diff --git a/.gitignore b/.gitignore
index 4c95a7b17d2e3f0cf797fca434d0d6cf519e441d..ac6cec0ae6148953f7080d9e8bdbc5772b017dda 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,5 +11,5 @@ renv/cache/
# Data files
data/
-R/05_performance_analysis_files
-R/05_performance_analysis.html
\ No newline at end of file
+R/*/
+R/*.html
\ No newline at end of file
diff --git a/R/02_02_functional_traits_preparation.R b/R/02_02_functional_traits_preparation.R
index 9d66867b7616d8ca25895d2442a8ef3ff1676c3b..0268d5337151598c44acd5c2b5b8a2c3edff7357 100644
--- a/R/02_02_functional_traits_preparation.R
+++ b/R/02_02_functional_traits_preparation.R
@@ -30,15 +30,13 @@ traits_matched = trait_names_matched %>%
save(traits_matched, file = "data/r_objects/traits_matched.RData")
-# Calculate Distances
-library("vegan")
-
-load("data/r_objects/range_maps.RData")
-load("data/r_objects/traits_matched.RData")
-target_species = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
+# Clean trait data
+diet_cols = c("Diet.Inv", "Diet.Vend", "Diet.Vect", "Diet.Vfish", "Diet.Vunk", "Diet.Scav", "Diet.Fruit", "Diet.Nect", "Diet.Seed", "Diet.PlantO")
+strata_cols = c("ForStrat.Value")
+activity_cols = c("Activity.Nocturnal", "Activity.Crepuscular", "Activity.Diurnal")
+bodymass_cols = c("BodyMass.Value")
-traits_target = traits_matched %>%
- dplyr::filter(species %in% !!target_species) %>%
+traits_proc = traits_matched %>%
mutate(match_genus = stringr::str_detect(species, Genus),
match_epithet = stringr::str_detect(species, Species)) %>%
dplyr::group_by(species) %>%
@@ -46,17 +44,15 @@ traits_target = traits_matched %>%
if (nrow(.x) == 1) {
return (.x)
}
-
x_mod = .x
- print(x_mod)
while (nrow(x_mod) > 1){
if(any(isFALSE(x_mod$match_genus))){
x_mod = dplyr::filter(x_mod, isTRUE(match_genus))
next
}
- if(any(isFALSE(x_mod$match_species))){
- x_mod = dplyr::filter(x_mod, isTRUE(match_species))
+ if(any(isFALSE(x_mod$match_epithet))){
+ x_mod = dplyr::filter(x_mod, isTRUE(match_epithet))
next
}
@@ -68,19 +64,23 @@ traits_target = traits_matched %>%
} else {
return(.x[1,])
}
-
- })
+ }) %>%
+ dplyr::select(c("species", diet_cols, strata_cols, activity_cols, bodymass_cols))
+save(traits_proc, file = "data/r_objects/traits_proc.RData")
-# some pre-processing
-traits_target$`ForStrat.Value.Proc` = as.numeric(factor(traits_target$`ForStrat.Value`, levels = c("G", "S", "Ar", "A")))
-traits_target$`BodyMass.Value.Proc` = scale(log(traits_target$`BodyMass.Value`))
+# Calculate Distances
+library("vegan")
-# Define columns
-diet_cols = c("Diet.Inv", "Diet.Vend", "Diet.Vect", "Diet.Vfish", "Diet.Vunk", "Diet.Scav", "Diet.Fruit", "Diet.Nect", "Diet.Seed", "Diet.PlantO")
-strata_cols = c("ForStrat.Value.Proc")
-activity_cols = c("Activity.Nocturnal", "Activity.Crepuscular", "Activity.Diurnal")
-bodymass_cols = c("BodyMass.Value.Proc")
+load("data/r_objects/range_maps.RData")
+load("data/r_objects/traits_proc.RData")
+
+target_species = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
+traits_target = dplyr::filter(traits_proc, species %in% target_species)
+
+# some pre-processing
