Last updated: 2024-02-13

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Knit directory: LocksofLineage/

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Logistic Regression Model Creation

prerequisite_mapping <- list(
  ConM = "Natal_Coat",
  ConB = "Natal_Coat",
  ConD = "Natal_Coat",
  incon = "Natal_Coat",
  SD_Location_Head = "Sexual_Dimorphism",
  Direction_Male = "Sexual_Dimorphism",
  Darker_Male = "Sexual_Dichromatism"
)
# Load the custom function
source("~/GitHub/LocksofLineage/analysis/binarized_phylogenetic_logistic_regression_function.R")

# Load your dataset
data_file_path <- "~/GitHub/LocksofLineage/data/data_binarized_Feb13.csv"
primate_data <- read_csv(data_file_path)
tree_file_path <- "~/GitHub/LocksofLineage/data/Phylo_Project_Data/MamPhy_BDvr_Completed_v2_tree0000.tre"

# List all variable names (excluding species name if it's there)
variable_names <- colnames(primate_data)[!colnames(primate_data) %in% c("family", "Genus", "species")]

# Initialize a list (or another structure) to store summaries or results
model_results_binarized_prereq <- list()

# Loop over all variable combinations
for (outcome_var in variable_names) {
    for (predictor_var in variable_names) {
        if (outcome_var != predictor_var) {
            #Check if the current predictor_var has a prerequisite
            prerequisite_trait <- ifelse(predictor_var %in% names(prerequisite_mapping), prerequisite_mapping[[predictor_var]], "")
            
            # Run the phylogenetic logistic regression analysis
            model_summary_binarized_prereq <- run_binarized_phylogenetic_logistic_regression(outcome_var, predictor_var, tree_file_path, data_file_path)
            
            if(!exists("model_results_binarized_prereq")) {
  model_results_binarized_prereq <- list()
}
            
            # Store the summary with a meaningful identifier
            model_id <- paste(outcome_var, "vs", predictor_var, sep = "_")
            model_results_binarized_prereq[[model_id]] <- model_summary_binarized_prereq

            # Optionally, print or inspect the summary
             print(model_summary_binarized_prereq)
        }
    }
}

Analyzing the Models

# Initialize the association matrix with the correct dimensions and names
n_variables <- length(variable_names)
association_matrix <- matrix(NA, nrow = n_variables, ncol = n_variables, dimnames = list(variable_names, variable_names))

# Loop over model_results to extract and store coefficients
for (model_id in names(model_results_binarized_prereq)) {
  model_summary_binarized_prereq <- model_results_binarized_prereq[[model_id]]
  
  if (!is.null(model_summary_binarized_prereq) && "summary.phyloglm" %in% class(model_summary_binarized_prereq)) {
    # Extract coefficients matrix
    coefficients_matrix <- model_summary_binarized_prereq$coefficients
    
    # Assuming you're interested in the first predictor's coefficient
    # and that predictor variable names directly match those in variable_names
    predictor_name <- gsub(".*vs_", "", model_id)  # Extract predictor variable name from model_id
    if (predictor_name %in% rownames(coefficients_matrix)) {
      coefficient <- coefficients_matrix[predictor_name, "Estimate"]
      
      # Determine indices for the association matrix based on variable names
      outcome_var <- gsub("_vs.*", "", model_id)  # Extract outcome variable name from model_id
      i <- which(variable_names == outcome_var)
      j <- which(variable_names == predictor_name)
      
      # Populate the association matrix
      if (length(i) == 1 && length(j) == 1) {  # Ensure valid indices
        association_matrix[i, j] <- coefficient
      }
    }
  }
}

# Now, the association matrix should be populated with the coefficients
# Assuming `model_results` is a list where each element is a model summary including AIC

# Extract AIC values and model identifiers
aic_values <- sapply(model_results_binarized_prereq, function(summary) summary$aic)  
# Adjust extraction based on your summary structure
model_ids <- names(model_results_binarized_prereq)

# Combine into a data frame for easy sorting and viewing
aic_df <- data.frame(model_id = model_ids, AIC = aic_values)

# Sort by AIC values
aic_sorted <- aic_df[order(aic_df$AIC), ]

# View sorted models by AIC
#print(aic_sorted)

# Identify best models (e.g., top 5 models with lowest AIC)
best_models <- head(aic_sorted, 5)
print(best_models)
                                               model_id      AIC
ConM_vs_SD_Location_Head       ConM_vs_SD_Location_Head 20.32009
ConM_vs_Direction_Male           ConM_vs_Direction_Male 22.68275
ConM_vs_Sexual_dimorphism     ConM_vs_Sexual_dimorphism 24.54911
ConM_vs_Sexual_dichromatism ConM_vs_Sexual_dichromatism 25.87730
ConM_vs_Darker_Male                 ConM_vs_Darker_Male 27.92916
# Create the plot for AIC of models with prerequisites
ggplot(aic_sorted, aes(x = reorder(model_id, AIC), y = AIC)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  coord_flip() +  # Flip coordinates to make the plot horizontal; easier to read model names
  labs(x = "Model", y = "AIC Value", title = "Comparison of Model AIC Values with Prerequisites") +
  geom_text(aes(label = sprintf("%.2f", AIC), hjust = -0.1))  # Add AIC values as text labels

#Basic r plotting
heatmap(association_matrix, Rowv = NA, Colv = NA, col = heat.colors(10), scale = "none")


sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.0    
 [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.1.8   
 [9] ggplot2_3.4.4   tidyverse_2.0.0 phylolm_2.6.2   ape_5.7        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.11         lattice_0.20-45     listenv_0.9.0      
 [4] rprojroot_2.0.4     digest_0.6.30       utf8_1.2.2         
 [7] parallelly_1.36.0   R6_2.5.1            evaluate_0.23      
[10] highr_0.10          pillar_1.8.1        rlang_1.1.2        
[13] rstudioapi_0.14     jquerylib_0.1.4     rmarkdown_2.20     
[16] labeling_0.4.2      bit_4.0.5           munsell_0.5.0      
[19] compiler_4.2.1      httpuv_1.6.11       xfun_0.41          
[22] pkgconfig_2.0.3     globals_0.16.2      htmltools_0.5.4    
[25] tidyselect_1.2.0    workflowr_1.7.1     codetools_0.2-18   
[28] fansi_1.0.3         future_1.33.0       crayon_1.5.2       
[31] tzdb_0.3.0          withr_2.5.0         later_1.3.1        
[34] grid_4.2.1          nlme_3.1-160        jsonlite_1.8.8     
[37] gtable_0.3.1        lifecycle_1.0.3     git2r_0.32.0       
[40] magrittr_2.0.3      scales_1.2.1        vroom_1.6.1        
[43] future.apply_1.11.0 cli_3.6.2           stringi_1.7.8      
[46] cachem_1.0.6        farver_2.1.1        fs_1.6.1           
[49] promises_1.2.1      bslib_0.4.2         ellipsis_0.3.2     
[52] vctrs_0.5.2         generics_0.1.3      tools_4.2.1        
[55] bit64_4.0.5         glue_1.6.2          hms_1.1.2          
[58] parallel_4.2.1      fastmap_1.1.0       yaml_2.3.7         
[61] timechange_0.2.0    colorspace_2.0-3    knitr_1.42         
[64] sass_0.4.5