           1            0.9           22       crps
+#> 23 1.0779020           1            0.9           23       crps
+#> 24 1.3068293           1            0.9           24       crps
+#> 25 0.7984283           1            0.9           25       crps
 #> 
 #> $all_series
 #>        score eval_horizon score_type
-#> 1  0.8143275            1   sum_crps
-#> 2  0.4279998            2   sum_crps
-#> 3  1.6240035            3   sum_crps
-#> 4  0.9180020            4   sum_crps
-#> 5  1.8852135            5   sum_crps
-#> 6  4.0027665            6   sum_crps
-#> 7  3.3081108            7   sum_crps
-#> 8  2.5482525            8   sum_crps
-#> 9  1.2880938            9   sum_crps
-#> 10 2.5379395           10   sum_crps
-#> 11 2.2626678           11   sum_crps
-#> 12 0.6289923           12   sum_crps
-#> 13 0.8154938           13   sum_crps
-#> 14 0.7501920           14   sum_crps
-#> 15 0.8248095           15   sum_crps
-#> 16 1.6553963           16   sum_crps
-#> 17 4.3406725           17   sum_crps
-#> 18 1.2399425           18   sum_crps
-#> 19 3.1618508           19   sum_crps
-#> 20 6.2502788           20   sum_crps
-#> 21 3.7396068           21   sum_crps
-#> 22 5.7143398           22   sum_crps
-#> 23 1.1730595           23   sum_crps
-#> 24 1.3452073           24   sum_crps
-#> 25 0.8060430           25   sum_crps
+#> 1  0.8890420            1   sum_crps
+#> 2  0.4492823            2   sum_crps
+#> 3  1.5185570            3   sum_crps
+#> 4  0.8989273            4   sum_crps
+#> 5  1.8055245            5   sum_crps
+#> 6  3.9993945            6   sum_crps
+#> 7  3.4538378            7   sum_crps
+#> 8  2.4664340            8   sum_crps
+#> 9  1.3232315            9   sum_crps
+#> 10 2.5988078           10   sum_crps
+#> 11 2.2445383           11   sum_crps
+#> 12 0.5999212           12   sum_crps
+#> 13 0.8349463           13   sum_crps
+#> 14 0.7671573           14   sum_crps
+#> 15 0.8676385           15   sum_crps
+#> 16 1.7038020           16   sum_crps
+#> 17 4.3611705           17   sum_crps
+#> 18 1.2247887           18   sum_crps
+#> 19 3.0207840           19   sum_crps
+#> 20 6.0145475           20   sum_crps
+#> 21 3.5215620           21   sum_crps
+#> 22 5.4205837           22   sum_crps
+#> 23 1.0779020           23   sum_crps
+#> 24 1.3068293           24   sum_crps
+#> 25 0.7984283           25   sum_crps
 #> 
 score(fc2)
 #> $series_1
 #>        score in_interval interval_width eval_horizon score_type
-#> 1  0.7433395           1            0.9            1       crps
-#> 2  1.6796785           1            0.9            2       crps
-#> 3  3.2520813           1            0.9            3       crps
-#> 4  1.9027748           1            0.9            4       crps
-#> 5  1.8418340           1            0.9            5       crps
-#> 6  2.0997740           1            0.9            6       crps
-#> 7  1.1650383           1            0.9            7       crps
-#> 8  1.7061112           1            0.9            8       crps
-#> 9  1.3908383           1            0.9            9       crps
-#> 10 1.2300728           1            0.9           10       crps
-#> 11 2.4948715           1            0.9           11       crps
-#> 12 2.1891583           1            0.9           12       crps
-#> 13 1.7893270           1            0.9           13       crps
-#> 14 1.8230665           1            0.9           14       crps
-#> 15 1.6008420           1            0.9           15       crps
-#> 16 1.5409188           1            0.9           16       crps
-#> 17 3.7453850           1            0.9           17       crps
-#> 18 2.1944927           1            0.9           18       crps
-#> 19 2.2025950           1            0.9           19       crps
-#> 20 1.6104808           1            0.9           20       crps
-#> 21 1.7263915           1            0.9           21       crps
-#> 22 2.4336692           1            0.9           22       crps
-#> 23 1.9008523           1            0.9           23       crps
-#> 24 3.9485318           1            0.9           24       crps
-#> 25 3.0568180           1            0.9           25       crps
+#> 1  0.6874878           1            0.9            1       crps
+#> 2  1.6471578           1            0.9            2       crps
+#> 3  3.0363160           1            0.9            3       crps
+#> 4  1.7709870           1            0.9            4       crps
+#> 5  1.7643665           1            0.9            5       crps
+#> 6  1.9566545           1            0.9            6       crps
+#> 7  1.1733258           1            0.9            7       crps
+#> 8  1.7089578           1            0.9            8       crps
+#> 9  1.3833360           1            0.9            9       crps
+#> 10 1.2870628           1            0.9           10       crps
+#> 11 2.6310383           1            0.9           11       crps
+#> 12 2.1334693           1            0.9           12       crps
+#> 13 1.7572383           1            0.9           13       crps
+#> 14 1.8325305           1            0.9           14       crps
+#> 15 1.6366635           1            0.9           15       crps
+#> 16 1.5081563           1            0.9           16       crps
+#> 17 3.9940062           1            0.9           17       crps
+#> 18 2.3000615           1            0.9           18       crps
+#> 19 2.2467020           1            0.9           19       crps
+#> 20 1.6397138           1            0.9           20       crps
+#> 21 1.7773023           1            0.9           21       crps
+#> 22 2.4609862           1            0.9           22       crps
+#> 23 1.9270123           1            0.9           23       crps
+#> 24 3.8463365           1            0.9           24       crps
+#> 25 2.8015595           1            0.9           25       crps
 #> 
 #> $all_series
 #>        score eval_horizon score_type
-#> 1  0.7433395            1   sum_crps
-#> 2  1.6796785            2   sum_crps
-#> 3  3.2520813            3   sum_crps
-#> 4  1.9027748            4   sum_crps
-#> 5  1.8418340            5   sum_crps
-#> 6  2.0997740            6   sum_crps
-#> 7  1.1650383            7   sum_crps
-#> 8  1.7061112            8   sum_crps
-#> 9  1.3908383            9   sum_crps
-#> 10 1.2300728           10   sum_crps
-#> 11 2.4948715           11   sum_crps
-#> 12 2.1891583           12   sum_crps
-#> 13 1.7893270           13   sum_crps
-#> 14 1.8230665           14   sum_crps
-#> 15 1.6008420           15   sum_crps
-#> 16 1.5409188           16   sum_crps
-#> 17 3.7453850           17   sum_crps
-#> 18 2.1944927           18   sum_crps
-#> 19 2.2025950           19   sum_crps
-#> 20 1.6104808           20   sum_crps
-#> 21 1.7263915           21   sum_crps
-#> 22 2.4336692           22   sum_crps
-#> 23 1.9008523           23   sum_crps
-#> 24 3.9485318           24   sum_crps
-#> 25 3.0568180           25   sum_crps
+#> 1  0.6874878            1   sum_crps
+#> 2  1.6471578            2   sum_crps
+#> 3  3.0363160            3   sum_crps
+#> 4  1.7709870            4   sum_crps
+#> 5  1.7643665            5   sum_crps
+#> 6  1.9566545            6   sum_crps
+#> 7  1.1733258            7   sum_crps
+#> 8  1.7089578            8   sum_crps
+#> 9  1.3833360            9   sum_crps
+#> 10 1.2870628           10   sum_crps
+#> 11 2.6310383           11   sum_crps
+#> 12 2.1334693           12   sum_crps
+#> 13 1.7572383           13   sum_crps
+#> 14 1.8325305           14   sum_crps
+#> 15 1.6366635           15   sum_crps
+#> 16 1.5081563           16   sum_crps
+#> 17 3.9940062           17   sum_crps
+#> 18 2.3000615           18   sum_crps
+#> 19 2.2467020           19   sum_crps
+#> 20 1.6397138           20   sum_crps
+#> 21 1.7773023           21   sum_crps
+#> 22 2.4609862           22   sum_crps
+#> 23 1.9270123           23   sum_crps
+#> 24 3.8463365           24   sum_crps
+#> 25 2.8015595           25   sum_crps
 #> 
 score(ensemble_fc)
 #> $series_1
 #>        score in_interval interval_width eval_horizon score_type
-#> 1  0.7595093           1            0.9            1       crps
-#> 2  0.7922652           1            0.9            2       crps
-#> 3  2.2211150           1            0.9            3       crps
-#> 4  1.3336568           1            0.9            4       crps
-#> 5  1.8597782           1            0.9            5       crps
-#> 6  2.9674017           1            0.9            6       crps
-#> 7  1.8333617           1            0.9            7       crps
-#> 8  1.6352802           1            0.9            8       crps
-#> 9  1.2077633           1            0.9            9       crps
-#> 10 1.6902492           1            0.9           10       crps
-#> 11 2.2163758           1            0.9           11       crps
-#> 12 1.1881601           1            0.9           12       crps
-#> 13 0.9893994           1            0.9           13       crps
-#> 14 0.9830449           1            0.9           14       crps
-#> 15 0.9830781           1            0.9           15       crps
-#> 16 1.4820704           1            0.9           16       crps
-#> 17 3.9777785           1            0.9           17       crps
-#> 18 1.5867608           1            0.9           18       crps
-#> 19 2.0606823           1            0.9           19       crps
-#> 20 3.2035984           1            0.9           20       crps
-#> 21 2.5161035           1            0.9           21       crps
-#> 22 3.9363800           1            0.9           22       crps
-#> 23 1.4755001           1            0.9           23       crps
-#> 24 2.3872868           1            0.9           24       crps
-#> 25 1.4354171           1            0.9           25       crps
+#> 1  0.7546147           1            0.9            1       crps
+#> 2  0.8195527           1            0.9            2       crps
+#> 3  2.1283856           1            0.9            3       crps
+#> 4  1.2097069           1            0.9            4       crps
+#> 5  1.7376459           1            0.9            5       crps
+#> 6  2.8649248           1            0.9            6       crps
+#> 7  1.8178390           1            0.9            7       crps
+#> 8  1.5547210           1            0.9            8       crps
+#> 9  1.1941840           1            0.9            9       crps
+#> 10 1.7886700           1            0.9           10       crps
+#> 11 2.3734945           1            0.9           11       crps
+#> 12 1.1552394           1            0.9           12       crps
+#> 13 0.9968969           1            0.9           13       crps
+#> 14 0.9847705           1            0.9           14       crps
+#> 15 1.0202320           1            0.9           15       crps
+#> 16 1.4975177           1            0.9           16       crps
+#> 17 4.1781860           1            0.9           17       crps
+#> 18 1.6047802           1            0.9           18       crps
+#> 19 2.0475912           1            0.9           19       crps
+#> 20 3.0501099           1            0.9           20       crps
+#> 21 2.3956491           1            0.9           21       crps
+#> 22 3.7320900           1            0.9           22       crps
+#> 23 1.4265935           1            0.9           23       crps
+#> 24 2.2222251           1            0.9           24       crps
+#> 25 1.3380422           1            0.9           25       crps
 #> 
 #> $all_series
 #>        score eval_horizon score_type
-#> 1  0.7595093            1   sum_crps
-#> 2  0.7922652            2   sum_crps
-#> 3  2.2211150            3   sum_crps
-#> 4  1.3336568            4   sum_crps
-#> 5  1.8597782            5   sum_crps
-#> 6  2.9674017            6   sum_crps
-#> 7  1.8333617            7   sum_crps
-#> 8  1.6352802            8   sum_crps
-#> 9  1.2077633            9   sum_crps
-#> 10 1.6902492           10   sum_crps
-#> 11 2.2163758           11   sum_crps
-#> 12 1.1881601           12   sum_crps
-#> 13 0.9893994           13   sum_crps
-#> 14 0.9830449           14   sum_crps
-#> 15 0.9830781           15   sum_crps
-#> 16 1.4820704           16   sum_crps
-#> 17 3.9777785           17   sum_crps
-#> 18 1.5867608           18   sum_crps
-#> 19 2.0606823           19   sum_crps
-#> 20 3.2035984           20   sum_crps
-#> 21 2.5161035           21   sum_crps
-#> 22 3.9363800           22   sum_crps
-#> 23 1.4755001           23   sum_crps
-#> 24 2.3872868           24   sum_crps
-#> 25 1.4354171           25   sum_crps
+#> 1  0.7546147            1   sum_crps
+#> 2  0.8195527            2   sum_crps
+#> 3  2.1283856            3   sum_crps
+#> 4  1.2097069            4   sum_crps
+#> 5  1.7376459            5   sum_crps
+#> 6  2.8649248            6   sum_crps
+#> 7  1.8178390            7   sum_crps
+#> 8  1.5547210            8   sum_crps
+#> 9  1.1941840            9   sum_crps
+#> 10 1.7886700           10   sum_crps
+#> 11 2.3734945           11   sum_crps
+#> 12 1.1552394           12   sum_crps
+#> 13 0.9968969           13   sum_crps
+#> 14 0.9847705           14   sum_crps
+#> 15 1.0202320           15   sum_crps
+#> 16 1.4975177           16   sum_crps
+#> 17 4.1781860           17   sum_crps
+#> 18 1.6047802           18   sum_crps
+#> 19 2.0475912           19   sum_crps
+#> 20 3.0501099           20   sum_crps
+#> 21 2.3956491           21   sum_crps
+#> 22 3.7320900           22   sum_crps
+#> 23 1.4265935           23   sum_crps
+#> 24 2.2222251           24   sum_crps
+#> 25 1.3380422           25   sum_crps
 #> 
 # }
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See alsoExamples

