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Bootstrap.R

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+# Set the library path
+.libPaths("M:/My Documents/R/win-library/3.6")
+
+# Choose the working directory
+Work_Dir <- choose.dir()
+
+# Get the subdirectories in the working directory
+dirs <- list.dirs(path = Work_Dir, full.names = FALSE, recursive = FALSE)
+
+# Set the output CSV file name and path
+CSV_file <- file.path(Work_Dir, "Final_Results.csv")
+
+# Set the data folder path
+Datafolder <- "W:/WFSR/Projects"
+
+# Set the number of repetitions and other parameters
+Reps <- 10000
+NPhotos <- 21
+PhotoType <- c(7, 7, 7)
+Target <- "WT" #"WT" or "Cnt" to sample weights or counts, respectively
+
+# Loop through each subdirectory
+for (dir in dirs) {
+
+  # Write the subdirectory name to the CSV file
+  write.table(dir, file = CSV_file, append = TRUE, sep = ",", row.names = "File:", col.names = FALSE)
+
+  # Set the path to the data folder in the subdirectory
+  Datafolder <- file.path(Work_Dir, dir)
+  setwd(Datafolder)
+
+  # Get the result files in the data folder
+  resfiles <- list.files(pattern = "Results.csv")
+
+  # Initialize the result vectors
+  R.Wt_indiv <- list()
+  R.Wt <- vector()
+  R.Count <- vector()
+  R.Type <- vector()
+
+  # Loop through each result file
+  for (f in 1:length(resfiles)) {
+    temp <- data.frame(read.csv(resfiles[f]))
+    temp <- temp[-1, ]
+    R.Wt_indiv[[f]] <- temp$Weight
+    R.Wt[f] <- sum(temp$Weight)
+    R.Count[f] <- length(which(temp$Area > 0))
+
+    temp.type <- unlist(strsplit(resfiles[f], "_"))
+    temp.type <- unlist(strsplit(temp.type[length(temp.type) - 2], split = ""))
+    R.Type[f] <- temp.type[temp.type %in% c("C", "R", "M")]
+  }
+
+  # Initialize the bootstrap vector
+  BS <- vector()
+
+  # Set the target variable for bootstrap resampling
+  if (Target == "WT") {
+    R.target <- R.Wt
+  } else if (Target == "Cnt") {
+    R.target <- R.Count
+  }
+
+  # Generate bootstrap samples and calculate means
+  for (R in 1:Reps) {
+    if (length(PhotoType) != 3) {
+      add.this <- sample(R.target, size = NPhotos, replace = TRUE)
+    }
+    if (length(PhotoType) == 3) {
+      N.per.type <- round(PhotoType / sum(PhotoType) * NPhotos, 0)
+      add.this <- c(sample(R.target[R.Type == "C"], size = N.per.type[1], replace = TRUE),
+                    sample(R.target[R.Type == "M"], size = N.per.type[2], replace = TRUE),
+                    sample(R.target[R.Type == "R"], size = N.per.type[3], replace = TRUE))
+    }
+    BS <- c(BS, mean(add.this))
+  }
+
+  # Calculate the observed mean
+  Observed <- mean(R.target) * (12.5^2 * pi) / ((1.408 * 1.056) - 0.140)
+
+  # Plot the bootstrap distribution with the observed mean as a vertical line
+  plot(density(BS))
+  abline(v = Observed, col = "blue", lwd=2)
+
+  mean.result <- mean(BS)
+  sd.result <- sqrt(var(BS)) 
+
+  variability <- (2 * (sd.result / mean.result) * 100)
+
+  write.table(cbind("Observed" = Observed), file = CSV_file, append = TRUE, sep = ",", row.names = FALSE, col.names = TRUE)
+  write.table(t(quantile(BS, c(.025, .25, .50, .75, .975))), file = CSV_file, append = TRUE, sep = ",", row.names = FALSE, col.names = c("2.5%", "25%", "50%", "75%", "97.5%"))
+  write.table(cbind("Mean" = mean.result), file = CSV_file, append = TRUE, sep = ",", row.names = FALSE, col.names = TRUE)
+  write.table(cbind("Std deviation" = sd.result), file = CSV_file, append = TRUE, sep = ",", row.names = FALSE, col.names = TRUE)
+  write.table(cbind("Variability" = variability), file = CSV_file, append = TRUE, sep = ",", row.names = FALSE, col.names = TRUE)
+  cat("\n", file = CSV_file, append = TRUE)
+}