From b282f45f6a8c41824069ec5c5825a60d90571dae Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Mon, 1 Jul 2024 08:35:48 +1000 Subject: [PATCH] update README --- README.Rmd | 8 ++-- README.md | 52 +++++++++++----------- cran-comments.md | 7 ++- man/figures/README-unnamed-chunk-12-1.png | Bin 21301 -> 20622 bytes man/figures/README-unnamed-chunk-13-1.png | Bin 31205 -> 19807 bytes man/figures/README-unnamed-chunk-14-1.png | Bin 8943 -> 9277 bytes man/figures/README-unnamed-chunk-15-1.png | Bin 17033 -> 17586 bytes man/figures/README-unnamed-chunk-16-1.png | Bin 6994 -> 6978 bytes man/figures/README-unnamed-chunk-17-1.png | Bin 9162 -> 9529 bytes man/figures/README-unnamed-chunk-18-1.png | Bin 11944 -> 11758 bytes man/figures/README-unnamed-chunk-19-1.png | Bin 12524 -> 13959 bytes man/figures/README-unnamed-chunk-20-1.png | Bin 14056 -> 13977 bytes man/figures/README-unnamed-chunk-21-1.png | Bin 16161 -> 16366 bytes man/figures/README-unnamed-chunk-22-1.png | Bin 14513 -> 14354 bytes man/figures/README-unnamed-chunk-23-1.png | Bin 36954 -> 36819 bytes 15 files changed, 32 insertions(+), 35 deletions(-) diff --git a/README.Rmd b/README.Rmd index 59cb6091..729b9f03 100644 --- a/README.Rmd +++ b/README.Rmd @@ -116,14 +116,14 @@ Inspect the series in a bit more detail using `mvgam`'s plotting utility plot_mvgam_series(data = lynx_train, y = 'population') ``` -Formulate an `mvgam` model; this model fits a GAM in which a cyclic smooth function for `season` is estimated jointly with a full time series model for the temporal process (in this case an `AR3` process). We assume the outcome follows a Poisson distribution and will condition the model in `Stan` using MCMC sampling with the `Cmdstan` interface: +Formulate an `mvgam` model; this model fits a GAM in which a cyclic smooth function for `season` is estimated jointly with a full time series model for the temporal process (in this case an `AR1` process). We assume the outcome follows a Poisson distribution and will condition the model in `Stan` using MCMC sampling with the `Cmdstan` interface: ```{r, include=FALSE} lynx_mvgam <- mvgam(data = lynx_train, newdata = lynx_test, formula = population ~ s(season, bs = 'cc', k = 12), knots = list(season = c(0.5, 19.5)), family = poisson(), - trend_model = AR(p = 3), + trend_model = AR(p = 1), noncentred = TRUE, use_stan = TRUE) ``` @@ -134,7 +134,7 @@ lynx_mvgam <- mvgam(population ~ s(season, bs = 'cc', k = 12), data = lynx_train, newdata = lynx_test, family = poisson(), - trend_model = AR(p = 3), + trend_model = AR(p = 1), backend = 'cmdstanr') ``` @@ -206,7 +206,7 @@ text(1, 0.8, cex = 1.5, label="Trend component", pos = 4, col="#7C0000", family = 'serif') ``` -Both components contribute to forecast uncertainty. Diagnostics of the model can also be performed using `mvgam`. Have a look at the model's residuals, which are posterior empirical quantiles of Dunn-Smyth randomised quantile residuals so should follow approximate normality. We are primarily looking for a lack of autocorrelation, which would suggest our AR3 model is appropriate for the latent trend +Both components contribute to forecast uncertainty. Diagnostics of the model can also be performed using `mvgam`. Have a look at the model's residuals, which are posterior empirical quantiles of Dunn-Smyth randomised quantile residuals so should follow approximate normality. We are primarily looking for a lack of autocorrelation, which would suggest our AR1 model is appropriate for the latent trend ```{r, fig.width=6.5, fig.height=6.5, dpi=160, fig.alt = "Plotting Dunn-Smyth residuals for time series analysis in mvgam and R"} plot(lynx_mvgam, type = 'residuals') ``` diff --git a/README.md b/README.md index 1fcef8eb..5c6d535a 100644 --- a/README.md +++ b/README.md @@ -189,7 +189,7 @@ plot_mvgam_series(data = lynx_train, y = 'population') Formulate an `mvgam` model; this model fits a GAM in which a cyclic smooth function for `season` is estimated jointly with a full time -series model for the temporal process (in this case an `AR3` process). +series model for the temporal process (in this case an `AR1` process). We assume the outcome follows a Poisson distribution and will condition the model in `Stan` using MCMC sampling with the `Cmdstan` interface: @@ -199,7 +199,7 @@ lynx_mvgam <- mvgam(population ~ s(season, bs = 'cc', k = 