# \donttest{
 simdat <- sim_mvgam(n_series = 3, trend_model = AR())
-mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
+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
 #> 
+#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
+#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
+#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:359,
+#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
+#>                  from stan/lib/stan_math/stan/math/rev.hpp:16,
+#>                  from stan/lib/stan_math/stan/math.hpp:19,
+#>                  from stan/src/stan/model/model_header.hpp:4,
+#>                  from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f85c0c3ea4.hpp:2:
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
+#>   194 |       if (cdf_n < 0.0)
+#>       | 
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory
 #> 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 1 Iteration: 200 / 1000 [ 20%]  (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 1 Iteration: 400 / 1000 [ 40%]  (Warmup) 
 #> Chain 2 Iteration: 500 / 1000 [ 50%]  (Warmup) 
 #> Chain 2 Iteration: 501 / 1000 [ 50%]  (Sampling) 
-#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
+#> Chain 1 Iteration: 500 / 1000 [ 50%]  (Warmup) 
+#> Chain 1 Iteration: 501 / 1000 [ 50%]  (Sampling) 
 #> Chain 2 Iteration: 600 / 1000 [ 60%]  (Sampling) 
-#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
+#> Chain 1 Iteration: 600 / 1000 [ 60%]  (Sampling) 
 #> Chain 2 Iteration: 700 / 1000 [ 70%]  (Sampling) 
+#> Chain 1 Iteration: 700 / 1000 [ 70%]  (Sampling) 
 #> Chain 2 Iteration: 800 / 1000 [ 80%]  (Sampling) 
 #> Chain 1 Iteration: 800 / 1000 [ 80%]  (Sampling) 
 #> Chain 2 Iteration: 900 / 1000 [ 90%]  (Sampling) 
 #> Chain 1 Iteration: 900 / 1000 [ 90%]  (Sampling) 
 #> Chain 2 Iteration: 1000 / 1000 [100%]  (Sampling) 
-#> Chain 2 finished in 2.1 seconds.
+#> Chain 2 finished in 0.9 seconds.
 #> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
-#> Chain 1 finished in 2.2 seconds.
+#> Chain 1 finished in 1.1 seconds.
 #> 
 #> Both chains finished successfully.
-#> Mean chain execution time: 2.1 seconds.
-#> Total execution time: 2.3 seconds.
+#> Mean chain execution time: 1.0 seconds.
+#> Total execution time: 1.2 seconds.
 #> 
 
 # Hindcasts on response scale
@@ -184,7 +197,7 @@ 
Examplesstr(hc)
 #> List of 15
 #>  $ call              :Class 'formula'  language y ~ s(season, bs = "cc", k = 6)
-#>   .. ..- attr(*, ".Environment")=<environment: 0x00000134260eede0> 
+#>   .. ..- attr(*, ".Environment")=<environment: 0x00000251dbbb3bb8> 
 #>  $ trend_call        : NULL
 #>  $ family            : chr "poisson"
 #>  $ trend_model       :List of 4
@@ -206,15 +219,15 @@ 
Examples#>  $ test_observations : NULL
 #>  $ test_times        : NULL
 #>  $ hindcasts         :List of 3
-#>   ..$ series_1: num [1:1000, 1:75] 2 5 0 2 1 0 2 1 0 0 ...
+#>   ..$ series_1: num [1:1000, 1:75] 3 0 1 1 0 0 0 3 0 2 ...
 #>   .. ..- attr(*, "dimnames")=List of 2
 #>   .. .. ..$ : NULL
 #>   .. .. ..$ : chr [1:75] "ypred[1,1]" "ypred[2,1]" "ypred[3,1]" "ypred[4,1]" ...
-#>   ..$ series_2: num [1:1000, 1:75] 0 1 0 0 1 2 0 1 0 1 ...
+#>   ..$ series_2: num [1:1000, 1:75] 0 0 1 0 0 0 0 0 0 0 ...
 #>   .. ..- attr(*, "dimnames")=List of 2
 #>   .. .. ..$ : NULL
 #>   .. .. ..$ : chr [1:75] "ypred[1,2]" "ypred[2,2]" "ypred[3,2]" "ypred[4,2]" ...
-#>   ..$ series_3: num [1:1000, 1:75] 0 2 0 1 1 0 0 5 1 1 ...
+#>   ..$ series_3: num [1:1000, 1:75] 0 2 0 0 1 1 0 0 1 0 ...
 #>   .. ..- attr(*, "dimnames")=List of 2
 #>   .. .. ..$ : NULL
 #>   .. .. ..$ : chr [1:75] "ypred[1,3]" "ypred[2,3]" "ypred[3,3]" "ypred[4,3]" ...
@@ -235,7 +248,7 @@ 
Examplesstr(fc)
 #> List of 16
 #>  $ call              :Class 'formula'  language y ~ s(season, bs = "cc", k = 6)
-#>   .. ..- attr(*, ".Environment")=<environment: 0x00000134260eede0> 
+#>   .. ..- attr(*, ".Environment")=<environment: 0x00000251dbbb3bb8> 
 #>  $ trend_call        : NULL
 #>  $ family            : chr "poisson"
 #>  $ family_pars       : NULL
@@ -261,34 +274,34 @@ 
Examples#>   ..$ series_3: int [1:25] 1 0 1 0 0 0 0 0 3 0 ...
 #>  $ test_times        : int [1:25] 76 77 78 79 80 81 82 83 84 85 ...
 #>  $ hindcasts         :List of 3
-#>   ..$ series_1: num [1:1000, 1:75] 2 5 0 2 1 0 2 1 0 0 ...
+#>   ..$ series_1: num [1:1000, 1:75] 3 0 1 1 0 0 0 3 0 2 ...
 #>   .. ..- attr(*, "dimnames")=List of 2
 #>   .. .. ..$ : NULL
 #>   .. .. ..$ : chr [1:75] "ypred[1,1]" "ypred[2,1]" "ypred[3,1]" "ypred[4,1]" ...
-#>   ..$ series_2: num [1:1000, 1:75] 0 1 0 0 1 2 0 1 0 1 ...
+#>   ..$ series_2: num [1:1000, 1:75] 0 0 1 0 0 0 0 0 0 0 ...
 #>   .. ..- attr(*, "dimnames")=List of 2
 #>   .. .. ..$ : NULL
 #>   .. .. ..$ : chr [1:75] "ypred[1,2]" "ypred[2,2]" "ypred[3,2]" "ypred[4,2]" ...
-#>   ..$ series_3: num [1:1000, 1:75] 0 2 0 1 1 0 0 5 1 1 ...
+#>   ..$ series_3: num [1:1000, 1:75] 0 2 0 0 1 1 0 0 1 0 ...
 #>   .. ..- attr(*, "dimnames")=List of 2
 #>   .. .. ..$ : NULL
 #>   .. .. ..$ : chr [1:75] "ypred[1,3]" "ypred[2,3]" "ypred[3,3]" "ypred[4,3]" ...
 #>  $ forecasts         :List of 3
-#>   ..$ series_1: int [1:1000, 1:25] 8 3 3 3 2 2 6 1 1 1 ...
-#>   ..$ series_2: int [1:1000, 1:25] 0 1 1 5 0 2 2 2 0 0 ...
-#>   ..$ series_3: int [1:1000, 1:25] 1 1 1 1 1 1 1 0 2 0 ...
+#>   ..$ series_1: int [1:1000, 1:25] 0 0 2 1 1 1 1 3 5 2 ...
+#>   ..$ series_2: int [1:1000, 1:25] 2 0 5 5 1 0 4 4 0 2 ...
+#>   ..$ series_3: int [1:1000, 1:25] 2 0 2 4 2 1 1 3 1 1 ...
 #>  - attr(*, "class")= chr "mvgam_forecast"
 plot(fc, series = 1)
 #> Out of sample DRPS:
-#> 32.117538
+#> 31.457705
 
 plot(fc, series = 2)
 #> Out of sample DRPS:
-#> 20.953595
+#> 20.949768
 
 plot(fc, series = 3)
 #> Out of sample DRPS:
-#> 10.209583
+#> 9.884977
 
 
 # Forecasts as expectations
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index 7be1ea04..a5a319b1 100644
--- a/docs/reference/irf.mvgam.html
+++ b/docs/reference/irf.mvgam.html
@@ -144,6 +144,19 @@ 
Examples             family = gaussian(),
              data = simdat$data_train,
              silent = 2)
+#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
+#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
+#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:359,
+#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
+#>                  from stan/lib/stan_math/stan/math/rev.hpp:16,
+#>                  from stan/lib/stan_math/stan/math.hpp:19,
+#>                  from stan/src/stan/model/model_header.hpp:4,
+#>                  from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f88e74d98.hpp:2:
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
+#>   194 |       if (cdf_n < 0.0)
+#>       | 
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory
 
