R/samp_Discrete.R

In paternogbc/sensiPhy: Sensitivity Analysis for Comparative Methods

#' Species Sampling uncertainty - Trait Evolution Discrete Characters
#' 
#' Fits models for trait evolution of discrete (binary) characters, 
#' evaluating sampling uncertainty. 
#'
#' @param data Data vector for a single binary trait, with names matching tips in \code{phy}.
#' @param phy A phylogeny (class 'phylo') matching \code{data}.
#' @param n.sim The number of times species are randomly deleted for each \code{break}.
#' @param breaks A vector containing the percentages of species to remove.
#' @param model The Mkn model to use (see Details). 
#' @param transform The evolutionary model to transform the tree (see Details). Default is \code{none}.
#' @param n.cores number of cores to use. If 'NULL', number of cores is detected.
#' @param bounds settings to constrain parameter estimates. See \code{\link[geiger]{fitDiscrete}}
#' @param track Print a report tracking function progress (default = TRUE)
#' @param ... Further arguments to be passed to \code{\link[geiger]{fitDiscrete}}
#' @details
#' This function randomly removes a given percentage of species (controlled by \code{breaks}),  
#' fits different models of discrete character evolution using \code{\link[geiger]{fitDiscrete}}, 
#' repeats this this many times (controlled by \code{n.sim}), stores the results and calculates 
#' the effects on model parameters Currently, only binary discrete traits are supported.
#' 
#' Different character model from \code{fitDiscrete} can be used, including \code{ER} (equal-rates), 
#' \code{SYM} (symmetric), \code{ARD} (all-rates-different) and \code{meristic} (stepwise fashion). 
#'
#' Transformations to the phylogenetic tree from \code{fitDiscrete} can be used, i.e. \code{none},
#' \code{EB}, \code{lambda}, \code{kappa} and\code{delta}.
#' 
#' See \code{\link[geiger]{fitDiscrete}} for more details on character models and tree transformations. 
#' 
#' Output can be visualised using \code{sensi_plot}.
#'
#' @return The function \code{tree_discrete} returns a list with the following
#' components:
#' @return \code{call}: The function call
#' @return \code{data}: The original full data vector
#' @return \code{optpar}: Transformation parameter used (e.g. \code{lambda}, \code{kappa} etc.)
#' @return \code{full.model.estimates}: Parameter estimates (transition rates q12 and q21), 
#' AICc and the optimised value of the phylogenetic transformation parameter (e.g. \code{lambda}) 
#' for the full model without deleted species.
#' @return \code{break.summary.tab}: Summary per \code{break} of the mean and median effects 
#' of species removal on percentage and absolute change in parameters q12 and q21. 
#' @return \code{sensi.estimates}: Parameter estimates (transition rates q12 and q21),(percentual) difference 
#' in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21),  
#' AICc and the optimised value of the phylogenetic transformation parameter (e.g. \code{lambda}) 
#' for each analysis with a species deleted.
#' @return \code{optpar}: Transformation parameter used (e.g. \code{lambda}, \code{kappa} etc.)
#' @author Gijsbert Werner & Gustavo Paterno
#' @seealso \code{\link[geiger]{fitDiscrete}}
#' @references 
#' 
#' Paterno, G. B., Penone, C. Werner, G. D. A. 
#' \href{http://doi.wiley.com/10.1111/2041-210X.12990}{sensiPhy: 
#' An r-package for sensitivity analysis in phylogenetic 
#' comparative methods.} Methods in Ecology and Evolution 
#' 2018, 9(6):1461-1467
#'
#' Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford. 
#' 
#' Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008.
#' GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
#' 
#' Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). 
#' A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
#' @examples 
#' \dontrun{
#' #Load data:
#' data("primates")
#' #Create a binary trait factor 
#' adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small")
#' adultMass_binary<-as.factor(as.factor(adultMass_binary))
#' names(adultMass_binary)<-rownames(primates$data)
#' #Model trait evolution accounting for sampling size 
#' samp_binary<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]],
#' n.sim=25,breaks=seq(.1,.3,.1),model = "SYM",transform = "none",n.cores = 2,track = TRUE)
#' #Print summary statistics
#' summary(samp_binary)
#' sensi_plot(samp_binary)
#' sensi_plot(samp_binary,graphs=1)
#' sensi_plot(samp_binary,graphs=2)
#' #Use a different evolutionary model or transformation 
#' samp_binary2<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]],
#' n.sim=25,breaks=seq(.1,.3,.1),model = "ARD",transform = "lambda",n.cores = 2,track = TRUE)
#' summary(samp_binary2)
#' sensi_plot(samp_binary2)
#' sensi_plot(samp_binary2,graphs=1)
#' sensi_plot(samp_binary2,graphs=3)
#' }
#' @export

