R/samp_phyglm.R

In sensiPhy: Sensitivity Analysis for Comparative Methods

#' Sensitivity Analysis Species Sampling  - Phylogenetic Logistic Regression
#'
#' Performs analyses of sensitivity to species sampling by randomly removing
#' species and detecting the effects on parameter estimates in phylogenetic
#' logistic regression.
#'
#' @param formula The model formula
#' @param data Data frame containing species traits with row 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 btol Bound on searching space. For details see \code{phyloglm}
#' @param track Print a report tracking function progress (default = TRUE)
#' @param ... Further arguments to be passed to \code{phyloglm}
#' @details
#'
#' This function randomly removes a given percentage of species (controlled by
#' \code{breaks}) from the full phylogenetic logistic regression, fits a phylogenetic
#' logistic regression model without these species using \code{\link[phylolm]{phyloglm}},
#' repeats this many times (controlled by \code{n.sim}), stores the results and
#' calculates the effects on model parameters.
#'
#' Only logistic regression using the "logistic_MPLE"-method from
#' \code{phyloglm} is implemented.
#'
#' Currently, this function can only implement simple logistic models (i.e. \eqn{trait~
#' predictor}). In the future we will implement more complex models.
#'
#' Output can be visualised using \code{sensi_plot}.
#' @return The function \code{samp_phylm} returns a list with the following
#' components:
#' @return \code{formula}: The formula
#' @return \code{full.model.estimates}: Coefficients, aic and the optimised
#' value of the phylogenetic parameter (e.g. \code{lambda} or \code{kappa}) for
#' the full model without deleted species.
#' @return \code{sensi.estimates}: A data frame with all simulation
#' estimates. Each row represents a model rerun with a given number of species
#' \code{n.remov} removed, representing \code{n.percent} of the full dataset.
#' Columns report the calculated regression intercept (\code{intercept}),
#' difference between simulation intercept and full model intercept (\code{DIFintercept}),
#' the percentage of change in intercept compared to the full model (\code{intercept.perc})
#' and intercept p-value (\code{pval.intercept}). All these parameters are also reported
#' for the regression slope (\code{DIFestimate} etc.). Additionally, model aic value
#' (\code{AIC}) and the optimised value (\code{optpar}) of the phylogenetic
#' parameter (e.g. \code{kappa} or \code{lambda}, depending on the phylogenetic model
#' used) are reported. Lastly we reported the standardised difference in intercept 
#' (\code{sDIFintercept}) and slope (\code{sDIFestimate}). 
#' @return \code{sign.analysis} For each break (i.e. each percentage of species
#' removed) this reports the percentage of statistically significant (at p<0.05)
#' intercepts (\code{perc.sign.intercept}) over all repetitions as well as the
#' percentage of statisticaly significant (at p<0.05) slopes (\code{perc.sign.estimate}).
#' @return \code{data}: Original full dataset.
#' @note Please be aware that dropping species may reduce power to detect 
#' significant slopes/intercepts and may partially be responsible for a potential 
#' effect of species removal on p-values. Please also consult standardised differences
#' in the (summary) output. 
#' @author Gustavo Paterno & Gijsbert D.A. Werner
#' @seealso \code{\link[phylolm]{phyloglm}}, \code{\link{samp_phylm}},
#' \code{\link{influ_phyglm}}, \code{\link{sensi_plot}}
#' @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.  
#'
#' 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.
#'   
#' #' Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for 
#' Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
#' @examples
#'# Simulate Data:
#'set.seed(6987)
#'phy = rtree(100)
#'x = rTrait(n=1,phy=phy)
#'X = cbind(rep(1,100),x)
#'y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
#'dat = data.frame(y, x)
#'# Run sensitivity analysis:
#'samp <- samp_phyglm(y ~ x, data = dat, phy = phy, n.sim = 10) 
#'# To check summary results and most influential species:
#'summary(samp)
#'\dontrun{
#'# Visual diagnostics for clade removal:
#'sensi_plot(samp)
#'}
#' @export

samp_phyglm <- function(formula,
                        data,
                        phy,
                        n.sim = 30,
                        breaks = seq(.1, .5, .1),
                        btol = 50,
                        track = TRUE,
                        ...)
{
  if (!inherits(formula, "formula"))
    stop("formula must be class 'formula'")
  if (!inherits(data, "data.frame"))
    stop("data must be class 'data.frame'")
  if (!inherits(phy, "phylo"))
    stop("phy must be class 'phylo'")
  if (length(breaks) < 2)
    stop("Please include more than one break,
                          e.g. breaks=c(.3,.5)")
  else
    
    # Check match between data and phy
    data_phy <- match_dataphy(formula, data, phy, ...)
  
  full.data <- data_phy$data
  phy <- data_phy$phy
  N <- nrow(full.data)
  
  mod.0 <- phylolm::phyloglm(
    formula,
    data = full.data,
    phy = phy,
    method = "logistic_MPLE",
    btol = btol
  )
  
  if (isTRUE(mod.0$convergence != 0))
    stop("Full model failed to converge,
                                              consider changing btol. See ?phyloglm")
  intercept.0             <- mod.0$coefficients[[1]]
  estimate.0                 <- mod.0$coefficients[[2]]
  optpar.0                <- mod.0$alpha
  pval.intercept.0        <-
    phylolm::summary.phyloglm(mod.0)$coefficients[[1, 4]]
  pval.estimate.0            <-
    phylolm::summary.phyloglm(mod.0)$coefficients[[2, 4]]
  aic.0                   <- mod.0$aic
  