 # Calulate Generalized IRFs for each series
 irfs <- irf(mod, h = 12, cumulative = FALSE)
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diff --git a/docs/reference/mvgam.html b/docs/reference/mvgam.html
index 1c3b4c3e..cd67bc75 100644
--- a/docs/reference/mvgam.html
+++ b/docs/reference/mvgam.html
@@ -128,7 +128,7 @@ 
Usage
     
Arguments

     
formula

 
A character string specifying the GAM observation model formula. These are exactly like the formula
-for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
+for a GLM except that smooth terms, s(), te(), ti(), t2(), as well as time-varying
 dynamic() terms, can be added to the right hand side
 to specify that the linear predictor depends on smooth functions of predictors
 (or linear functionals of these). In nmix() family models, the formula is used to
@@ -217,16 +217,16 @@ 
Argumentsgaussian() for real-valued data

 
  • betar() for proportional data on (0,1)
    • 
-
  • lognormal() for non-negative real-valued data
    • 
+
  • lognormal() for non-negative real-valued data
    • 
 
  • student_t() for real-valued data
    • 
 
  • Gamma() for non-negative real-valued data
    • 
-
  • bernoulli() for binary data
    • 
+
  • bernoulli() for binary data
    • 
 
  • poisson() for count data
    • 
 
  • nb() for overdispersed count data
    • 
 
  • binomial() for count data with imperfect detection when the number of trials is known;
 note that the cbind() function must be used to bind the discrete observations and the discrete number
 of trials
    • 
-
  • beta_binomial() as for binomial() but allows for overdispersion
    • 
+
  • beta_binomial() as for binomial() but allows for overdispersion
    • 
 
  • nmix() for count data with imperfect detection when the number of trials
 is unknown and should be modeled via a State-Space N-Mixture model.
 The latent states are Poisson, capturing the 'true' latent
@@ -434,7 +434,7 @@ 
    Argumentsfindjags
    • 
+for modelling if use_stan == FALSE. If missing, the path will be recovered from a call to findjags
    
 
 
 
    ...
    
@@ -548,7 +548,7 @@ 
    Details
    6. Update the model as needed and use loo_compare.mvgam for in-sample model comparisons, or alternatively
 use forecast.mvgam and score.mvgam_forecast to compare models based on out-of-sample forecasts (see the forecast evaluation vignette for guidance)
    
 
  • 7. When satisfied with the model structure, use predict.mvgam,
-plot_predictions and/or plot_slopes for
+plot_predictions and/or plot_slopes for
 more targeted inferences (see "How to interpret and report nonlinear effects from Generalized Additive Models" for some guidance on interpreting GAMs)
    •
    @@ -600,7 +600,7 @@

    Examples run_model = FALSE) # View the model code in Stan language -stancode(mod1) +stancode(mod1) #> // Stan model code generated by package mvgam #> data { #> int<lower=0> total_obs; // total number of observations @@ -691,7 +691,7 @@

    Examples#> # View the data objects needed to fit the model in Stan -sdata1 <- standata(mod1) +sdata1 <- standata(mod1) str(sdata1) #> List of 18 #> $ y : num [1:60, 1:3] 12 17 12 31 30 8 0 1 3 5 ... @@ -731,46 +731,59 @@

    Examples chains = 2) #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f843d074b8.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> 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 2 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) +#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 1 Iteration: 500 / 1000 [ 50%] (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 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 1 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 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 1.9 seconds. -#> Chain 2 finished in 1.8 seconds. +#> Chain 1 finished in 0.9 seconds. +#> Chain 2 finished in 0.9 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 1.9 seconds. -#> Total execution time: 2.0 seconds. +#> Mean chain execution time: 0.9 seconds. +#> Total execution time: 1.0 seconds. #> # Extract the model summary summary(mod1) #> GAM formula: #> y ~ s(season, bs = "cc", k = 6) -#> <environment: 0x000001340a92ca48> +#> <environment: 0x00000251dde37b70> #> #> Family: #> poisson @@ -794,27 +807,27 @@

    Examples#> #> #> GAM coefficient (beta) estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> (Intercept) 1.80 1.900 2.10 1 475 -#> s(season).1 0.14 0.370 0.60 1 514 -#> s(season).2 -0.34 -0.097 0.15 1 566 -#> s(season).3 -1.00 -0.720 -0.46 1 470 -#> s(season).4 -0.50 -0.270 -0.03 1 799 +#> 2.5% 50% 97.5% Rhat n_eff +#> (Intercept) 1.80 2.000 2.1000 1.01 350 +#> s(season).1 0.15 0.370 0.6000 1.01 199 +#> s(season).2 -0.33 -0.091 0.1400 1.00 404 +#> s(season).3 -1.00 -0.710 -0.4500 1.01 372 +#> s(season).4 -0.52 -0.270 -0.0059 1.01 528 #> #> Approximate significance of GAM smooths: #> edf Ref.df Chi.sq p-value -#> s(season) 3.88 4 26 <2e-16 *** +#> s(season) 3.69 4 26.7 1.1e-06 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Latent trend parameter AR estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> ar1[1] 0.300 0.74 0.99 1.00 384 -#> ar1[2] -0.930 -0.34 0.23 1.00 127 -#> ar1[3] 0.011 0.41 0.96 1.00 167 -#> sigma[1] 0.380 0.52 0.73 1.00 446 -#> sigma[2] 0.390 0.55 0.79 1.00 198 -#> sigma[3] 0.410 0.58 0.78 1.01 313 +#> 2.5% 50% 97.5% Rhat n_eff +#> ar1[1] 0.2400 0.73 0.98 1.01 274 +#> ar1[2] -0.9300 -0.36 0.18 1.01 147 +#> ar1[3] 0.0021 0.38 0.92 1.02 149 +#> sigma[1] 0.3900 0.53 0.73 1.00 314 +#> sigma[2] 0.3900 0.55 0.77 1.00 267 +#> sigma[3] 0.4300 0.59 0.78 1.00 309 #> #> Stan MCMC diagnostics: #> n_eff / iter looks reasonable for all parameters @@ -823,7 +836,7 @@

    Examples#> 0 of 1000 iterations saturated the maximum tree depth of 12 (0%) #> E-FMI indicated no pathological behavior #> -#> Samples were drawn using NUTS(diag_e) at Thu Aug 29 10:57:46 AM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Sep 09 10:40:46 AM 2024. #> For each parameter, n_eff is a crude measure of effective sample size, #> and Rhat is the potential scale reduction factor on split MCMC chains #> (at convergence, Rhat = 1) @@ -839,7 +852,7 @@

    Examples resids <- residuals(mod1) str(resids) -#> num [1:180, 1:4] 0.0253 -0.0822 0.1508 0.5405 -0.0836 ... +#> num [1:180, 1:4] 0.0319 -0.06 0.1531 0.5229 -0.0881 ... #> - attr(*, "dimnames")=List of 2 #> ..$ : NULL #> ..$ : chr [1:4] "Estimate" "Est.Error" "Q2.5" "Q97.5" @@ -849,7 +862,7 @@

    Examplesstr(fc) #> List of 16 #> $ call :Class 'formula' language y ~ s(season, bs = "cc", k = 6) -#> .. ..- attr(*, ".Environment")=<environment: 0x000001340a92ca48> +#> .. ..- attr(*, ".Environment")=<environment: 0x00000251dde37b70> #> $ trend_call : NULL #> $ family : chr "poisson" #> $ family_pars : NULL @@ -875,26 +888,26 @@

    Examples#> ..$ series_3: int [1:20] 10 NA 8 5 16 13 0 0 3 2 ... #> $ test_times : int [1:20] 61 62 63 64 65 66 67 68 69 70 ... #> $ hindcasts :List of 3 -#> ..$ series_1: num [1:1000, 1:60] 16 13 8 13 6 17 12 9 11 8 ... +#> ..$ series_1: num [1:1000, 1:60] 22 9 10 1 18 15 11 19 10 15 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : NULL #> .. .. ..$ : chr [1:60] "ypred[1,1]" "ypred[2,1]" "ypred[3,1]" "ypred[4,1]" ... -#> ..$ series_2: num [1:1000, 1:60] 7 10 2 5 11 12 8 7 8 12 ... +#> ..$ series_2: num [1:1000, 1:60] 12 13 5 5 12 7 16 12 6 9 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : NULL #> .. .. ..$ : chr [1:60] "ypred[1,2]" "ypred[2,2]" "ypred[3,2]" "ypred[4,2]" ... -#> ..$ series_3: num [1:1000, 1:60] 8 14 7 15 7 13 8 6 10 5 ... +#> ..$ series_3: num [1:1000, 1:60] 5 11 9 9 6 15 5 6 14 12 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : NULL #> .. .. ..$ : chr [1:60] "ypred[1,3]" "ypred[2,3]" "ypred[3,3]" "ypred[4,3]" ... #> $ forecasts :List of 3 -#> ..$ series_1: int [1:1000, 1:20] 11 13 3 3 12 11 8 9 35 44 ... -#> ..$ series_2: int [1:1000, 1:20] 19 13 9 38 19 6 27 26 21 7 ... -#> ..$ series_3: int [1:1000, 1:20] 23 21 4 29 14 12 15 4 18 6 ... +#> ..$ series_1: int [1:1000, 1:20] 3 14 6 10 5 8 12 6 11 21 ... +#> ..$ series_2: int [1:1000, 1:20] 4 10 10 39 13 20 12 20 8 11 ... +#> ..$ series_3: int [1:1000, 1:20] 10 9 38 65 10 9 17 41 5 4 ... #> - attr(*, "class")= chr "mvgam_forecast" plot(fc) #> Out of sample DRPS: -#> 54.720637 +#> 55.27413 # Plot the estimated seasonal smooth function @@ -915,11 +928,11 @@

