Update README

README.Rmd

@@ -29,7 +29,7 @@ knitr::opts_chunk$set(
 
 ## Overview
 
-The **coevolve** package allows the user to fit Bayesian dynamic coevolutionary
+The **coevolve** package allows the user to fit Bayesian generalized dynamic
 phylogenetic models in Stan. These models can be used to estimate how variables 
 have coevolved over evolutionary time and to assess causal directionality
 (X → Y vs. Y → X) and contingencies (X, then Y) in evolution.
@@ -82,7 +82,6 @@ fit <-
     prior = list(A_offdiag = "normal(0, 2)"),
     # arguments for cmdstanr
     parallel_chains = 4,
-    iter_sampling = 500,
     refresh = 0,
     seed = 1
   )
@@ -99,7 +98,7 @@ posterior draws for the model parameters. In particular, the output shows the
 autoregressive selection effects (i.e., the effect of a variable on itself in 
 the future), the cross selection effects (i.e., the effect of a variable on 
 another variable in the future), the amount of drift, continuous time intercept 
-parameters for the schocastic differential equation, and cutpoints for the 
+parameters for the stochastic differential equation, and cutpoints for the 
 ordinal variables.
 
 While this summary output is useful as a first glance, it is difficult to

README.md

@@ -12,8 +12,8 @@
 
 ## Overview
 
-The **coevolve** package allows the user to fit Bayesian dynamic
-coevolutionary phylogenetic models in Stan. These models can be used to
+The **coevolve** package allows the user to fit Bayesian generalized
+dynamic phylogenetic models in Stan. These models can be used to
 estimate how variables have coevolved over evolutionary time and to
 assess causal directionality (X → Y vs. Y → X) and contingencies (X,
 then Y) in evolution.
@@ -70,21 +70,23 @@ fit <-
     prior = list(A_offdiag = "normal(0, 2)"),
     # arguments for cmdstanr
     parallel_chains = 4,
-    iter_sampling = 500,
     refresh = 0,
     seed = 1
   )
+```
+
+```r
 #> Running MCMC with 4 parallel chains...
 #> 
-#> Chain 3 finished in 972.1 seconds.
-#> Chain 1 finished in 976.9 seconds.
-#> Chain 4 finished in 995.5 seconds.
-#> Chain 2 finished in 1003.6 seconds.
+#> Chain 1 finished in 442.7 seconds.
+#> Chain 2 finished in 574.2 seconds.
+#> Chain 3 finished in 606.5 seconds.
+#> Chain 4 finished in 612.8 seconds.
 #> 
 #> All 4 chains finished successfully.
-#> Mean chain execution time: 987.0 seconds.
-#> Total execution time: 1004.0 seconds.
-#> Warning: 35 of 2000 (2.0%) transitions ended with a divergence.
+#> Mean chain execution time: 559.0 seconds.
+#> Total execution time: 613.0 seconds.
+#> Warning: 22 of 4000 (1.0%) transitions ended with a divergence.
 #> See https://mc-stan.org/misc/warnings for details.
 ```
 
@@ -96,39 +98,39 @@ summary(fit)
 #>            religious_authority = ordered_logistic 
 #>      Data: authority$data (Number of observations: 97)
 #> Phylogeny: authority$phylogeny (Number of trees: 1)
-#>     Draws: 4 chains, each with iter = 500; warmup = 1000; thin = 1
-#>            total post-warmup draws = 2000
+#>     Draws: 4 chains, each with iter = 1000; warmup = 1000; thin = 1
+#>            total post-warmup draws = 4000
 #> 
 #> Autoregressive selection effects:
 #>                     Estimate Est.Error  2.5% 97.5% Rhat Bulk_ESS Tail_ESS
-#> political_authority    -0.67      0.53 -1.97 -0.02 1.00     1015      769
-#> religious_authority    -0.78      0.57 -2.15 -0.04 1.00     1265     1061
+#> political_authority    -0.67      0.53 -1.99 -0.03 1.00     2120     1768
+#> religious_authority    -0.78      0.59 -2.20 -0.03 1.00     2260     1766
 #> 
 #> Cross selection effects:
 #>                                           Estimate Est.Error  2.5% 97.5% Rhat Bulk_ESS Tail_ESS
-#> political_authority ⟶ religious_authority     2.28      0.98  0.31  4.20 1.01      874      951