samp_discrete <- function(data,
                          phy,
                          n.sim = 30,
                          breaks = seq(.1, .5, .1),
                          model,
                          transform = "none",
                          bounds = list(),
                          n.cores = NULL,
                          track = TRUE,
                          ...) {
  #Error check
  if (is.null(model))
    stop("model must be specified (e.g. 'ARD' or 'SYM'")
  if (!inherits(data, "factor"))
    stop("data must supplied as a factor with species as names. Consider as.factor()")
  if (length(levels(data)) > 2)
    stop("discrete data can have maximal two levels")
  if (!inherits(phy, "phylo"))
    stop("phy must be class 'phylo'")
  if (transform == "white")
    stop("the white-noise (non-phylogenetic) model is not allowed")
  if (length(breaks) < 2)
    stop("Please include more than one break, e.g. breaks=c(.3,.5)")
  else
    
    #Matching tree and phylogeny
    full.data <- data
  phy <- phy
  
  #Calculates the full model, extracts model parameters
  N                   <- length(full.data)
  mod.0               <-
    geiger::fitDiscrete(
      phy = phy,
      dat = full.data,
      model = model,
      transform = transform,
      bounds = bounds,
      ncores = n.cores,
      ...
    )
  q12.0               <- mod.0$opt$q12
  q21.0               <- mod.0$opt$q21
  aicc.0              <- mod.0$opt$aicc
  if (transform == "none") {
    optpar.0 <- NA
  }
  if (transform == "EB") {
    optpar.0               <- mod.0$opt$a
  }
  if (transform == "lambda") {
    optpar.0               <- mod.0$opt$lambda
  }
  if (transform == "kappa") {
    optpar.0               <- mod.0$opt$kappa
  }
  if (transform == "delta") {
    optpar.0               <- mod.0$opt$delta
  }
  
  
  #Creates empty data frame to store model outputs
  sensi.estimates <-
    data.frame(
      "n.remov" = numeric(),
      "n.percent" = numeric(),
      "q12" = numeric(),
      "DIFq12" = numeric(),
      "q12.perc" = numeric(),
      "q21" = numeric(),
      "DIFq21" = numeric(),
      "q21.perc" = numeric(),
      "aicc" = numeric(),
      "optpar" = numeric()
    )
  