    Examples # Plot conditional response predictions using marginaleffects library(marginaleffects) -conditional_effects(mod1) +conditional_effects(mod1) #> Warning: Removed 19 rows containing missing values or values outside the scale range #> (`geom_point()`). -plot_predictions(mod1, condition = 'season', points = 0.5) +plot_predictions(mod1, condition = 'season', points = 0.5) #> Warning: Removed 19 rows containing missing values or values outside the scale range #> (`geom_point()`). @@ -934,41 +947,41 @@

    Examplesbeta_draws_df <- as.data.frame(mod1, variable = 'betas') head(beta_draws_df) #> (Intercept) s(season).1 s(season).2 s(season).3 s(season).4 -#> 1 1.87065 0.473169 -0.03894510 -0.488944 -0.335604 -#> 2 1.99174 0.186258 -0.00918666 -0.841499 -0.193604 -#> 3 1.90925 0.670423 -0.17225100 -0.755631 -0.429818 -#> 4 1.94451 0.534511 -0.04538430 -0.691632 -0.516703 -#> 5 2.08142 0.221111 0.10645400 -0.559959 -0.221205 -#> 6 2.02562 0.354744 -0.01871940 -0.662982 -0.435189 +#> 1 1.94416 0.394466 -0.06900860 -0.783359 -0.426288 +#> 2 1.92798 0.271471 -0.00850006 -0.685065 -0.244868 +#> 3 1.90193 0.230704 -0.04661740 -0.676646 -0.314741 +#> 4 1.88637 0.430494 -0.30333200 -0.634353 -0.345208 +#> 5 1.97914 0.322658 0.15346600 -0.703236 -0.263105 +#> 6 2.02798 0.407478 0.05122050 -0.581718 -0.361806 str(beta_draws_df) #> 'data.frame': 1000 obs. of 5 variables: -#> $ (Intercept): num 1.87 1.99 1.91 1.94 2.08 ... -#> $ s(season).1: num 0.473 0.186 0.67 0.535 0.221 ... -#> $ s(season).2: num -0.03895 -0.00919 -0.17225 -0.04538 0.10645 ... -#> $ s(season).3: num -0.489 -0.841 -0.756 -0.692 -0.56 ... -#> $ s(season).4: num -0.336 -0.194 -0.43 -0.517 -0.221 ... +#> $ (Intercept): num 1.94 1.93 1.9 1.89 1.98 ... +#> $ s(season).1: num 0.394 0.271 0.231 0.43 0.323 ... +#> $ s(season).2: num -0.069 -0.0085 -0.0466 -0.3033 0.1535 ... +#> $ s(season).3: num -0.783 -0.685 -0.677 -0.634 -0.703 ... +#> $ s(season).4: num -0.426 -0.245 -0.315 -0.345 -0.263 ... # Investigate model fit mc.cores.def <- getOption('mc.cores') options(mc.cores = 1) -loo(mod1) +loo(mod1) #> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details. #> #> Computed from 1000 by 161 log-likelihood matrix #> #> Estimate SE -#> elpd_loo -449.6 7.9 -#> p_loo 88.7 4.3 -#> looic 899.2 15.9 +#> elpd_loo -452.2 8.5 +#> p_loo 91.6 4.8 +#> looic 904.4 17.0 #> ------ #> Monte Carlo SE of elpd_loo is NA. #> #> Pareto k diagnostic values: #> Count Pct. Min. n_eff -#> (-Inf, 0.5] (good) 19 11.8% 151 -#> (0.5, 0.7] (ok) 67 41.6% 59 -#> (0.7, 1] (bad) 67 41.6% 17 -#> (1, Inf) (very bad) 8 5.0% 8 +#> (-Inf, 0.5] (good) 22 13.7% 131 +#> (0.5, 0.7] (ok) 61 37.9% 61 +#> (0.7, 1] (bad) 63 39.1% 19 +#> (1, Inf) (very bad) 15 9.3% 4 #> See help('pareto-k-diagnostic') for details. options(mc.cores = mc.cores.def) @@ -992,39 +1005,52 @@

    Examples chains = 2) #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f8137a4fb3.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> 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: 1 / 1000 [ 0%] (Warmup) #> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 2 Iteration: 400 / 1000 [ 40%] (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: 500 / 1000 [ 50%] (Warmup) #> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) #> 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 2 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 3.3 seconds. +#> Chain 1 finished in 1.0 seconds. +#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 3.5 seconds. +#> Chain 2 finished in 2.0 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 3.4 seconds. -#> Total execution time: 3.6 seconds. +#> Mean chain execution time: 1.5 seconds. +#> Total execution time: 2.1 seconds. #> # The mapping matrix is now supplied as data to the model in the 'Z' element @@ -1179,15 +1205,28 @@

    Examples chains = 2) #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f868403df2.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> 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: 100 / 1000 [ 10%] (Warmup) #> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) @@ -1198,27 +1237,27 @@

    Examples#> 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: 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 1.3 seconds. #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 1.4 seconds. +#> Chain 1 finished in 0.8 seconds. +#> Chain 2 finished in 0.7 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 1.4 seconds. -#> Total execution time: 1.6 seconds. +#> Mean chain execution time: 0.7 seconds. +#> Total execution time: 0.9 seconds. #> # Inspect the model summary, forecast and time-varying coefficient distribution summary(mod) #> GAM formula: #> out ~ gp(time, by = temp, c = 5/4, k = 40, scale = FALSE) -#> <environment: 0x000001340a92ca48> +#> <environment: 0x00000251dde37b70> #> #> Family: #> gaussian @@ -1243,68 +1282,67 @@

    Examples#> #> Observation error parameter estimates: #> 2.5% 50% 97.5% Rhat n_eff -#> sigma_obs[1] 0.44 0.49 0.55 1 642 +#> sigma_obs[1] 0.44 0.49 0.55 1 1179 #> #> GAM coefficient (beta) estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> (Intercept) 4.000 4.0e+00 4.1000 1.01 177 -#> gp(time):temp.1 0.950 3.2e+00 6.5000 1.00 494 -#> gp(time):temp.2 -2.400 1.7e+00 5.5000 1.01 359 -#> gp(time):temp.3 -5.600 -1.7e+00 3.6000 1.00 599 -#> gp(time):temp.4 -5.200 -1.2e+00 2.2000 1.00 934 -#> gp(time):temp.5 -2.800 2.3e-01 3.5000 1.00 765 -#> gp(time):temp.6 -2.500 1.5e-02 3.4000 1.00 1019 -#> gp(time):temp.7 -3.500 -1.9e-01 2.1000 1.00 703 -#> gp(time):temp.8 -1.500 1.2e-01 2.8000 1.00 844 -#> gp(time):temp.9 -1.400 1.2e-01 2.6000 1.01 631 -#> gp(time):temp.10 -2.200 -1.3e-01 1.0000 1.01 515 -#> gp(time):temp.11 -1.900 -1.1e-03 1.2000 1.00 802 -#> gp(time):temp.12 -0.630 5.9e-02 2.0000 1.00 437 -#> gp(time):temp.13 -1.200 2.2e-12 1.2000 1.00 1294 -#> gp(time):temp.14 -1.200 -1.8e-05 0.6500 1.00 942 -#> gp(time):temp.15 -0.890 -4.4e-19 0.9000 1.00 1176 -#> gp(time):temp.16 -0.660 1.8e-17 0.9300 1.00 1499 -#> gp(time):temp.17 -0.640 2.0e-17 0.7800 1.00 1348 -#> gp(time):temp.18 -0.680 -7.5e-23 0.7700 1.00 1831 -#> gp(time):temp.19 -0.830 -3.1e-19 0.3700 1.00 754 -#> gp(time):temp.20 -0.400 2.3e-29 0.5000 1.00 1432 -#> gp(time):temp.21 -0.190 3.7e-28 0.8200 1.00 747 -#> gp(time):temp.22 -0.480 -2.4e-32 0.2600 1.00 1214 -#> gp(time):temp.23 -0.470 -1.8e-33 0.2100 1.00 995 -#> gp(time):temp.24 -0.140 1.6e-36 0.4800 1.00 724 -#> gp(time):temp.25 -0.210 2.5e-61 0.3200 1.00 1165 -#> gp(time):temp.26 -0.380 -7.4e-48 0.1100 1.00 838 -#> gp(time):temp.27 -0.120 -1.0e-60 0.2900 1.00 1245 -#> gp(time):temp.28 -0.097 -7.5e-67 0.2100 1.00 912 -#> gp(time):temp.29 -0.150 -5.2e-70 0.0940 1.00 1129 -#> gp(time):temp.30 -0.130 4.6e-79 0.0790 1.00 1228 -#> gp(time):temp.31 -0.180 -2.8e-86 0.0800 1.00 1248 -#> gp(time):temp.32 -0.120 -2.0e-74 0.0710 1.00 1007 -#> gp(time):temp.33 -0.089 -1.2e-81 0.0520 1.00 1098 -#> gp(time):temp.34 -0.037 -2.9e-98 0.1100 1.00 1064 -#> gp(time):temp.35 -0.042 2.1e-99 0.0630 1.00 947 -#> gp(time):temp.36 -0.075 -3.6e-115 0.0470 1.00 1178 -#> gp(time):temp.37 -0.056 1.4e-116 0.0210 1.00 1658 -#> gp(time):temp.38 -0.015 -3.7e-127 0.0510 1.00 1148 -#> gp(time):temp.39 -0.013 -6.9e-124 0.0310 1.00 1207 -#> gp(time):temp.40 -0.033 6.9e-135 0.0098 1.00 885 -#> gp(time):temp.41 -0.024 -9.1e-137 0.0130 1.00 962 +#> 2.5% 50% 97.5% Rhat n_eff +#> (Intercept) 4.000 4.0e+00 4.100 1.00 1235 +#> gp(time):temp.1 0.740 3.3e+00 6.500 1.00 548 +#> gp(time):temp.2 -2.400 1.5e+00 5.800 1.00 435 +#> gp(time):temp.3 -5.700 -1.5e+00 3.800 1.00 573 +#> gp(time):temp.4 -5.200 -1.2e+00 2.300 1.00 464 +#> gp(time):temp.5 -2.900 4.5e-01 3.800 1.00 889 +#> gp(time):temp.6 -2.300 1.6e-01 3.300 1.00 813 +#> gp(time):temp.7 -3.400 -2.8e-01 2.200 1.00 839 +#> gp(time):temp.8 -1.700 1.9e-01 2.700 1.00 794 +#> gp(time):temp.9 -1.200 1.6e-01 2.700 1.00 920 +#> gp(time):temp.10 -2.500 -2.5e-01 0.910 1.00 534 +#> gp(time):temp.11 -1.900 -2.6e-02 1.200 1.00 832 +#> gp(time):temp.12 -0.610 6.3e-02 1.800 1.00 671 +#> gp(time):temp.13 -1.200 1.8e-09 1.200 1.00 1055 +#> gp(time):temp.14 -1.400 -1.2e-03 0.690 1.00 667 +#> gp(time):temp.15 -0.780 2.3e-18 1.000 1.00 841 +#> gp(time):temp.16 -0.730 3.3e-06 0.860 1.00 1027 +#> gp(time):temp.17 -0.810 5.1e-26 0.970 1.00 1103 +#> gp(time):temp.18 -0.780 5.6e-29 0.670 1.00 1011 +#> gp(time):temp.19 -1.000 -6.4e-06 0.320 1.00 833 +#> gp(time):temp.20 -0.560 1.4e-19 0.590 1.00 997 +#> gp(time):temp.21 -0.260 7.7e-12 0.910 1.00 669 +#> gp(time):temp.22 -0.470 -6.6e-25 0.330 1.00 1345 +#> gp(time):temp.23 -0.600 -4.1e-23 0.180 1.00 464 +#> gp(time):temp.24 -0.220 1.8e-19 0.710 1.00 1152 +#> gp(time):temp.25 -0.370 3.2e-33 0.310 1.00 1078 +#> gp(time):temp.26 -0.610 -4.5e-21 0.096 1.00 448 +#> gp(time):temp.27 -0.200 4.7e-39 0.360 1.00 764 +#> gp(time):temp.28 -0.100 9.8e-21 0.320 1.00 938 +#> gp(time):temp.29 -0.240 -5.7e-57 0.180 1.00 1000 +#> gp(time):temp.30 -0.160 2.8e-82 0.230 1.00 1156 +#> gp(time):temp.31 -0.160 2.6e-57 0.160 1.00 948 +#> gp(time):temp.32 -0.130 -1.0e-60 0.170 1.00 772 +#> gp(time):temp.33 -0.160 -1.7e-57 0.075 1.00 772 +#> gp(time):temp.34 -0.066 8.3e-75 0.110 1.00 944 +#> gp(time):temp.35 -0.038 1.6e-82 0.130 1.00 434 +#> gp(time):temp.36 -0.085 3.4e-124 0.051 1.00 957 +#> gp(time):temp.37 -0.062 7.2e-125 0.057 1.00 800 +#> gp(time):temp.38 -0.046 4.0e-72 0.072 1.00 1389 +#> gp(time):temp.39 -0.024 -4.0e-87 0.087 1.00 728 +#> gp(time):temp.40 -0.062 -9.5e-73 0.019 1.00 902 +#> gp(time):temp.41 -0.036 3.6e-126 0.029 1.01 754 #> #> GAM gp term marginal deviation (alpha) and length scale (rho) estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> alpha_gp(time):temp 0.18 0.35 1.2 1.16 12 -#> rho_gp(time):temp 12.00 33.00 130.0 1.11 14 +#> 2.5% 50% 97.5% Rhat n_eff +#> alpha_gp(time):temp 0.17 0.33 0.84 1.01 210 +#> rho_gp(time):temp 11.00 32.00 100.00 1.01 135 #> #> Stan MCMC diagnostics: #> n_eff / iter looks reasonable for all parameters -#> Rhats above 1.05 found for 9 parameters -#> *Diagnose further to investigate why the chains have not mixed -#> 151 of 1000 iterations ended with a divergence (15.1%) +#> Rhat looks reasonable for all parameters +#> 63 of 1000 iterations ended with a divergence (6.3%) #> *Try running with larger adapt_delta to remove the divergences #> 0 of 1000 iterations saturated the maximum tree depth of 12 (0%) #> E-FMI indicated no pathological behavior #> -#> Samples were drawn using NUTS(diag_e) at Thu Aug 29 10:59:38 AM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Sep 09 10:42:03 AM 2024. #> For each parameter, n_eff is a crude measure of effective sample size, #> and Rhat is the potential scale reduction factor on split MCMC chains #> (at convergence, Rhat = 1) @@ -1313,7 +1351,7 @@

