update readme

README.Rmd

@@ -227,7 +227,7 @@ description
 ```
 
 ```{r, echo=FALSE}
-str_break = function(x, width = 90L) {
+str_break = function(x, width = 105L) {
   n = nchar(x)
   if (n <= width) return(x)
   n1 = seq(1L, n, by = width)
@@ -243,12 +243,10 @@ cat(str_break(description$methods_text), sep = '\n')
 cat('\nPrimary references\n')
 for(i in seq_along(description$citations)){
   message(description$citations[[i]])
-  message()
 }
 cat('\nOther useful references\n')
 for(i in seq_along(description$other_citations)){
   message(description$other_citations[[i]])
-  message()
 }
 ```
 

README.md

@@ -246,38 +246,38 @@ summary(lynx_mvgam)
 #> 
 #> GAM coefficient (beta) estimates:
 #>                2.5%   50%  97.5% Rhat n_eff
-#> (Intercept)   6.400  6.60  6.900 1.00  1033
-#> s(season).1  -0.660 -0.13  0.340 1.00  1236
-#> s(season).2   0.730  1.30  1.900 1.00   991
-#> s(season).3   1.300  1.90  2.500 1.01   730
-#> s(season).4  -0.040  0.52  1.100 1.00   989
-#> s(season).5  -1.300 -0.68 -0.095 1.00   893
-#> s(season).6  -1.200 -0.56  0.150 1.00  1002
-#> s(season).7   0.051  0.75  1.400 1.00  1056
-#> s(season).8   0.640  1.40  2.100 1.00   840
-#> s(season).9  -0.360  0.22  0.800 1.00   767
-#> s(season).10 -1.400 -0.87 -0.380 1.00  1004
+#> (Intercept)   6.400  6.60  6.900 1.01   926
+#> s(season).1  -0.630 -0.13  0.340 1.00  1365
+#> s(season).2   0.730  1.30  1.900 1.00  1060
+#> s(season).3   1.200  1.90  2.600 1.00   993
+#> s(season).4  -0.087  0.55  1.200 1.00   975
+#> s(season).5  -1.300 -0.70 -0.074 1.00   968
+#> s(season).6  -1.300 -0.56  0.120 1.00  1252
+#> s(season).7   0.032  0.73  1.400 1.00  1259
+#> s(season).8   0.610  1.40  2.100 1.00   729
+#> s(season).9  -0.370  0.23  0.890 1.00   829
+#> s(season).10 -1.400 -0.86 -0.360 1.00  1233
 #> 
 #> Approximate significance of GAM smooths:
 #>            edf Ref.df Chi.sq p-value    
-#> s(season) 9.94     10   55.6  <2e-16 ***
+#> s(season) 9.97     10   48.6  <2e-16 ***
 #> ---
 #> 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]   0.58 0.82  0.98    1   636
-#> sigma[1] 0.38 0.47  0.60    1   838
+#> ar1[1]   0.60 0.83  0.98 1.01   671
+#> sigma[1] 0.38 0.47  0.60 1.00   750
 #> 
 #> Stan MCMC diagnostics:
 #> n_eff / iter looks reasonable for all parameters
 #> Rhat looks reasonable for all parameters
 #> 0 of 2000 iterations ended with a divergence (0%)
-#> 13 of 2000 iterations saturated the maximum tree depth of 10 (0.65%)
+#> 3 of 2000 iterations saturated the maximum tree depth of 10 (0.15%)
 #>  *Run with max_treedepth set to a larger value to avoid saturation
 #> E-FMI indicated no pathological behavior
 #> 
-#> Samples were drawn using NUTS(diag_e) at Tue Dec 03 9:24:49 AM 2024.
+#> Samples were drawn using NUTS(diag_e) at Tue Dec 03 9:38:07 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)
@@ -420,8 +420,8 @@ plot(lynx_mvgam, type = 'forecast', newdata = lynx_test)
 
 <img src="man/figures/README-unnamed-chunk-21-1.png" alt="Plotting forecast distributions using mvgam in R" width="60%" style="display: block; margin: auto;" />
 
-    #> Out of sample DRPS:
-    #> 2454.53054775
+    #> Out of sample CRPS:
+    #> 2453.7903515
 
 And the estimated latent trend component, again using the more flexible
 `plot_mvgam_...()` option to show first derivatives of the estimated
@@ -475,42 +475,31 @@ description
 ```
 