+traits_target$`ForStrat.Value` = as.numeric(factor(traits_target$`ForStrat.Value`, levels = c("G", "S", "Ar", "A")))
+traits_target$`BodyMass.Value` = scale(log(traits_target$`BodyMass.Value`))
diet_dist = vegan::vegdist(traits_target[,diet_cols], "bray")
foraging_dist = dist(traits_target[,strata_cols])
diff --git a/R/05_01_performance_analysis.qmd b/R/05_01_performance_analysis.qmd
index 2fea07f540a92561912fe6a7cc07aae8ca55ac9d..7a99fe138d410715ea78fc696238aa5483ab2aec 100644
--- a/R/05_01_performance_analysis.qmd
+++ b/R/05_01_performance_analysis.qmd
@@ -529,7 +529,7 @@ bslib::card(plot, full_screen = T)
Functional groups were assigned based on taxonomic order. The following groupings were used:
| Functional group | Taxomic orders |
-|-------------------|-----------------------------------------------------|
+|--------------------|----------------------------------------------------|
| large ground-dwelling | Carnivora, Artiodactyla, Cingulata, Perissodactyla |
| small ground-dwelling | Rodentia, Didelphimorphia, Soricomorpha, Paucituberculata, Lagomorpha |
| arboreal | Primates, Pilosa |
diff --git a/R/05_02_MSDM_comparison.qmd b/R/05_02_MSDM_comparison.qmd
index 667edb695e5cae12e2de2fff9ea5504d54f51ad0..6de03270e0b10dad513009a5214738afa0b560c2 100644
--- a/R/05_02_MSDM_comparison.qmd
+++ b/R/05_02_MSDM_comparison.qmd
@@ -10,6 +10,7 @@ library(tidyverse)
library(sf)
library(plotly)
library(DT)
+library(shiny)
load("../data/r_objects/msdm_results_embedding_raw.RData")
load("../data/r_objects/msdm_results_embedding_traits_static.RData")
@@ -22,7 +23,12 @@ load("../data/r_objects/msdm_results_embedding_range_trained.RData")
sf::sf_use_s2(use_s2 = FALSE)
```
+
```{r globals, echo = FALSE, include = FALSE}
+# Select metrics
+focal_metrics = c("auc", "f1", "accuracy") # There's a weird bug in plotly that scrambles up lines when using more than three groups
+
+# Prepare final dataframes
results_embedding_raw = msdm_results_embedding_raw %>%
dplyr::mutate(
embedding = "raw",
@@ -48,10 +54,57 @@ results_embedding_informed_merged = lapply(results_embedding_informed, function(
results_final = results_embedding_raw %>%
bind_rows(results_embedding_informed_merged) %>%
- drop_na()
+ drop_na() %>%
+ mutate(
+ model = recode(
+ model,
+ "MSDM_embed" = "M_STD",
+ "MSDM_embed_informed_phylo_static" = "M_PS",
+ "MSDM_embed_informed_phylo_trained" = "M_PT",
+ "MSDM_embed_informed_traits_static" = "M_TS",
+ "MSDM_embed_informed_traits_trained" = "M_TT",
+ "MSDM_embed_informed_range_static" = "M_RS",
+ "MSDM_embed_informed_range_trained" = "M_RT"
+ ),
+ across(all_of(focal_metrics), round, 3)
+ )
+
+results_final_long = results_final %>%
+ tidyr::pivot_longer(focal_metrics, names_to = "metric", values_to = "value")
+
+delta_performance = results_final %>%
+ dplyr::select(all_of(c("species", "model", focal_metrics))) %>%
+ tidyr::pivot_wider(id_cols = species, names_from = model, values_from = c(auc, accuracy, f1)) %>%
+ dplyr::mutate(
+ across(starts_with("auc_M_"), ~ . - auc_M_STD),
+ across(starts_with("accuracy_M_"), ~ . - accuracy_M_STD),
