PBJ0/ECDF.R at master · SFUStatgen/PBJ0 · GitHub

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#
# Load all the necessary functions to calculate the ECDF datasets both for localization and detection
#
source('ECDF_Utillity_Functions.R')


#
# Define the main directory where 500 datasets are located
#
main_path = "D:/Research/Project1/Simulations/msprime/WrightFisherModel/SearchForExampleDataset_July28/"


#####  Localization ####
#
# Define a matrix to record the average distance of the peak from the risk region for each method.
# Rows in this matrix represents each simulated dataset and columns represents each method.
#
# We defined the order of columns as follows:
# "FET", "SKATO", "dCorN", "Mantel"
#
Localization_results = matrix(NA, nrow = 500, ncol = 4)
colnames(Localization_results) = c("FET", "SKATO", "dCorN", "Mantel")
for(i in 1:500){
  dir          = paste0( main_path, 'Dataset', i, '/')
  Localization_results[i,] = get_localization_results(dir)
  print(dir)
}
save(x = Localization_results, file = 'Localization_results_1_500.RData')


#####  Detection ####
#
# Define a matrix to record the calculated pvalue based on permutation for:
# FET, SKATO, dCorN, Mantel
#
# Rows in this matrix represents each simulated dataset and columns represents each method.
#
# We defined the order of columns as follows:
# "FET", "SKATO", "dCorN", "Mantel"
#

Detection_results           = matrix(NA, nrow = 500, ncol = 4)
colnames(Detection_results) = c("FET", "SKATO", "dCorN", "Mantel")
for(i in 1:500){
  dir                   = paste0( main_path, 'Dataset', i, '/')
  Detection_results[i,] = get_detection_results(dir)
  print(dir)
}
save(x = Detection_results, file = 'Detection_results_1_500.RData')