    Examplesfc <- forecast(mod, newdata = data_test) plot(fc) #> Out of sample CRPS: -#> 6.22329038558597 +#> 6.33897291368901 # Propagating the smooth term shows how the coefficient is expected to evolve @@ -1343,6 +1381,19 @@

    Examples chains = 2) #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f85c546b04.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> Start sampling #> Running MCMC with 2 parallel chains... #> @@ -1352,30 +1403,30 @@

    Examples#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) +#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) +#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) +#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 14.0 seconds. +#> Chain 2 finished in 3.9 seconds. #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 15.0 seconds. +#> Chain 1 finished in 4.2 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 14.5 seconds. -#> Total execution time: 15.1 seconds. +#> Mean chain execution time: 4.0 seconds. +#> Total execution time: 4.3 seconds. #> # Inspect the model file to see the modification to the linear predictor @@ -1477,10 +1528,10 @@

    Exampleslayout(matrix(1:2, ncol = 2)) plot(fc, series = 1, ylim = c(0, 75)) #> Out of sample DRPS: -#> 22.754399 +#> 22.781273 plot(fc, series = 2, ylim = c(0, 75)) #> Out of sample DRPS: -#> 77.633266 +#> 77.61833 layout(1) @@ -1490,7 +1541,7 @@

    Examplesfc <- forecast(mod, newdata = dat$data_test) plot(fc) #> Out of sample DRPS: -#> 38.550576 +#> 38.523539 # Relative Risks can be computed by fixing the offset to the same value @@ -1552,44 +1603,57 @@

    Examples#> This warning is displayed once per session. #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f849e13761.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> 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: 100 / 1000 [ 10%] (Warmup) #> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) +#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 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 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 3.1 seconds. -#> Chain 2 finished in 3.1 seconds. +#> Chain 2 finished in 1.0 seconds. +#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 1 finished in 1.1 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 3.1 seconds. -#> Total execution time: 3.3 seconds. +#> Mean chain execution time: 1.1 seconds. +#> Total execution time: 1.2 seconds. #> summary(mod) #> GAM formula: #> cbind(y, ntrials) ~ series + s(x, by = series) -#> <environment: 0x000001340a92ca48> +#> <environment: 0x00000251dde37b70> #> #> Family: #> binomial @@ -1614,31 +1678,31 @@

    Examples#> #> GAM coefficient (beta) estimates: #> 2.5% 50% 97.5% Rhat n_eff -#> (Intercept) -0.690 -0.55000 -0.410 1.00 816 -#> seriesseries2 0.300 0.50000 0.680 1.00 898 -#> s(x):seriesseries1.1 -0.110 0.06900 0.500 1.02 198 -#> s(x):seriesseries1.2 -0.170 0.01700 0.290 1.01 428 -#> s(x):seriesseries1.3 -0.098 -0.00420 0.061 1.00 552 -#> s(x):seriesseries1.4 -0.110 0.00380 0.150 1.00 433 -#> s(x):seriesseries1.5 -0.056 -0.00098 0.046 1.00 715 -#> s(x):seriesseries1.6 -0.130 -0.00410 0.100 1.00 430 -#> s(x):seriesseries1.7 -0.045 0.00130 0.053 1.00 422 -#> s(x):seriesseries1.8 -0.680 0.03200 0.880 1.00 410 -#> s(x):seriesseries1.9 0.360 0.96000 1.300 1.01 197 -#> s(x):seriesseries2.1 -0.350 -0.03300 0.160 1.01 244 -#> s(x):seriesseries2.2 -0.260 -0.01600 0.180 1.00 289 -#> s(x):seriesseries2.3 -0.048 0.00810 0.110 1.01 307 -#> s(x):seriesseries2.4 -0.140 -0.00520 0.120 1.00 312 -#> s(x):seriesseries2.5 -0.047 0.00230 0.052 1.00 935 -#> s(x):seriesseries2.6 -0.110 0.00380 0.120 1.00 314 -#> s(x):seriesseries2.7 -0.043 0.00015 0.043 1.00 346 -#> s(x):seriesseries2.8 -0.790 -0.02200 0.700 1.00 293 -#> s(x):seriesseries2.9 -0.950 -0.69000 -0.220 1.01 259 +#> (Intercept) -0.280 -0.17000 -0.036 1.00 592 +#> seriesseries2 -0.320 -0.15000 0.028 1.00 577 +#> s(x):seriesseries1.1 -0.120 0.02100 0.380 1.01 176 +#> s(x):seriesseries1.2 -0.190 0.00420 0.260 1.00 222 +#> s(x):seriesseries1.3 -0.088 -0.00440 0.063 1.00 307 +#> s(x):seriesseries1.4 -0.150 -0.00084 0.150 1.00 250 +#> s(x):seriesseries1.5 -0.064 0.00330 0.092 1.00 232 +#> s(x):seriesseries1.6 -0.120 -0.00120 0.130 1.00 253 +#> s(x):seriesseries1.7 -0.036 -0.00051 0.028 1.00 364 +#> s(x):seriesseries1.8 -0.440 0.00740 0.500 1.00 223 +#> s(x):seriesseries1.9 0.280 0.70000 0.920 1.01 171 +#> s(x):seriesseries2.1 -0.650 -0.07300 0.070 1.02 76 +#> s(x):seriesseries2.2 -0.270 0.02900 0.520 1.03 138 +#> s(x):seriesseries2.3 -0.068 0.01300 0.170 1.02 191 +#> s(x):seriesseries2.4 -0.160 0.02600 0.340 1.03 142 +#> s(x):seriesseries2.5 -0.180 -0.01700 0.055 1.03 127 +#> s(x):seriesseries2.6 -0.120 0.02400 0.310 1.03 130 +#> s(x):seriesseries2.7 -0.031 0.00420 0.079 1.00 374 +#> s(x):seriesseries2.8 -0.430 0.12000 1.200 1.03 117 +#> s(x):seriesseries2.9 -0.800 -0.58000 0.013 1.02 89 #> #> Approximate significance of GAM smooths: #> edf Ref.df Chi.sq p-value -#> s(x):seriesseries1 1.93 9 53.8 <2e-16 *** -#> s(x):seriesseries2 1.08 9 27.6 0.42 +#> s(x):seriesseries1 2.12 9 27.4 < 2e-16 *** +#> s(x):seriesseries2 1.77 9 38.8 0.00017 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> @@ -1649,7 +1713,7 @@