  #Creates empty data frame to store model outputs
  sensi.estimates <-
    data.frame(
      "n.remov" = numeric(),
      "n.percent" = numeric(),
      "intercept" = numeric(),
      "DIFintercept" = numeric(),
      "intercept.perc" = numeric(),
      "pval.intercept" = numeric(),
      "estimate" = numeric(),
      "DIFestimate" = numeric(),
      "estimate.perc" = numeric(),
      "pval.estimate" = numeric(),
      "AIC" = 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) * nrow(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) {
      exclude <- sample(1:N, i)
      crop.data <- full.data[-exclude,]
      crop.phy <-  ape::drop.tip(phy, phy$tip.label[exclude])
      mod <- try(phylolm::phyloglm(
        formula,
        data = crop.data,
        phy = crop.phy,
        method = "logistic_MPLE",
        btol = btol
      ),
      TRUE)
      
      if (isTRUE(class(mod) == "try-error")) {
        next
      }
      else {
        intercept             <- mod$coefficients[[1]]
        estimate                 <- mod$coefficients[[2]]
        pval.intercept        <-
          phylolm::summary.phyloglm(mod)$coefficients[[1, 4]]
        pval.estimate <-
          phylolm::summary.phyloglm(mod)$coefficients[[2, 4]]
        aic <- mod$aic
        DIFintercept <- intercept - intercept.0
        DIFestimate <- estimate - estimate.0
        intercept.perc  <- round((abs(DIFintercept / intercept.0)) * 100, digits = 1)
        estimate.perc <- round((abs(DIFestimate / estimate.0)) * 100, digits = 1)
        aic <- mod$aic
        optpar <- mod$alpha
        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
        estim.simu <-
          data.frame(
            n.remov,
            n.percent,
            intercept,
            DIFintercept,
            intercept.perc,
            pval.intercept,
            estimate,
            DIFestimate,
            estimate.perc,
            pval.estimate,
            aic,
            optpar,
            stringsAsFactors = F
          )
        sensi.estimates[counter,]  <- estim.simu
        counter = counter + 1
      }
    }
  }
  if (track == TRUE)
    on.exit(close(pb))
  
  #Calculates Standardized DFbeta and DIFintercept
  sDIFintercept <- sensi.estimates$DIFintercept /
    stats::sd(sensi.estimates$DIFintercept)
  sDIFestimate     <- sensi.estimates$DIFestimate /
    stats::sd(sensi.estimates$DIFestimate)
  
  sensi.estimates$sDIFintercept <- sDIFintercept
  sensi.estimates$sDIFestimate     <- sDIFestimate
  
  #Calculates percentages of signficant intercepts & slopes within breaks.
  res                     <- sensi.estimates
  n.sim                   <- table(res$n.remov)
  breaks                  <- unique(res$n.percent)
  sign.intercept          <- res$pval.intercept > .05
  sign.estimate              <- res$pval.estimate > .05
  res$sign.intercept      <- sign.intercept
  res$sign.estimate          <- sign.estimate
  perc.sign.intercept <-
    1 - (with(res, tapply(sign.intercept, n.remov, sum))) / n.sim
  perc.sign.estimate     <-
    1 - (with(res, tapply(sign.estimate, n.remov, sum))) / n.sim
  mean.sDIFestimate       <-
    with(res, tapply(sDIFestimate, n.remov, mean))
  mean.sDIFintercept   <- with(res, tapply(sDIFintercept, n.remov, mean))
  mean.perc.intercept <- with(res, tapply(intercept.perc, n.remov, mean))
  mean.perc.estimate     <-
    with(res, tapply(estimate.perc, n.remov, mean))
  perc.sign.tab       <- data.frame(
    percent_sp_removed = breaks,
    perc.sign.intercept = as.numeric(perc.sign.intercept),
    mean.perc.intercept = as.numeric(mean.perc.intercept),
    mean.sDIFintercept = as.numeric(mean.sDIFintercept),
    perc.sign.estimate = as.numeric(perc.sign.estimate),
    mean.perc.estimate = as.numeric(mean.perc.estimate),
    mean.sDIFestimate = as.numeric(mean.sDIFestimate)
  )
  
  #Creates a list with full model estimates:
  param0 <- list(
    coef = phylolm::summary.phyloglm(mod.0)$coefficients,
    aic = phylolm::summary.phyloglm(mod.0)$aic,
    optpar = mod.0$alpha
  )
  
  #Generates output:
  res <- list(
    call = match.call(),
    model = "logistic_MPLE",
    formula = formula,
    full.model.estimates = param0,
    sensi.estimates = sensi.estimates,
    sign.analysis = perc.sign.tab,
    data = full.data
  )
  class(res) <- "sensiSamp"
  return(res)
  
}