R/classifSample.lda.R

In MorphoTools2: Multivariate Morphometric Analysis

#' Classificatory discriminant analysis
#' @export
classifSample.lda <- function(sampleData, trainingData) {

  .checkClass(sampleData, "morphodata")
  .checkClass(trainingData, "morphodata")


  # matica musi byt plna
  if (any(is.na(sampleData$data))) stop("NA values in 'sampleData'.", call. = FALSE)
  if (any(is.na(trainingData$data))) stop("NA values in 'trainingData'.", call. = FALSE)

  if (! all(colnames(sampleData$data) == colnames(trainingData$data)))
    stop("Characters of 'sampleData' and 'trainingData' are not the same.", call. = FALSE)


  ntax<-length(levels(trainingData$Taxon))
  char<-colnames(trainingData$data)


  res = .newClassifdata()

  #lda.train = MASS::lda(stats::as.formula(paste("trainingData$Taxon ~ ", paste(char, collapse="+"))), data=trainingData$data, prior = rep(1/ntax,ntax))
  lda.train = MASS::lda(trainingData$Taxon ~ . , data=trainingData$data, prior = rep(1/ntax,ntax))
  lda.samp = stats::predict(lda.train,sampleData$data)


  res$ID = as.character(sampleData$ID)
  res$Population = sampleData$Population
  res$Taxon = sampleData$Taxon

  #res$classif = data.frame("classification" = as.character(lda.samp$class))
  res$classif = lda.samp$class
  #rownames(res$classif) = res$ID

  res$prob = round(lda.samp$posterior, digits = 4)
  res$prob = as.data.frame(res$prob)


  res$correct = NULL
  attr(res, "method") <- "lda"

  return(res)
}