-#> religious_authority ⟶ political_authority     1.71      1.09 -0.36  3.95 1.01      494     1012
+#> political_authority ⟶ religious_authority     2.32      1.03  0.38  4.45 1.00     1567     1971
+#> religious_authority ⟶ political_authority     1.82      1.11 -0.28  4.07 1.00     1288     2124
 #> 
 #> Drift parameters:
 #>                                              Estimate Est.Error  2.5% 97.5% Rhat Bulk_ESS Tail_ESS
-#> sd(political_authority)                          1.98      0.80  0.30  3.51 1.01      402      344
-#> sd(religious_authority)                          1.28      0.77  0.07  2.93 1.01      447      776
-#> cor(political_authority,religious_authority)     0.27      0.31 -0.42  0.77 1.00     1303     1340
+#> sd(political_authority)                          1.95      0.83  0.27  3.50 1.01      801     1193
+#> sd(religious_authority)                          1.29      0.80  0.06  2.94 1.00      761     1327
+#> cor(political_authority,religious_authority)     0.26      0.32 -0.44  0.78 1.00     2732     2641
 #> 
 #> Continuous time intercept parameters:
 #>                     Estimate Est.Error  2.5% 97.5% Rhat Bulk_ESS Tail_ESS
-#> political_authority     0.20      0.95 -1.62  2.05 1.00     2342     1495
-#> religious_authority     0.27      0.90 -1.42  2.02 1.01     2094     1229
+#> political_authority     0.21      0.95 -1.56  2.09 1.00     4088     1162
+#> religious_authority     0.21      0.94 -1.65  2.06 1.00     5112     2655
 #> 
 #> Ordinal cutpoint parameters:
 #>                        Estimate Est.Error  2.5% 97.5% Rhat Bulk_ESS Tail_ESS
-#> political_authority[1]    -1.34      0.86 -2.97  0.33 1.00     1113     1468
-#> political_authority[2]    -0.58      0.83 -2.22  1.08 1.00     1257     1585
-#> political_authority[3]     1.61      0.86 -0.00  3.31 1.00     1371     1540
-#> religious_authority[1]    -1.50      0.92 -3.29  0.34 1.00     1647     1280
-#> religious_authority[2]    -0.81      0.90 -2.54  0.93 1.00     1693     1464
-#> religious_authority[3]     1.61      0.94 -0.17  3.52 1.00     1751     1648
-#> Warning: There were 35 divergent transitions after warmup.
+#> political_authority[1]    -1.31      0.91 -3.12  0.51 1.00     2736     2683
+#> political_authority[2]    -0.56      0.89 -2.32  1.24 1.00     3129     2891
+#> political_authority[3]     1.64      0.92 -0.10  3.55 1.00     3268     2956
+#> religious_authority[1]    -1.53      0.92 -3.31  0.24 1.00     3019     3066
+#> religious_authority[2]    -0.84      0.90 -2.59  0.94 1.00     3329     3051
+#> religious_authority[3]     1.60      0.95 -0.17  3.51 1.00     3205     3102
+#> Warning: There were 22 divergent transitions after warmup.
 #> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 ```
 
@@ -137,7 +139,7 @@ the posterior draws for the model parameters. In particular, the output
 shows the autoregressive selection effects (i.e., the effect of a
 variable on itself in the future), the cross selection effects (i.e.,
 the effect of a variable on another variable in the future), the amount
-of drift, continuous time intercept parameters for the schocastic
+of drift, continuous time intercept parameters for the stochastic
 differential equation, and cutpoints for the ordinal variables.
 
 While this summary output is useful as a first glance, it is difficult
@@ -155,7 +157,7 @@ increase in another variable.
 
 ``` r
 coev_plot_delta_theta(fit)
-#> Warning: Removed 123 rows containing non-finite outside the scale range (`stat_density()`).
+#> Warning: Removed 235 rows containing non-finite outside the scale range (`stat_density()`).
 ```
 
 <img src="man/figures/README-authority-delta-theta-1.png" width="60%" style="display: block; margin: auto;" />