+ across(starts_with("f1_M_"), ~ . - f1_M_STD)
+ ) %>%
+ dplyr::select(-auc_M_STD, -accuracy_M_STD, -f1_M_STD) %>%
+ pivot_longer(-species, names_to = c("metric", "model"), names_pattern = "(^[a-zA-Z1-9]+)_(.+$)", values_to = "delta")
+
+# Regression functions
+asym_regression = function(x, y){
+ nls_fit = nls(y ~ 1 - (1-b) * exp(-c * log(x)), start = list(b = 0.1, c = 0.1))
+ new_x = exp(seq(log(min(x)), log(max(x)), length.out = 100))
+ data.frame(
+ x = new_x,
+ fit = predict(nls_fit, newdata = data.frame(x = new_x))
+ )
+}
+
+lin_regression = function(x, y){
+ glm_fit = suppressWarnings(glm(y~x, family = "binomial"))
+ new_x = seq(min(x), max(x), length.out = 100)
+ data.frame(
+ x = new_x,
+ fit = predict(glm_fit, newdata = data.frame(x = new_x), type = "response")
+ )
+}
```
-```{r globals, echo = FALSE}
+
+```{r create_summaries, echo = FALSE}
auc_overview = results_final %>%
pivot_wider(names_from = model, values_from = auc, id_cols = c(species, obs)) %>%
dplyr::arrange(obs)
@@ -63,4 +116,228 @@ accuracy_overview = results_final %>%
f1_overview = results_final %>%
pivot_wider(names_from = model, values_from = f1, id_cols = c(species, obs)) %>%
dplyr::arrange(obs)
-```
\ No newline at end of file
+```
+
+## *Model overview*
+
+::: panel-tabset
+
+### *AUC*
+
+```{r echo = FALSE}
+datatable(
+ auc_overview,
+ options = list(
+ pageLength = 10,
+ initComplete = htmlwidgets::JS(
+ "function(settings, json) {
+ $(this.api().table().container()).css({'font-size': '10pt'});
+ }"
+ ),
+ autoWidth = TRUE,
+ columnDefs = list(list(width = "250px", targets = 1))
+ )
+)
+```
+
+### *Accuracy*
+
+```{r echo = FALSE}
+datatable(
+ accuracy_overview,
+ options = list(
+ pageLength = 10,
+ initComplete = htmlwidgets::JS(
+ "function(settings, json) {
+ $(this.api().table().container()).css({'font-size': '10pt'});
+ }"
+ ),
+ autoWidth = TRUE,
+ columnDefs = list(list(width = "250px", targets = 1))
+ )
+)
+```
+
+### *F1 score*
+
+```{r echo = FALSE}
+datatable(
+ f1_overview,
+ options = list(
+ pageLength = 10,
+ initComplete = htmlwidgets::JS(
+ "function(settings, json) {
+ $(this.api().table().container()).css({'font-size': '10pt'});
+ }"
+ ),
+ autoWidth = TRUE,
+ columnDefs = list(list(width = "250px", targets = 1))
+ )
+)
+```
+:::
+
+## *Model comparison*
+### *Number of records*
+
+```{r performance_vs_occurrences, echo = FALSE, message=FALSE, warnings=FALSE}
+# Dropdown options
+plotly_buttons = list()
+for(metric in focal_metrics){
+ plotly_buttons[[length(plotly_buttons) + 1]] = list(method = "restyle", args = list("transforms[0].value", metric), label = metric)
+}
+
+df_plot = results_final_long
+
+# Calculate regression lines for each model and metric combination
+suppressWarnings({
+ regression_lines = df_plot %>%
+ group_by(model, metric) %>%
+ group_modify(~asym_regression(.x$obs, .x$value))
+})
+
+# Create base plot
+plot <- plot_ly() %>%
+ layout(
+ title = "Model Performance vs. Number of observations",
+ xaxis = list(title = "Number of observations", type = "log"),
+ yaxis = list(title = "Value"),