  
  #Loops over breaks, remove percentage of species determined by 'breaks
  #and repeat determined by 'n.sim'.
  counter <- 1
  limit <- sort(round((breaks) * length(full.data), digits = 0))
  NL <- length(breaks) * n.sim
  if (track == TRUE)
    pb <- utils::txtProgressBar(min = 0, max = NL, style = 3)
  for (i in limit) {
    for (j in 1:n.sim) {
      #Prep simulation data
      exclude <- sample(1:N, i)
      crop.data <- full.data[-exclude]
      crop.phy <-
        ape::drop.tip(phy, setdiff(phy$tip.label, names(crop.data)))
      #Run the model
      mod = try(geiger::fitDiscrete(
        phy = crop.phy,
        dat = crop.data,
        model = model,
        transform = transform,
        bounds = bounds,
        ncores = n.cores,
        ...
      ),
      TRUE)
      if (isTRUE(class(mod) == "try-error")) {
        next
      }
      else {
        q12               <- mod$opt$q12
        q21               <- mod$opt$q21
        DIFq12            <- q12 - q12.0
        DIFq21            <- q21 - q21.0
        q12.perc          <-
          round((abs(DIFq12 / q12.0)) * 100,
                digits = 1)
        q21.perc          <-
          round((abs(DIFq21 / q21.0)) * 100,
                digits = 1)
        aicc              <- mod$opt$aicc
        if (transform == "none") {
          optpar <- NA
        }
        if (transform == "EB") {
          optpar               <- mod$opt$a
        }
        if (transform == "lambda") {
          optpar               <- mod$opt$lambda
        }
        if (transform == "kappa") {
          optpar               <- mod$opt$kappa
        }
        if (transform == "delta") {
          optpar               <- mod$opt$delta
        }
        
        n.remov <- i
        n.percent <- round((n.remov / N) * 100, digits = 0)
        #rep <- j
        
        if (track == TRUE)
          (utils::setTxtProgressBar(pb, counter))
        # Stores values for each simulation
        # Stores values for each simulation
        # Store reduced model parameters:
        estim.simu <-
          data.frame(
            n.remov,
            n.percent,
            q12,
            DIFq12,
            q12.perc,
            q21,
            DIFq21,
            q21.perc,
            aicc,
            optpar,
            stringsAsFactors = F
          )
        sensi.estimates[counter,]  <- estim.simu
        counter <- counter + 1
      }
    }
  }
  if (track == TRUE)
    on.exit(close(pb))
  
  #Calculates Standardized DFbeta and DIFq12
  sDIFq12 <- sensi.estimates$DIFq12 /
    stats::sd(sensi.estimates$DIFq12)
  sDIFq21     <- sensi.estimates$DIFq21 /
    stats::sd(sensi.estimates$DIFq21)
  
  sensi.estimates$sDIFq21     <- sDIFq21
  sensi.estimates$sDIFq12     <- sDIFq12
  
  #Calculates stats
  res                 <- sensi.estimates
  n.sim               <- table(res$n.remov)
  breaks              <- unique(res$n.percent)
  mean.sDIFq12   <- with(res, tapply(sDIFq12, n.remov, mean))
  mean.sDIFq21  <- with(res, tapply(sDIFq21, n.remov, mean))
  mean.perc.q21 <- with(res, tapply(q21.perc, n.remov, mean))
  mean.perc.q12  <- with(res, tapply(q12.perc, n.remov, mean))
  median.sDIFq12   <- with(res, tapply(sDIFq12, n.remov, median))
  median.sDIFq21  <- with(res, tapply(sDIFq21, n.remov, median))
  breaks.summary.tab       <-
    data.frame(
      percent_sp_removed = breaks,
      mean.perc.q12 = as.numeric(mean.perc.q12),
      mean.sDIFq12 = as.numeric(mean.sDIFq12),
      median.sDIFq12 = as.numeric(median.sDIFq12),
      mean.perc.q21 = as.numeric(mean.perc.q21),
      mean.sDIFq21 = as.numeric(mean.sDIFq21),
      median.sDIFq21 = as.numeric(median.sDIFq21)
    )
  
  #Creates a list with full model estimates:
  param0 <- list(
    q12 = q12.0,
    q21 = q21.0,
    aicc = aicc.0,
    optpar = optpar.0
  )
  
  #Generates output:
  res <- list(
    call = match.call(),
    data = full.data,
    optpar = transform,
    full.model.estimates = param0,
    breaks.summary.tab = breaks.summary.tab,
    sensi.estimates = sensi.estimates
  )
  
  class(res) <- "sensiSamp.TraitEvol"
  return(res)
  
}