12), data = lynx_train, newdata = lynx_test, family = poisson(), - trend_model = AR(p = 3), + trend_model = AR(p = 1), backend = 'cmdstanr') ``` @@ -219,7 +219,7 @@ summary(lynx_mvgam) #> log #> #> Trend model: -#> AR(p = 3) +#> AR(p = 1) #> #> N series: #> 1 @@ -234,31 +234,29 @@ summary(lynx_mvgam) #> #> #> GAM coefficient (beta) estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> (Intercept) 6.20 6.600 6.90 1.00 1100 -#> s(season).1 -0.56 -0.070 0.51 1.00 1202 -#> s(season).2 0.57 1.200 1.90 1.00 1102 -#> s(season).3 1.10 1.900 2.70 1.00 1073 -#> s(season).4 -0.14 0.520 1.20 1.00 1169 -#> s(season).5 -1.30 -0.590 0.18 1.00 961 -#> s(season).6 -1.10 -0.350 0.41 1.00 1257 -#> s(season).7 -0.19 0.670 1.40 1.00 1171 -#> s(season).8 0.10 1.100 1.90 1.01 627 -#> s(season).9 -0.54 0.051 0.63 1.00 1044 -#> s(season).10 -1.40 -0.950 -0.54 1.00 1069 +#> 2.5% 50% 97.5% Rhat n_eff +#> (Intercept) 6.400 6.60 6.900 1 709 +#> s(season).1 -0.680 -0.13 0.360 1 1111 +#> s(season).2 0.730 1.30 1.900 1 1091 +#> s(season).3 1.200 1.90 2.500 1 733 +#> s(season).4 -0.085 0.54 1.100 1 900 +#> s(season).5 -1.300 -0.68 -0.089 1 850 +#> s(season).6 -1.200 -0.54 0.130 1 1139 +#> s(season).7 0.074 0.71 1.400 1 1063 +#> s(season).8 0.620 1.30 2.100 1 715 +#> s(season).9 -0.380 0.21 0.830 1 839 +#> s(season).10 -1.400 -0.85 -0.350 1 871 #> #> Approximate significance of GAM smooths: #> edf Ref.df Chi.sq p-value -#> s(season) 9.93 10 46.1 1.5e-06 *** +#> s(season) 9.97 10 48.3 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> -#> Latent trend AR parameter estimates: -#> 2.5% 50% 97.5% Rhat n_eff -#> ar1[1] 0.600 0.86 0.99 1.00 1331 -#> ar2[1] 0.038 0.45 0.86 1.00 421 -#> ar3[1] 0.079 0.45 0.93 1.01 379 -#> sigma[1] 0.360 0.46 0.59 1.00 663 +#> Latent trend parameter AR estimates: +#> 2.5% 50% 97.5% Rhat n_eff +#> ar1[1] 0.60 0.83 0.97 1 656 +#> sigma[1] 0.39 0.47 0.62 1 715 #> #> Stan MCMC diagnostics: #> n_eff / iter looks reasonable for all parameters @@ -267,7 +265,7 @@ summary(lynx_mvgam) #> 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 Fri Jun 28 3:39:52 PM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Jul 01 8:32:43 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) @@ -387,8 +385,8 @@ series (testing and training) ``` r plot(lynx_mvgam, type = 'forecast', newdata = lynx_test) -#> Out of sample CRPS: -#> 1846.84576525 +#> Out of sample DRPS: +#> 2420.7128115 ``` Plotting forecast distributions using mvgam in R @@ -425,7 +423,7 @@ model can also be performed using `mvgam`. Have a look at the model’s residuals, which are posterior empirical quantiles of Dunn-Smyth randomised quantile residuals so should follow approximate normality. We are primarily looking for a lack of autocorrelation, which would suggest -our AR3 model is appropriate for the latent trend +our AR1 model is appropriate for the latent trend ``` r plot(lynx_mvgam, type = 'residuals') @@ -552,7 +550,7 @@ summary(mod, include_betas = FALSE) #> 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 Fri Jun 28 3:40:36 PM 2024. +#> Samples were drawn using NUTS(diag_e) at Mon Jul 01 8:34:07 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) diff --git a/cran-comments.md b/cran-comments.md index 85114a96..d77db677 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,8 +1,7 @@ -## Version 1.1.1 +## Version 1.1.2 -## Response to previous check comments -* Changed indexing of an internal c++ function after Prof Brian Ripley’s email: Dear maintainer, Please see the problems shown on https://cran.r-project.org/web/checks/check_results_mvgam.html. Please correct before 2024-05-22 to safely retain your package on CRAN. The CRAN Team. I presume this was triggered by a memory 'Invalid read of size' message from `valgrind`, which occurred in one of the examples and one of the tests. Strangely this behaviour did not occur in other examples that use identical codes, so I suspect it may have been a false positive. But nevertheless I have made some changes and re-checked with `valgrind` (see '`valgrind` memory check results' below) -* Also reduced sizes of vignette html files in response to several NOTEs about the large package install size +## Summary of changes +This version brings a few efficiency updates and added functionality to enhance the user interface. 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