f - run_logistic_regression.R

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#########################################  Adjusted Linear Regression  ########################################
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

# Purpose: This function runs the adjusted linear regression for all chemicals and all immune measures
#          
# Inputs: long_nhanes_subset - long dataframe containing complete demographic and cognitive data for each
#                              participant
#         conversion - dataframe of chemical names, codenames, and families
#
# Outputs: model_stats_adjusted - dataframe of linear regression outputs

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#

run_logistic_regression <- function(long_nhanes_subset,
                                  conversion,
                                  weights_dataset,
                                  chem_master,
                                  nhanes_subset)
{
  library(tidyverse)
  library(beepr)
  
  #TEMPORARY
  # long_nhanes_subset <- long_nhanes_subset_dataset
  # conversion <- use_these_chems
  
  long_nhanes_subset$chemical_codename <- as.character(long_nhanes_subset$chemical_codename)
  
  long_nhanes_subset <- long_nhanes_subset %>%
   mutate(chem_copy = chemical_codename) 
  
  

  #run linear regressions
  # print("run reg")
  df_regressions_i <- long_nhanes_subset %>%
    filter(chemical_codename %in% c("LBXCOT",
                                    "LBXBCD",
                                    "LBXSCU")) %>%
    filter(celltype_codename %in% c("LBXWBCSI")) %>%
    group_by(celltype_codename, chemical_codename) %>%
    do(run_if_else_glm_logistic_wt(.,
                                   weights_dataset,
                                   chem_master, 
                                   nhanes_subset)) %>% #the dot is each group by chunk - pulling from chemical_immune_chunk
    ungroup(.)
  # View(df_regressions_i)
  
  #############################################################################################################
  ############################################## RENAME CHEMICALS #############################################
  #############################################################################################################
  
  conversion_subset <- conversion %>%
    dplyr::select(chemical_codename_use,
                  chem_family,
                  chemical_name) %>%
    distinct(chemical_codename_use, .keep_all = TRUE) %>%
    rename(chemical_codename = chemical_codename_use)

  #merge in the chemical_names
  df_regressions_names <- left_join(df_regressions_i, conversion_subset, by = "chemical_codename")

  df_regressions_names <- df_regressions_names %>%
    mutate(immune_measure = case_when(
                                      celltype_codename == "LBDLYMNO" ~ "Lymphocyte (1000 cells/uL)", 
                                      celltype_codename == "LBDMONO" ~ "Monocyte (1000 cells/uL)",  
                                      celltype_codename == "LBDNENO" ~ "Neutrophils (1000 cells/uL)",  
                                      celltype_codename == "LBDEONO" ~ "Eosinophils (1000 cells/uL)",  
                                      celltype_codename == "LBDBANO" ~ "Basophils (1000 cells/uL)",
                                      celltype_codename == "LBXWBCSI" ~ "WBC (1000 cells/uL)",
                                      celltype_codename == "LBXRBCSI" ~ "RBC (million cells/uL)",
                                      celltype_codename == "LBXMCVSI" ~ "Mean Corpuscular Volume (fL)"))
  # View(df_regressions_names)
  
  #############################################################################################################
  ###################################### ADD CONFIDENCE INTERVALS AND FDR #####################################
  #############################################################################################################

  #calculate the unscaled confidence intervals
  z_score <- 1.96
  model_stats_CI_unscaled <- df_regressions_names %>%
    mutate(lower.CI = estimate - (z_score*std.error),
           upper.CI = estimate + (z_score*std.error)) 
  
  model_stats_CI_unscaled_chem_other <- model_stats_CI_unscaled %>%
    filter(term != "chem_log_measurement") 
    
  
  model_stats_CI_unscaled_chem <- model_stats_CI_unscaled %>%
    filter(term == "chem_log_measurement") %>%
    group_by(celltype_codename) %>%
    mutate(FDR = p.adjust(p.value, method = "BY")) %>%
    ungroup()

  model_stats_CI_unscaled_updated <- full_join(model_stats_CI_unscaled_chem
                                       , model_stats_CI_unscaled_chem_other
                                       , by = NULL) %>%
    arrange(celltype_codename
            , chemical_codename)
  # View(model_stats_CI_unscaled_updated)

  model_stats_adjusted <- model_stats_CI_unscaled_updated %>%
    dplyr::select(chemical_name,
                  chemical_codename,
                  chem_family,
                  immune_measure,
                  celltype_codename,
                  term,
                  estimate,
                  std.error,
                  lower.CI,
                  upper.CI,
                  statistic,
                  p.value,
                  FDR,
                  nobs)
  # View(model_stats_adjusted)

  beep()
  
  return(model_stats_adjusted)
}