Defines functions dewlap_neural

Documented in dewlap_neural

#' Habitat classification with neural networks
#' This function trains neural networks to recognize differences between habitats. Each neural network is trained on a random sample of half of the data, and tested against the other half. The success of the classification is compared to a null expectation generated from a permuted dataset where no differences exist between habitats. The 5\% best performing machines are studied more in depth to identify what were the most important variables in discriminating between habitats.
#' @param specdata A data frame containing at least columns for the dependent variables, as well as a column "habitat".
#' @param vars A character or integer vector. The names, or indices, of the dependent variables in \code{specdata}.
#' @param nRepet The number of neural networks to train (same number for empirical and permuted datasets).
#' @param seed Seed for random number gnerators
#' @param plotit Whether to plot the success or not
#' @details A large number of machines are trained on the data. An equal number of machines are trained on randomized data, to produce a null distribution of the success rate. Each machine is trained on a training set. The training set is a random sample of half of the data points, which is then downsampled so that all groups are equally represented (balanced design). The machine is a classifier Support Vector Machine using a Gaussian kernel. After training, the machine is tested against the testing set, which is the other half of the data. We record the number of successful reassignments. This number is tested against chance by applying a binomial test. We record confusion matrices that show which groups were mistaken for which ones. Finally, we isolate the 5\% best machines (those with the highest scores) and display the weights of each of the input variables for these machines, thus giving an idea of what variables in the data contribute most to the differences between the groups.
#' @return A list of outputs: (1) a table containing the success rate, binomial p-value, number of points in the training set, number of support vectors, Gaussian kernel sigma hyperparameter and cost parameter of each machine; (2) an importance table summarizing the weight each input variable among the 5\% best machines, (3) a list of the confusion matrices of all the 5\% best machines and (4) the 95th percentile success rate among the machines trained on non-randomized data (i.e. the threshold that defines the 5\% best machines).
#' @author Raphael Scherrer
#' @export

# Function to train neural networks to detect differences between habitats
dewlap_neural <- function(specdata, vars, nRepet = 1000, seed, plotit = F) {

  # Load dependencies

  if(!missing("seed")) set.seed(seed)

  nhabitats <- nlevels(specdata$habitat)

  # Initialization
  res <- matrix(0, nrow = 2 * nRepet, ncol = 6)
  labels <- rep(c("Randomized", "Empirical"), each = nRepet)
  confumats <- vector("list", nRepet)
  machines <- vector("list", nRepet)
  trainings <- vector("list", nRepet)

  # Loop through machines
  for(i in 1:(2 * nRepet)) {

    # Permute the data if within the first half of the loop
    if(i <= nRepet) {

      permid <- sample(nrow(specdata), replace = F)
      permhabitats <- specdata$habitat[permid]
      currdata <- data.frame(specdata[,vars], habitat = permhabitats)

    } else {

      currdata <- data.frame(specdata[,vars], habitat = specdata$habitat)


    message(paste0("Training machine ", i, "/", 2 * nRepet, "..."))

    # Sample training and testing sets
    istraining <- caret::createDataPartition(currdata$habitat, p = 0.5, list = F)
    trainingset <- currdata[istraining,]
    testingset <- currdata[-istraining,]

    # Downsample the training set to ensure balance
    trainingset <- DescTools::Strata(trainingset, stratanames = "habitat", size = rep(min(table(trainingset$habitat)), length(unique(trainingset$habitat))))
    drop <- c("stratum","size","id")
    trainingset <- trainingset[,-which(colnames(trainingset) %in% drop)]

    # Record size of the training set
    trainsize <- nrow(trainingset)

    # Train the Vector Support Machine
    svm.model <- fit(habitat ~ ., data = trainingset, model = "svm", kernel = "rbfdot", task = "class")

    # How many support vectors?
    nSupportVectors <- svm.model@object@nSV

    # Parameters of the machine
    sigma <- svm.model@mpar$kpar$sigma
    cost <- svm.model@mpar$C

    # Test the machine
    svm.pred <- predict(svm.model, testingset[, -which(colnames(testingset) == "habitat")])

    # Confusion matrix
    confusion <- table(svm.pred, true = testingset$habitat)

    # Assess performance
    nSuccess <- sum(diag(confusion))
    nTotal <- sum(confusion)
    propSuccess <- nSuccess / nTotal

    # Test the performance against pure chance
    p.binom <- binom.test(nSuccess, nTotal, p = 1/nhabitats)$p.value

    # Write output
    res[i,1] <- propSuccess
    res[i,2] <- p.binom
    res[i,3] <- trainsize
    res[i,4] <- nSupportVectors
    res[i,5] <- sigma
    res[i,6] <- cost

    if(i > nRepet) {
      confumats[[i - nRepet]] <- confusion
      machines[[i - nRepet]] <- svm.model
      trainings[[i - nRepet]] <- trainingset


  res <- data.frame(labels, res)
  colnames(res) <- c("label", "propSuccess", "pBinom", "trainingSize", "nVectors", "sigma", "cost")

  message("Analyzing the best machines...")

  # Identify the best machines
  quant95 <- quantile(res$propSuccess[res$label == "Empirical"], probs = 0.95)
  bestid <-res$propSuccess[res$label == "Empirical"] >= quant95

  bestmachines <- machines[bestid]
  bestconfumats <- confumats[bestid]
  besttrainings <- trainings[bestid]

  # Get Feature Importance for top 5% machines
  bestFeatures <- mapply(Importance, bestmachines, besttrainings, MoreArgs = list(method="sensv"), SIMPLIFY = FALSE)
  importanceTable <- sapply(bestFeatures,"[[","imp")
  if(ncol(cbind(importanceTable)) > 1) importanceTable <- rowSums(importanceTable) else importanceTable <- c(importanceTable)
  names(importanceTable) <- colnames(trainings[[1]])
  importanceTable <- importanceTable[-1] # first value is habitat

  names(importanceTable) <- gsub("meanrefl", "Mean\nreflectance", names(importanceTable))
  names(importanceTable) <- gsub("cuton", "Cut-on\nwavelength", names(importanceTable))

  out <- list(res = res, imp = importanceTable, conf = bestconfumats, quant95 = quant95)


  if(plotit) plot_neural_success(out$res)


rscherrer/sagreicolor documentation built on March 24, 2019, 8:34 p.m.