    Examples#> 0 of 1000 iterations saturated the maximum tree depth of 12 (0%) #> E-FMI indicated no pathological behavior #> -#> Samples were drawn using NUTS(diag_e) at Thu Aug 29 11:01:26 AM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Sep 09 10:43:01 AM 2024. #> For each parameter, n_eff is a crude measure of effective sample size, #> and Rhat is the potential scale reduction factor on split MCMC chains #> (at convergence, Rhat = 1) @@ -1659,7 +1723,7 @@

    Examplespp_check(mod, type = "ecdf_overlay_grouped", group = "series", ndraws = 50) -conditional_effects(mod, type = 'link') +conditional_effects(mod, type = 'link') # } diff --git a/docs/reference/mvgam_diagnostics.html b/docs/reference/mvgam_diagnostics.html index 3b631b5f..879c12a0 100644 --- a/docs/reference/mvgam_diagnostics.html +++ b/docs/reference/mvgam_diagnostics.html @@ -119,49 +119,62 @@

    Examples chains = 2) #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f8335480c.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> 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 1 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup) #> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 1 finished in 0.9 seconds. +#> Chain 1 finished in 0.3 seconds. #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 1.0 seconds. +#> Chain 2 finished in 0.4 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 0.9 seconds. -#> Total execution time: 1.1 seconds. +#> Mean chain execution time: 0.4 seconds. +#> Total execution time: 0.5 seconds. #> np <- nuts_params(mod) head(np) #> Chain Iteration Parameter Value -#> 1 1 1 accept_stat__ 0.946851 -#> 2 1 2 accept_stat__ 0.971100 -#> 3 1 3 accept_stat__ 0.980961 -#> 4 1 4 accept_stat__ 0.911601 -#> 5 1 5 accept_stat__ 0.926669 -#> 6 1 6 accept_stat__ 0.837443 +#> 1 1 1 accept_stat__ 0.944154 +#> 2 1 2 accept_stat__ 0.947702 +#> 3 1 3 accept_stat__ 0.919436 +#> 4 1 4 accept_stat__ 0.920023 +#> 5 1 5 accept_stat__ 0.998240 +#> 6 1 6 accept_stat__ 0.941577 # extract the number of divergence transitions sum(subset(np, Parameter == "divergent__")$Value) @@ -169,7 +182,7 @@

    Examples head(neff_ratio(mod)) #> mus[1,1] mus[2,1] mus[3,1] mus[4,1] mus[5,1] mus[6,1] -#> 0.6358219 0.3672060 0.7036268 0.8197350 0.6501380 0.4767678 +#> 0.4479520 0.7294096 0.7456069 0.8241892 0.7841851 0.7799631 # }

    diff --git a/docs/reference/mvgam_families-1.png b/docs/reference/mvgam_families-1.png index 84c6fd52..c84f0e76 100644 Binary files a/docs/reference/mvgam_families-1.png and b/docs/reference/mvgam_families-1.png differ diff --git a/docs/reference/mvgam_families.html b/docs/reference/mvgam_families.html index 082a0610..efc618a9 100644 --- a/docs/reference/mvgam_families.html +++ b/docs/reference/mvgam_families.html @@ -244,25 +244,38 @@

    Examples#> Your model may benefit from using "noncentred = TRUE" #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f8b195da5.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> 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: 100 / 1000 [ 10%] (Warmup) #> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) +#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) +#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) @@ -270,24 +283,24 @@

    Examples#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 45.9 seconds. +#> Chain 2 finished in 9.9 seconds. #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 46.5 seconds. +#> Chain 1 finished in 10.4 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 46.2 seconds. -#> Total execution time: 46.6 seconds. +#> Mean chain execution time: 10.1 seconds. +#> Total execution time: 10.4 seconds. #> # The usual diagnostics summary(mod) #> GAM observation formula: #> obs ~ species - 1 -#> <environment: 0x000001e60fc56eb8> +#> <environment: 0x00000251dbf04c98> #> #> GAM process formula: #> ~s(time, by = trend, k = 4) + species -#> <environment: 0x000001e60fc56eb8> +#> <environment: 0x00000251dbf04c98> #> #> Family: #> nmix @@ -314,25 +327,25 @@

    Examples#> #> #> GAM observation model coefficient (beta) estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> speciessp_1 -0.28 0.700 1.40 1 522 -#> speciessp_2 -1.10 -0.012 0.84 1 532 +#> 2.5% 50% 97.5% Rhat n_eff +#> speciessp_1 -0.27 0.68000 1.40 1 376 +#> speciessp_2 -1.20 -0.00015 0.89 1 503 #> #> GAM process model coefficient (beta) estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> (Intercept)_trend 2.700 3.0000 3.400 1.00 555 -#> speciessp_2_trend -1.300 -0.6200 0.130 1.00 545 -#> s(time):trendtrend1.1_trend -0.068 0.0140 0.210 1.00 361 -#> s(time):trendtrend1.2_trend -0.230 0.0069 0.250 1.00 564 -#> s(time):trendtrend1.3_trend -0.470 -0.2500 -0.034 1.00 586 -#> s(time):trendtrend2.1_trend -0.290 -0.0150 0.088 1.01 158 -#> s(time):trendtrend2.2_trend -0.170 0.0350 0.550 1.00 170 -#> s(time):trendtrend2.3_trend 0.056 0.3300 0.600 1.01 480 +#> 2.5% 50% 97.5% Rhat n_eff +#> (Intercept)_trend 2.700 3.00000 3.400 1.00 359 +#> speciessp_2_trend -1.200 -0.62000 0.170 1.00 357 +#> s(time):trendtrend1.1_trend -0.074 0.01600 0.200 1.00 276 +#> s(time):trendtrend1.2_trend -0.280 -0.00085 0.270 1.00 341 +#> s(time):trendtrend1.3_trend -0.460 -0.25000 -0.050 1.00 412 +#> s(time):trendtrend2.1_trend -0.300 -0.01600 0.082 1.01 105 +#> s(time):trendtrend2.2_trend -0.190 0.03500 0.610 1.01 140 +#> s(time):trendtrend2.3_trend 0.043 0.32000 0.620 1.00 499 #> #> Approximate significance of GAM process smooths: -#> edf Ref.df Chi.sq p-value -#> s(time):seriestrend1 1.01 3 2.52 0.50 -#> s(time):seriestrend2 1.11 3 1.06 0.86 +#> edf Ref.df Chi.sq p-value +#> s(time):seriestrend1 0.964 3 3.59 0.64 +#> s(time):seriestrend2 0.966 3 4.08 0.81 #> #> Stan MCMC diagnostics: #> n_eff / iter looks reasonable for all parameters @@ -341,17 +354,17 @@

    Examples#> 0 of 1000 iterations saturated the maximum tree depth of 12 (0%) #> E-FMI indicated no pathological behavior #> -#> Samples were drawn using NUTS(diag_e) at Tue Sep 03 2:04:02 PM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Sep 09 10:44:18 AM 2024. #> For each parameter, n_eff is a crude measure of effective sample size, #> and Rhat is the potential scale reduction factor on split MCMC chains #> (at convergence, Rhat = 1) # Plotting conditional effects library(ggplot2); library(marginaleffects) -#> Warning: package ‘ggplot2’ was built under R version 4.2.3 +#> Warning: package ‘ggplot2’ was built under R version 4.3.3 #> RStudio Community is a great place to get help: #> https://community.rstudio.com/c/tidyverse -plot_predictions(mod, condition = 'species', +plot_predictions(mod, condition = 'species', type = 'detection') + ylab('Pr(detection)') + ylim(c(0, 1)) + @@ -383,9 +396,6 @@

    Examplesmod <- mvgam(cbind(y, ntrials) ~ series + s(x, by = series), family = binomial(), data = dat) -#> Warning: Binomial and Beta-binomial families require cbind(n_successes, n_trials) -#> in the formula left-hand side. Do not use cbind(n_successes, n_failures)! -#> This warning is displayed once per session. #> Compiling Stan program using cmdstanr #> #> Start sampling @@ -396,62 +406,62 @@

    Examples#> Chain 3 Iteration: 1 / 1000 [ 0%] (Warmup) #> Chain 4 Iteration: 1 / 1000 [ 0%] (Warmup) #> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 3 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 4 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 3 Iteration: 100 / 1000 [ 10%] (Warmup) +#> Chain 4 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 3 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 4 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 4 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 3 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 3 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 4 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 3 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 3 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 4 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 3 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 3 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 4 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 4 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 3 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) +#> Chain 3 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 4 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) +#> Chain 3 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 3 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 4 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 3 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 4 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 3 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 3.3 seconds. #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 3 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 3 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 4 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 1 finished in 1.1 seconds. +#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 3 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 4 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 1 finished in 3.5 seconds. -#> Chain 3 finished in 3.5 seconds. +#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 3 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 4 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 4 finished in 3.6 seconds. +#> Chain 4 finished in 1.3 seconds. +#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 3 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 3 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 3 finished in 1.4 seconds. +#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 2 finished in 1.5 seconds. #> #> All 4 chains finished successfully. -#> Mean chain execution time: 3.5 seconds. -#> Total execution time: 3.8 seconds. +#> Mean chain execution time: 1.3 seconds. +#> Total execution time: 1.6 seconds. #> summary(mod) #> GAM formula: #> cbind(y, ntrials) ~ series + s(x, by = series) -#> <environment: 0x000001e60fc56eb8> +#> <environment: 0x00000251dbf04c98> #> #> Family: #> binomial @@ -476,43 +486,42 @@

