Defines functions dewlap_dfa_global

Documented in dewlap_dfa_global

#' Global Discriminant Function Analysis (DFA) of dewlap color
#' This function performs a single, global DFA to test clustering by habitat across all islands. The algorithm fits functions that best discriminate among habitats based on dewlap color data. The type of DFA can be linear (LDA) or quadratic (QDA). Each data point is then classified i.e. its original habitat is predicted based on the discriminant functions. Predictions can be jacknifed (leave-one-out). The significance of the classification is assessed in two ways. First, a MANOVA tests for differences in dependent variables across predicted groups. Second, a binomial test assesses the departure of the observed number of successful predictions from the null expectation.
#' @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 type A character. Type of discriminant analysis. \code{"linear"} (default) or \code{"quadratic"}.
#' @param plotit Logical. Whether to plot the loadings of the data points on the discriminant functions (applicable only if \code{type == "linear"}).
#' @param CV Logical. Whether to jacknife the predictions (cross-validation).
#' @return A vector with elements:
#' \itemize{
#' \item \code{observed}: the observed number of succesful assignments
#' \item \code{expected}: the number of succesful assignments expected by change
#' \item \code{n}: the number of assignments
#' \item \code{p.binom}: the P-value of the binomial test
#' \item \code{df}: the degrees of freedom of the MANOVA
#' \item \code{wilks}: Wilk's lambda
#' \item \code{approx.F}: the approximate F-value calculated from Wilk's lambda
#' \item \code{num.df}: numerator degrees of freedom for F-test
#' \item \code{denom.df}: denumerator degrees of freedom for F-test
#' \item \code{p.manova}: P-value of the MANOVA
#' }
#' @note Quadratic discriminant analysis doesn't require homogeneous covariance matrices among groups, unlike linear (Robert I. Kabacoff, Quick-R, https://www.statmethods.net/advstats/discriminant.html).
#' @author Raphael Scherrer
#' @export

# Function to perform a global discriminant function analysis across all islands
dewlap_dfa_global <- function(specdata, vars, type = "linear", plotit = T, CV = F) {


  # Extract dependent variables
  Y <- as.matrix(specdata[, vars])

  # Perform discriminant analysis (linear or quadratic)
  # Note: quadratic analysis doesn't require homogeneous covariance matrices among groups unlike linear (Robert I. Kabacoff, Quick-R, https://www.statmethods.net/advstats/discriminant.html)
  if(type == "linear") {

    dfa.fit <- MASS::lda(habitat ~ Y, data = specdata, CV = CV)

  } else if(type == "quadratic") {

    dfa.fit <- MASS::qda(habitat ~ Y, data = specdata, CV = CV)


  # Plot the discriminant functions (only if LDA)
  if(type == "linear" & plotit) plot(dfa.fit, main = curr.island)

  # Predictions
  pred <- predict(dfa.fit, as.data.frame(Y))

  # Test for differences among predicted groups
  MANOVA <- manova(Y ~ pred$class)
  MANOVA.res <- summary(MANOVA, test = "Wilks")
  print("MANOVA table")

  # Classification matrix (cross-tabulation)
  classMat <- table(specdata$habitat, pred$class)
  print("Classification matrix")

  # How many successful assignments?
  nSuccess <- sum(diag(classMat))

  # How many assignments in total?
  nTotal <- sum(classMat)

  # Probability of a correct reassignment by chance
  prob <- 1 / ncol(classMat)

  # Is the number of success higher than expected by chance?
  binom.res <- binom.test(nSuccess, nTotal, prob, alternative = "greater")

  # Output
  out <- with(binom.res, c(null.value, estimate, parameter, p.value))

  # Add MANOVA output
  out <- c(out, c(MANOVA.res$stats[1,]))

  names(out) <- c("expected", "observed", "n", "p.binom", "df", "wilks", "approx.F", "num.df", "denom.df", "p.manova")


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