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Models: GAM Year Effect Spatial

Source: vignettes/articles/models_gam_spatial.Rmd
models_gam_spatial.Rmd

Data prep

Load packages

Stratify 

s <- stratify(by = "bbs_cws", sample = TRUE)
#> Using 'bbs_cws' (standard) stratification
#> Using sample BBS data...
#> Using species Pacific Wren (sample data)
#> Stratifying data...
#>   Combining BCR 7 and NS and PEI...
#>   Renaming routes...

Prepare counts 

p <- prepare_data(s, min_max_route_years = 2)

Prepare spatial 

map <- load_map("bbs_cws")
ggplot(map, aes(fill = strata_name)) +
  geom_sf(show.legend = FALSE)

Map of North America show the CWS spatial stratification divisions as different colours


n <- prepare_spatial(p, map)
#> Preparing spatial data...
#> Identifying neighbours (non-Voronoi method)...
#> Formating neighbourhood matrices...
#> Plotting neighbourhood matrices...

Prepare model 

md <- prepare_model(n, model = "gamye", model_variant = "spatial")

Run model 

m <- run_model(md, iter_sampling = 10, iter_warmup = 10, chains = 2)
#> One set of `init` values supplied, duplicating for each chain.
#> Compiling Stan program...
#> 
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Running MCMC with 2 chains, at most 4 in parallel...
#> 
#> Chain 1 WARNING: No variance estimation is 
#> Chain 1          performed for num_warmup < 20 
#> Chain 1 Iteration:  1 / 20 [  5%]  (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Log location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 2 WARNING: No variance estimation is 
#> Chain 2          performed for num_warmup < 20 
#> Chain 2 Iteration:  1 / 20 [  5%]  (Warmup)
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Log location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpMgb4Bj/model-77e61376517b.stan', line 247, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Iteration: 11 / 20 [ 55%]  (Sampling) 
#> Chain 1 Iteration: 11 / 20 [ 55%]  (Sampling) 
#> Chain 2 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 2 finished in 20.8 seconds.
#> Chain 1 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 1 finished in 23.0 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 21.9 seconds.
#> Total execution time: 23.1 seconds.
#> Saving model output to /home/steffi/Projects/Business/ECCC/bbsBayes2/vignettes/articles/BBS_STAN_gamye_spatial_2023-01-20.rds

Explore results

Convergence 

conv <- get_convergence(m)
conv
#> # A tibble: 11,900 × 5
#>    variable_type variable       rhat ess_bulk ess_tail
#>    <chr>         <chr>         <dbl>    <dbl>    <dbl>
#>  1 lp__          lp__          1.70        10       10
#>  2 strata_raw    strata_raw[1] 1.37        10       10
#>  3 strata_raw    strata_raw[2] 1.27        10       10
#>  4 strata_raw    strata_raw[3] 0.999       10       10
#>  5 strata_raw    strata_raw[4] 1.96        10       10
#>  6 strata_raw    strata_raw[5] 1.09        10       10
#>  7 strata_raw    strata_raw[6] 1.29        10       10
#>  8 strata_raw    strata_raw[7] 0.988       10       10
#>  9 strata_raw    strata_raw[8] 1.25        10       10
#> 10 strata_raw    strata_raw[9] 1.18        10       10
#> # … with 11,890 more rows

Indices 

i <- generate_indices(model_output = m,
                      regions = c("continent",
                                  "country",
                                  "prov_state",
                                  "stratum"))
#> Processing region continent
#> Processing region country
#> Processing region prov_state
#> Processing region stratum

p <- plot_indices(i, add_observed_means = TRUE)
patchwork::wrap_plots(p, ncol = 3)

Grid of plots showing model indices over years for each regional division with blue lines and blue-grey ribbons to indicate the confidence intervals.

t <- generate_trends(indices = i)
plot_map(trends = t)

Map of North America showing the stratification areas colour-coded by trend.

Geofacet plots 

plot_geofacet(indices = i, trends = t)

A grid of plots in the shap of North America, with one plot per province or state. Each plot shows the indices over time.

Reproducibility and Clean up 

list.files(pattern = "csv|rds")
#> [1] "BBS_STAN_gamye_spatial_2023-01-20-1.csv" "BBS_STAN_gamye_spatial_2023-01-20-2.csv"
#> [3] "BBS_STAN_gamye_spatial_2023-01-20.rds"
unlink(list.files(pattern = "csv|rds"))
devtools::session_info()
#> ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────
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#>  language en_CA:en
#>  collate  en_CA.UTF-8
#>  ctype    en_CA.UTF-8
#>  tz       America/Winnipeg
#>  date     2023-01-20
#>  rstudio  2022.12.0+353 Elsbeth Geranium (desktop)
#>  pandoc   2.19.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
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