    Examples#> #> GAM coefficient (beta) estimates: #> 2.5% 50% 97.5% Rhat n_eff -#> (Intercept) -0.550 -0.41000 -0.260 1.00 1198 -#> seriesseries2 -0.170 0.02500 0.230 1.00 1140 -#> s(x):seriesseries1.1 -0.220 -0.00640 0.190 1.00 765 -#> s(x):seriesseries1.2 -0.240 0.00210 0.290 1.01 603 -#> s(x):seriesseries1.3 -0.057 0.00640 0.110 1.00 448 -#> s(x):seriesseries1.4 -0.160 -0.00510 0.130 1.00 693 -#> s(x):seriesseries1.5 -0.040 0.00200 0.051 1.00 815 -#> s(x):seriesseries1.6 -0.140 -0.00340 0.120 1.01 620 -#> s(x):seriesseries1.7 -0.022 0.00084 0.028 1.00 951 -#> s(x):seriesseries1.8 -0.550 0.01800 0.650 1.00 654 -#> s(x):seriesseries1.9 0.810 1.20000 1.400 1.00 617 -#> s(x):seriesseries2.1 -0.120 0.05200 0.700 1.05 66 -#> s(x):seriesseries2.2 -0.930 -0.02600 0.260 1.03 122 -#> s(x):seriesseries2.3 -0.260 -0.00420 0.091 1.04 97 -#> s(x):seriesseries2.4 -0.200 0.00014 0.390 1.02 341 -#> s(x):seriesseries2.5 -0.190 -0.00480 0.041 1.03 109 -#> s(x):seriesseries2.6 -0.160 0.00360 0.410 1.03 143 -#> s(x):seriesseries2.7 -0.056 -0.00025 0.039 1.00 691 -#> s(x):seriesseries2.8 -1.700 -0.00180 0.700 1.03 146 -#> s(x):seriesseries2.9 -1.200 -0.94000 -0.038 1.05 65 +#> (Intercept) -0.550 -0.41000 -0.270 1.01 833 +#> seriesseries2 -0.170 0.03000 0.220 1.00 1020 +#> s(x):seriesseries1.1 -0.200 0.00089 0.200 1.00 504 +#> s(x):seriesseries1.2 -0.280 0.00670 0.320 1.00 475 +#> s(x):seriesseries1.3 -0.150 -0.00370 0.068 1.00 317 +#> s(x):seriesseries1.4 -0.160 0.00140 0.160 1.00 461 +#> s(x):seriesseries1.5 -0.045 0.00053 0.057 1.00 562 +#> s(x):seriesseries1.6 -0.140 0.00210 0.150 1.00 423 +#> s(x):seriesseries1.7 -0.031 -0.00050 0.024 1.01 433 +#> s(x):seriesseries1.8 -0.670 -0.00710 0.650 1.00 457 +#> s(x):seriesseries1.9 0.830 1.20000 1.500 1.01 378 +#> s(x):seriesseries2.1 -0.120 0.04300 0.630 1.02 128 +#> s(x):seriesseries2.2 -0.250 0.01900 0.680 1.01 158 +#> s(x):seriesseries2.3 -0.081 0.00290 0.190 1.01 261 +#> s(x):seriesseries2.4 -0.200 -0.00035 0.250 1.01 285 +#> s(x):seriesseries2.5 -0.140 -0.00390 0.040 1.01 208 +#> s(x):seriesseries2.6 -0.130 0.00300 0.280 1.01 176 +#> s(x):seriesseries2.7 -0.037 0.00032 0.058 1.00 669 +#> s(x):seriesseries2.8 -1.200 -0.00260 0.630 1.01 188 +#> s(x):seriesseries2.9 -1.200 -0.96000 -0.140 1.02 146 #> #> Approximate significance of GAM smooths: #> edf Ref.df Chi.sq p-value -#> s(x):seriesseries1 1.06 9 71.5 0.00081 *** -#> s(x):seriesseries2 1.65 9 55.5 < 2e-16 *** +#> s(x):seriesseries1 1.10 9 70.4 0.0016 ** +#> s(x):seriesseries2 2.45 9 60.7 <2e-16 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Stan MCMC diagnostics: #> n_eff / iter looks reasonable for all parameters -#> Rhats above 1.05 found for 3 parameters -#> *Diagnose further to investigate why the chains have not mixed +#> Rhat looks reasonable for all parameters #> 0 of 2000 iterations ended with a divergence (0%) #> 0 of 2000 iterations saturated the maximum tree depth of 12 (0%) #> E-FMI indicated no pathological behavior #> -#> Samples were drawn using NUTS(diag_e) at Tue Sep 03 2:04:52 PM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Sep 09 10:44:23 AM 2024. #> For each parameter, n_eff is a crude measure of effective sample size, #> and Rhat is the potential scale reduction factor on split MCMC chains #> (at convergence, Rhat = 1) diff --git a/docs/reference/mvgam_marginaleffects.html b/docs/reference/mvgam_marginaleffects.html index 8fffd966..4a4eae81 100644 --- a/docs/reference/mvgam_marginaleffects.html +++ b/docs/reference/mvgam_marginaleffects.html @@ -70,16 +70,16 @@

    Usage

    # S3 method for mvgam
-get_coef(model, trend_effects = FALSE, ...)
+get_coef(model, trend_effects = FALSE, ...)
 
 # S3 method for mvgam
-set_coef(model, coefs, trend_effects = FALSE, ...)
+set_coef(model, coefs, trend_effects = FALSE, ...)
 
 # S3 method for mvgam
-get_vcov(model, vcov = NULL, ...)
+get_vcov(model, vcov = NULL, ...)
 
 # S3 method for mvgam
-get_predict(model, newdata, type = "response", process_error = FALSE, ...)
+get_predict(model, newdata, type = "response", process_error = FALSE, ...)
 
 # S3 method for mvgam
 get_data(x, source = "environment", verbose = TRUE, ...)
@@ -128,7 +128,7 @@ 
    Arguments?slopes documentation for a non-exhaustive list of available
+?slopes documentation for a non-exhaustive list of available
 arguments.
    
 
 
@@ -153,11 +153,9 @@ 
    Argumentsinsight::get_data(), which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model.
    
-
  • datagrid() call to specify a custom grid of regressors. For example:
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    See also

    plot_mvgam_resids, plot_mvgam_smooth, plot_mvgam_fc, plot_mvgam_trend, plot_mvgam_uncertainty, plot_mvgam_factors, plot_mvgam_randomeffects, conditional_effects.mvgam, -plot_predictions, plot_slopes, +plot_predictions, plot_slopes, gratia_mvgam_enhancements

    @@ -177,6 +177,19 @@

    Examples#> Your model may benefit from using "noncentred = TRUE" #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f827f5212f.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> Start sampling #> Running MCMC with 2 parallel chains... #> @@ -184,32 +197,32 @@

    Examples#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup) #> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) -#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) +#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) +#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 1 Iteration: 500 / 1000 [ 50%] (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 2 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 16.3 seconds. -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 2 finished in 6.5 seconds. #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 32.9 seconds. +#> Chain 1 finished in 7.5 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 24.6 seconds. -#> Total execution time: 33.0 seconds. +#> Mean chain execution time: 7.0 seconds. +#> Total execution time: 7.6 seconds. #> # Plot predictions and residuals for each series @@ -234,13 +247,13 @@

    Examples # More flexible plots with 'marginaleffects' utilities library(marginaleffects) -plot_predictions(mod, condition = 'season', type = 'link') +plot_predictions(mod, condition = 'season', type = 'link') -plot_predictions(mod, +plot_predictions(mod, condition = c('season', 'series', 'series'), type = 'link') -plot_predictions(mod, condition = 'series', type = 'link') +plot_predictions(mod, condition = 'series', type = 'link') # When using a State-Space model with predictors on the process @@ -253,6 +266,19 @@

    Examples#> Your model may benefit from using "noncentred = TRUE" #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f8280855a9.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> Start sampling #> Running MCMC with 2 parallel chains... #> @@ -262,36 +288,36 @@

    Examples#> 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 1 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 15.2 seconds. #> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) +#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 600 / 1000 [ 60%] (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 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 55.4 seconds. +#> Chain 2 finished in 4.4 seconds. +#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 1 finished in 4.5 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 35.3 seconds. -#> Total execution time: 55.5 seconds. +#> Mean chain execution time: 4.4 seconds. +#> Total execution time: 4.6 seconds. #> plot(mod, type = 'smooths', trend_effects = TRUE) # But marginaleffects functions work without any modification -plot_predictions(mod, condition = 'season', type = 'link') +plot_predictions(mod, condition = 'season', type = 'link') # } diff --git a/docs/reference/posterior_epred.mvgam.html b/docs/reference/posterior_epred.mvgam.html index 0cf92587..fa95f5c8 100644 --- a/docs/reference/posterior_epred.mvgam.html +++ b/docs/reference/posterior_epred.mvgam.html @@ -177,71 +177,84 @@

    Examples data = simdat$data_train) #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f8239e6cbd.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> Start sampling #> Running MCMC with 4 parallel chains... #> #> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup) -#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup) -#> Chain 3 Iteration: 1 / 1000 [ 0%] (Warmup) -#> Chain 4 Iteration: 1 / 1000 [ 0%] (Warmup) #> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup) +#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup) +#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) +#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 3 Iteration: 1 / 1000 [ 0%] (Warmup) +#> Chain 3 Iteration: 100 / 1000 [ 10%] (Warmup) +#> Chain 3 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 4 Iteration: 1 / 1000 [ 0%] (Warmup) +#> Chain 4 Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 3 Iteration: 100 / 1000 [ 10%] (Warmup) -#> Chain 3 Iteration: 200 / 1000 [ 20%] (Warmup) +#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 3 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 3 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 3 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 3 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 4 Iteration: 100 / 1000 [ 10%] (Warmup) +#> Chain 3 Iteration: 600 / 1000 [ 60%] (Sampling) +#> Chain 3 Iteration: 700 / 1000 [ 70%] (Sampling) +#> Chain 3 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 4 Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 4 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 4 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 4 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 4 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 3 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 4 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 3 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 3 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 4 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 4 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 2 finished in 0.3 seconds. #> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) -#> Chain 3 Iteration: 900 / 1000 [ 90%] (Sampling) -#> Chain 4 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 3 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 3 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 4 Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 4 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 0.7 seconds. -#> Chain 3 finished in 0.8 seconds. -#> Chain 4 finished in 0.7 seconds. -#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 0.8 seconds. +#> Chain 1 finished in 0.4 seconds. +#> Chain 3 finished in 0.4 seconds. +#> Chain 4 finished in 0.4 seconds. #> #> All 4 chains finished successfully. -#> Mean chain execution time: 0.8 seconds. -#> Total execution time: 1.0 seconds. +#> Mean chain execution time: 0.4 seconds. +#> Total execution time: 0.5 seconds. #> # Compute posterior expectations expectations <- posterior_epred(mod) str(expectations) -#> num [1:2000, 1:75] 2.25 1.96 1.83 1.73 2.27 ... +#> num [1:2000, 1:75] 0.54 0.371 0.542 0.376 0.676 ... # }

    diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index fdaf67c0..e069d9ea 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -115,7 +115,7 @@
    marginaleffects
    -

    comparisons, datagrid, hypotheses, plot_comparisons, plot_predictions, plot_slopes, predictions, slopes

    +

    comparisons, datagrid, hypotheses, plot_comparisons, plot_predictions, plot_slopes, predictions, slopes

    mgcv
    diff --git a/docs/reference/score.mvgam_forecast.html b/docs/reference/score.mvgam_forecast.html index a6fccf94..a0955c25 100644 --- a/docs/reference/score.mvgam_forecast.html +++ b/docs/reference/score.mvgam_forecast.html @@ -151,6 +151,19 @@

