Update test_all_functions.R

R/analyze_linear_selection.R

@@ -18,6 +18,7 @@ analyze_linear_selection <- function(data, fitness_col, trait_cols, fitness_type
 
   if (fitness_type == "continuous") {
     resp <- if ("relative_fitness" %in% names(data)) {
+      "relative_fitness"
     } else {
       fitness_col
     }

R/selection_coefficients.R

@@ -11,14 +11,20 @@ selection_coefficients <- function(data, fitness_col, trait_cols,
 
   fitness_type <- match.arg(fitness_type)
 
+  # 3 decide which fitness column to model 
+  # binary fitness must always be on absolute values 0 or 1 
+  # continuous fitness may use relative fitness 
+  rel_col <- paste0(fitness_col, "_relative")
+
   # 1 prepare data
   df <- prepare_selection_data(
     data          = data,
     fitness_col   = fitness_col,
     trait_cols    = trait_cols,
     standardize   = standardize,
     add_relative  = TRUE,   
-    na_action     = "warn"
+    na_action     = "warn",
+    name_relative = rel_col
   )
 
   # 2 detect family if auto
@@ -28,11 +34,6 @@ selection_coefficients <- function(data, fitness_col, trait_cols,
   }
   family_obj <- if (fitness_type == "binary") binomial("logit") else gaussian()
 
-  # 3 decide which fitness column to model 
-  # binary fitness must always be on absolute values 0 or 1 
-  # continuous fitness may use relative fitness 
-  rel_col <- paste0(fitness_col, "relative")
-
   model_fitness_col <-
     if (fitness_type == "binary") {
       if (use_relative_for_fit) {
@@ -84,3 +85,4 @@ selection_coefficients <- function(data, fitness_col, trait_cols,
 
   return(all_coefs)
 }
+

R/tests scripts/test_all_functions.R

@@ -1,3 +1,11 @@
+# ---- Packages ----
+if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
+library(ggplot2)
+# Ensure other likely dependencies are loaded if needed by the sourced functions
+for (pkg in c("mgcv", "fields", "dplyr", "tidyr")) {
+  if (requireNamespace(pkg, quietly = TRUE)) library(pkg, character.only = TRUE)
+}
+
 cat("1. Loading all function files...\n")
 function_files <- c(
   "prepare_selection_data.R", 
@@ -18,8 +26,11 @@ for (file in function_files) {
   if (file.exists(file)) {
     source(file)
     cat("Loaded:", file, "\n")
+  } else if (file.exists(file.path("..", file))) {
+    source(file.path("..", file))
+    cat("Loaded:", file.path("..", file), "\n")
   } else {
-    cat("ile not found:", file, "\n")
+    cat("File not found:", file, "\n")
   }
 }
 
@@ -124,7 +135,6 @@ tps_result <- correlational_tps(
   data = df_continuous,
   fitness_col = "fitness",
   trait_cols = c("size", "speed"),
-  use_relative = TRUE,
   grid_n = 30
 )
 
@@ -288,7 +298,6 @@ for (traits in trait_combinations) {
     data = df_continuous,
     fitness_col = "fitness",
     trait_cols = traits,
-    use_relative = TRUE,
     grid_n = 30
   )
 
@@ -312,7 +321,6 @@ for (traits in trait_combinations[1:2]) {
     data = df_binary,
     fitness_col = "fitness",
     trait_cols = traits,
-    use_relative = FALSE,
     grid_n = 25
   )
 
@@ -361,10 +369,10 @@ validate_plot <- function(plot, plot_name) {
   )
 
   if (all(checks)) {
-    cat(lot_name, "PASS\n")
+    cat(plot_name, "PASS\n")
     return(TRUE)
   } else {
-    cat(lot_name, "FAIL - Issues:", paste(names(checks)[!checks], collapse = ", "), "\n")
+    cat(plot_name, "FAIL - Issues:", paste(names(checks)[!checks], collapse = ", "), "\n")
     return(FALSE)
   }
 }
@@ -395,6 +403,10 @@ cat("Correlation plots:", length(correlation_plots), "\n")
 
 cat("\n11. Updated final summary...\n")
 
+# Calculate passed and total based on the 'results' list from Section 5
+total <- length(results)
+passed <- sum(grepl("^pass", unlist(results)))
+
 cat("Functions tested:", length(function_files), "\n")
 cat("Tests passed:", passed, "/", total, "\n")
 cat("Plots created:", total_plots, "\n")