     #> Methods text skeleton
-    #> We used the R package mvgam (version 1.1.4; Clark & Wells, 2023) to construct, fit and int
-    #> errogate the model. mvgam fits Bayesian State-Space models that can include flexible predi
-    #> ctor effects in both the process and observation components by incorporating functionaliti
-    #> es from the brms (Bürkner 2017), mgcv (Wood 2017) and splines2 (Wang & Yan, 2023) packages
-    #> . The mvgam-constructed model and observed data were passed to the probabilistic programmi
-    #> ng environment Stan (version 2.34.1; Carpenter et al. 2017, Stan Development Team 2024), s
-    #> pecifically through the cmdstanr interface (Gabry & Češnovar, 2021). We ran 4 Hamiltonian 
-    #> Monte Carlo chains for 500 warmup iterations and 500 sampling iterations for joint posteri
-    #> or estimation. Rank normalized split Rhat (Vehtari et al. 2021) and effective sample sizes
-    #>  were used to monitor convergence.
+    #> We used the R package mvgam (version 1.1.4; Clark & Wells, 2023) to construct, fit and interrogate the mo
+    #> del. mvgam fits Bayesian State-Space models that can include flexible predictor effects in both the proce
+    #> ss and observation components by incorporating functionalities from the brms (Bürkner 2017), mgcv (Wood 2
+    #> 017) and splines2 (Wang & Yan, 2023) packages. The mvgam-constructed model and observed data were passed 
+    #> to the probabilistic programming environment Stan (version 2.34.1; Carpenter et al. 2017, Stan Developmen
+    #> t Team 2024), specifically through the cmdstanr interface (Gabry & Češnovar, 2021). We ran 4 Hamiltonian 
+    #> Monte Carlo chains for 500 warmup iterations and 500 sampling iterations for joint posterior estimation. 
+    #> Rank normalized split Rhat (Vehtari et al. 2021) and effective sample sizes were used to monitor converge
+    #> nce.
 
     #> 
     #> Primary references
     #> Clark, NJ and Wells K (2022). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. Methods in Ecology and Evolution, 14, 771-784. doi.org/10.1111/2041-210X.13974
-    #> 
     #> Bürkner, PC (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01
-    #> 
     #> Wood, SN (2017). Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC.
-    #> 
     #> Wang W and Yan J (2021). Shape-Restricted Regression Splines with R Package splines2. Journal of Data Science, 19(3), 498-517. doi:10.6339/21-JDS1020 <https://doi.org/10.6339/21-JDS1020>.
-    #> 
     #> Carpenter, B, Gelman, A, Hoffman, MD, Lee, D, Goodrich, B, Betancourt, M, Brubaker, M, Guo, J, Li, P and Riddell, A (2017). Stan: A probabilistic programming language. Journal of Statistical Software 76.
-    #> 
     #> Gabry J, Češnovar R, Johnson A, and Bronder S (2024). cmdstanr: R Interface to 'CmdStan'. https://mc-stan.org/cmdstanr/, https://discourse.mc-stan.org.
-    #> 
     #> Vehtari A, Gelman A, Simpson D, Carpenter B, and Bürkner P (2021). “Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion).” Bayesian Analysis 16(2) 667-718. https://doi.org/10.1214/20-BA1221.
     #> 
-    #> 
     #> Other useful references
     #> Arel-Bundock V (2024). marginaleffects: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests. R package version 0.19.0.4, https://marginaleffects.com/.
     #> Gabry J, Simpson D, Vehtari A, Betancourt M, and Gelman A (2019). “Visualization in Bayesian workflow.” Journal of the Royal Statatistical Society A, 182, 389-402. doi:10.1111/rssa.12378.
-    #> 
     #> Vehtari A, Gelman A, and Gabry J (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27, 1413-1432. doi:10.1007/s11222-016-9696-4.
-    #> 
     #> Bürkner, PC, Gabry, J, and Vehtari, A. (2020). Approximate leave-future-out cross-validation for Bayesian time series models. Journal of Statistical Computation and Simulation, 90(14), 2499–2523. https://doi.org/10.1080/00949655.2020.1783262
-    #> 
 
 ## Extended observation families
 
@@ -629,7 +618,7 @@ summary(mod, include_betas = FALSE)
 #> 0 of 2000 iterations saturated the maximum tree depth of 10 (0%)
 #> E-FMI indicated no pathological behavior
 #> 
-#> Samples were drawn using NUTS(diag_e) at Tue Dec 03 9:26:10 AM 2024.
+#> Samples were drawn using NUTS(diag_e) at Tue Dec 03 9:39:28 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)