    Examples#> Your model may benefit from using "noncentred = TRUE" #> Compiling Stan program using cmdstanr #> +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f82091fd4.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory #> Start sampling #> Running MCMC with 2 parallel chains... #> @@ -164,26 +177,26 @@

    Examples#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup) -#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) -#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup) +#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling) -#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) +#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling) -#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling) -#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 1 finished in 10.2 seconds. +#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling) -#> Chain 2 finished in 14.5 seconds. +#> Chain 2 finished in 2.6 seconds. +#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling) +#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling) +#> Chain 1 finished in 3.8 seconds. #> #> Both chains finished successfully. -#> Mean chain execution time: 12.4 seconds. -#> Total execution time: 14.7 seconds. +#> Mean chain execution time: 3.2 seconds. +#> Total execution time: 3.8 seconds. #> # Extract forecasts into a 'mvgam_forecast' object @@ -194,25 +207,25 @@

    Examplesstr(fc_scores) #> List of 4 #> $ series_1 :'data.frame': 25 obs. of 5 variables: -#> ..$ score : num [1:25] 1.886 0.349 0.316 0.973 0.276 ... +#> ..$ score : num [1:25] 0.378 0.382 0.784 0.376 0.245 ... #> ..$ in_interval : num [1:25] 1 1 1 1 1 1 1 1 1 1 ... #> ..$ interval_width: num [1:25] 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 ... #> ..$ eval_horizon : int [1:25] 1 2 3 4 5 6 7 8 9 10 ... #> ..$ score_type : chr [1:25] "drps" "drps" "drps" "drps" ... #> $ series_2 :'data.frame': 25 obs. of 5 variables: -#> ..$ score : num [1:25] 1.928 0.19 0.47 0.315 0.382 ... +#> ..$ score : num [1:25] 1.4 2.67 2.62 1.33 1.35 ... #> ..$ in_interval : num [1:25] 1 1 1 1 1 1 1 1 1 1 ... #> ..$ interval_width: num [1:25] 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 ... #> ..$ eval_horizon : int [1:25] 1 2 3 4 5 6 7 8 9 10 ... #> ..$ score_type : chr [1:25] "drps" "drps" "drps" "drps" ... #> $ series_3 :'data.frame': 25 obs. of 5 variables: -#> ..$ score : num [1:25] 0.337 0.344 1.095 0.228 0.246 ... -#> ..$ in_interval : num [1:25] 1 1 1 1 1 1 1 1 1 1 ... +#> ..$ score : num [1:25] 0.342 2.639 0.909 0.874 0.298 ... +#> ..$ in_interval : num [1:25] 1 0 1 1 1 1 1 1 1 1 ... #> ..$ interval_width: num [1:25] 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 ... #> ..$ eval_horizon : int [1:25] 1 2 3 4 5 6 7 8 9 10 ... #> ..$ score_type : chr [1:25] "drps" "drps" "drps" "drps" ... #> $ all_series:'data.frame': 25 obs. of 3 variables: -#> ..$ score : num [1:25] 4.152 0.883 1.882 1.516 0.904 ... +#> ..$ score : num [1:25] 2.12 5.69 4.31 2.58 1.89 ... #> ..$ eval_horizon: int [1:25] 1 2 3 4 5 6 7 8 9 10 ... #> ..$ score_type : chr [1:25] "sum_drps" "sum_drps" "sum_drps" "sum_drps" ... # } diff --git a/docs/reference/stability.mvgam-1.png b/docs/reference/stability.mvgam-1.png index 0f799a65..81477826 100644 Binary files a/docs/reference/stability.mvgam-1.png and b/docs/reference/stability.mvgam-1.png differ diff --git a/docs/reference/stability.mvgam-2.png b/docs/reference/stability.mvgam-2.png index 74c2a0b4..4e7e376f 100644 Binary files a/docs/reference/stability.mvgam-2.png and b/docs/reference/stability.mvgam-2.png differ diff --git a/docs/reference/stability.mvgam-3.png b/docs/reference/stability.mvgam-3.png index 10b0c961..2b520039 100644 Binary files a/docs/reference/stability.mvgam-3.png and b/docs/reference/stability.mvgam-3.png differ diff --git a/docs/reference/stability.mvgam.html b/docs/reference/stability.mvgam.html index 68a89d7e..01a71546 100644 --- a/docs/reference/stability.mvgam.html +++ b/docs/reference/stability.mvgam.html @@ -72,7 +72,7 @@

    Usage 
    stability(object, ...)
 
 # S3 method for mvgam
-stability(object, ...)
    +stability(object)
    @@ -91,12 +91,13 @@

    ArgumentsValue

    -

    A data.frame containing posterior draws for each of the above stability metrics.

    +

    An data.frame containing posterior draws for each stability metric.

    Details

    -

    These measures of stability can be used to assess how systems respond to -environmental perturbations. Using the formula for a latent VAR(1) as: +

    These measures of stability can be used to assess how important inter-series +dependencies are to the variability of a multivariate system and to ask how systems +are expected to respond to environmental perturbations. Using the formula for a latent VAR(1) as: $$ \mu_t \sim \text{MVNormal}(A(\mu_{t - 1}), \Sigma) \quad $$ @@ -107,8 +108,8 @@

    Details -

  • prop_int_adj: Same as prop_int but scaled by the number of series to facilitate -direct comparisons among systems with different numbers of interacting variables \(p\): +

  • prop_int_adj: Same as prop_int but scaled by the number of series \(p\) to facilitate +direct comparisons among systems with different numbers of interacting variables: $$ det(A)^{2/p} \quad $$

    • @@ -126,17 +127,10 @@

    Details -

  • prop_cov_offdiag: Sensitivity of \(\Sigma_{\infty}\) to inter-series error correlations +

  • prop_cov: Sensitivity of \(\Sigma_{\infty}\) to intra-series error correlations (i.e. how important are off-diagonal covariances in \(\Sigma\) for shaping \(\Sigma_{\infty}\)?), calculated as the relative magnitude of the off-diagonals in the partial derivative matrix: -$$ - [2~det(\Sigma_{\infty}) (\Sigma_{\infty}^{-1})^T] \quad - $$

    • -

  • prop_cov_diag: Sensitivity of \(\Sigma_{\infty}\) to intra-series error variances -(i.e. how important are diagonal variances in \(\Sigma\) for shaping -\(\Sigma_{\infty}\)?), calculated as the relative magnitude of the diagonals in -the partial derivative matrix: $$ [2~det(\Sigma_{\infty}) (\Sigma_{\infty}^{-1})^T] \quad $$

    • @@ -160,7 +154,9 @@

    Details -

    +

    To more directly inspect possible interactions among the time series in a latent VAR process, +you can inspect Generalized Orthogonalized Impulse Response Functions using the irf function.

    +

    References

    AR Ives, B Dennis, KL Cottingham & SR Carpenter (2003). @@ -169,7 +165,7 @@

    References

    See also

    -

    VAR,irf

    +

    VAR, irf

    Author

    @@ -187,14 +183,26 @@

    Examplesplot_mvgam_series(data = simdat$data_train, series = 'all') -# Fit a State-Space VAR(1) -mod <- mvgam(y ~ series, - trend_formula = ~ -1, +# Fit a model that uses a latent VAR(1) +mod <- mvgam(y ~ -1, + trend_formula = ~ 1, trend_model = VAR(cor = TRUE), family = gaussian(), - share_obs_params = TRUE, data = simdat$data_train, silent = 2) +#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5, +#> from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4, +#> from stan/lib/stan_math/stan/math/prim/prob.hpp:359, +#> from stan/lib/stan_math/stan/math/prim.hpp:16, +#> from stan/lib/stan_math/stan/math/rev.hpp:16, +#> from stan/lib/stan_math/stan/math.hpp:19, +#> from stan/src/stan/model/model_header.hpp:4, +#> from C:/Users/uqnclar2/AppData/Local/Temp/RtmpuihtV8/model-49f85c41daa.hpp:2: +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)': +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers +#> 194 | if (cdf_n < 0.0) +#> | +#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory # Calulate stability metrics for this system metrics <- stability(mod) @@ -202,66 +210,34 @@

    Examples# Proportion of stationary forecast distribution # attributable to lagged interactions hist(metrics$prop_int, - xlim = c(0, 1), - xlab = 'Prop_int', - main = '', - col = '#B97C7C', - border = 'white') + xlim = c(0, 1), + xlab = 'Prop_int', + main = '', + col = '#B97C7C', + border = 'white') -# Within this contribution of interactions, how important -# are inter-series interactions (offdiagonals of the A matrix) vs -# intra-series density dependence (diagonals of the A matrix)? -layout(matrix(1:2, nrow = 2)) -hist(metrics$prop_int_offdiag, - xlim = c(0, 1), - xlab = '', - main = 'Inter-series interactions', - col = '#B97C7C', - border = 'white') - -hist(metrics$prop_int_diag, - xlim = c(0, 1), - xlab = 'Contribution to interaction effect', - main = 'Intra-series interactions (density dependence)', - col = 'darkblue', - border = 'white') - -layout(1) - -# How important are inter-series error covariances -# (offdiagonals of the Sigma matrix) vs -# intra-series variances (diagonals of the Sigma matrix) for explaining -# the variance of the stationary forecast distribution? -layout(matrix(1:2, nrow = 2)) -hist(metrics$prop_cov_offdiag, - xlim = c(0, 1), - xlab = '', - main = 'Inter-series covariances', - col = '#B97C7C', - border = 'white') - -hist(metrics$prop_cov_diag, - xlim = c(0, 1), - xlab = 'Contribution to forecast variance', - main = 'Intra-series variances', - col = 'darkblue', - border = 'white') - -layout(1) +# Proportion of stationary forecast distribution +# attributable to correlated process errors +hist(metrics$prop_env, + xlim = c(0, 1), + xlab = 'Prop_env', + main = '', + col = '#B97C7C', + border = 'white') +#> Error in hist.default(metrics$prop_env, xlim = c(0, 1), xlab = "Prop_env", main = "", col = "#B97C7C", border = "white"): 'x' must be numeric # Reactivity, i.e. degree to which the system moves # away from a stable equilibrium following a perturbation -# (values > 1 suggest a more reactive, less stable system) hist(metrics$reactivity, - main = '', - xlab = 'Reactivity', - col = '#B97C7C', - border = 'white', - xlim = c(-1*max(abs(metrics$reactivity)), - max(abs(metrics$reactivity)))) + main = '', + xlab = 'Reactivity', + col = '#B97C7C', + border = 'white', + xlim = c(-1*max(abs(metrics$reactivity)), + max(abs(metrics$reactivity)))) abline(v = 0, lwd = 2.5) - + # }