+ legend = list(x = 1.1, y = 0.5), # Move legend to the right of the plot
+ margin = list(r = 150), # Add right margin to accommodate legend
+ hovermode = 'closest',
+ updatemenus = list(
+ list(
+ type = "dropdown",
+ active = 0,
+ buttons = plotly_buttons
+ )
+ )
+ )
+
+# Points
+for (model_name in unique(df_plot$model)) {
+ plot = plot %>%
+ add_markers(
+ data = filter(df_plot, model == model_name),
+ x = ~obs,
+ y = ~value,
+ color = model_name, # Set color to match legendgroup
+ legendgroup = model_name,
+ opacity = 0.6,
+ name = ~model,
+ hoverinfo = 'text',
+ text = ~paste("Species:", species, "<br>Observations:", obs, "<br>Value:", round(value, 3)),
+ transforms = list(
+ list(
+ type = 'filter',
+ target = ~metric,
+ operation = '=',
+ value = focal_metrics[1]
+ )
+ )
+ )
+}
+
+# Add regression lines
+for(model_name in unique(df_plot$model)){
+ reg_data = dplyr::filter(regression_lines, model == model_name)
+ plot = plot %>%
+ add_lines(
+ data = reg_data,
+ x = ~x,
+ y = ~fit,
+ color = model_name, # Set color to match legendgroup
+ legendgroup = model_name,
+ name = paste(model_name, '(fit)'),
+ showlegend = FALSE,
+ transforms = list(
+ list(
+ type = 'filter',
+ target = ~metric,
+ operation = '=',
+ value = focal_metrics[1]
+ )
+ )
+ )
+}
+
+bslib::card(plot, full_screen = T)
+```
+
+```{r trait_pca, echo = FALSE, message=FALSE, warnings=FALSE}
+load("../data/r_objects/traits_proc.RData")
+
+# Preprocess traits for PCA
+traits_num = traits_proc %>%
+ ungroup() %>%
+ dplyr::mutate(temp_id = row_number()) %>%
+ pivot_wider(
+ names_from = ForStrat.Value,
+ values_from = temp_id,
+ values_fn = function(x) 1,
+ values_fill = 0,
+ names_prefix = "ForStrat."
+ ) %>%
+ drop_na(species) %>%
+ column_to_rownames(var = "species") %>%
+ scale()
+
+# Run PCA
+traits_pca = prcomp(traits_num)
+species_vcts = traits_pca$x %>%
+ as.data.frame() %>%
+ rownames_to_column(var = "species")
+```
+
+
+```{r performance_vs_traits, echo = FALSE, message=FALSE, warnings=FALSE}
+# Create plot df
+df_plot = dplyr::inner_join(delta_performance, species_vcts)
+
+# Buttons
+plotly_buttons = list()
+for(metric in unique(df_plot$model)){
+ plotly_buttons[[length(plotly_buttons) + 1]] = list(method = "restyle", args = list("transforms[0].value", metric), label = metric)
+}
+
+# Create base plot
+plot <- plot_ly() %>%
+ layout(
+ title = "Delta Performance Traits",
+ xaxis = list(title = "PC1"),
+ yaxis = list(title = "PC2"),
+ legend = list(x = 1.1, y = 0.5), # Move legend to the right of the plot
+ margin = list(r = 150), # Add right margin to accommodate legend
+ hovermode = 'closest',
+ updatemenus = list(
+ list(
+ type = "dropdown",
+ active = 0,
+ buttons = plotly_buttons
+ )
+ )
+ )
+
+# Points
+for (metric in unique(df_plot$metric)) {
+ plot = plot %>%
+ add_markers(
+ data = filter(df_plot, metric == !!metric),
+ x = ~PC1,
+ y = ~PC2,
+ color = ~delta,
+ opacity = 0.6,
+ hoverinfo = 'text',
+ transforms = list(
+ list(
+ type = 'filter',
+ target = ~metric,
+ operation = '=',
+ value = focal_metrics[1]
+ )
+ )
+ )
+}
+
+bslib::card(plot, full_screen = T)
+```