R/tree_phyglm.R

In paternogbc/sensiPhy: Sensitivity Analysis for Comparative Methods

#' Phylogenetic uncertainty - Phylogenetic Logistic Regression
#'
#' Performs Phylogenetic logistic regression evaluating
#' uncertainty in trees topology.
#'
#' @param formula The model formula
#' @param data Data frame containing species traits with species as row names.
#' @param phy A phylogeny (class 'multiPhylo', see ?\code{ape}).
#' @param n.tree Number of times to repeat the analysis with n different trees picked 
#' randomly in the multiPhylo file.
#' If NULL, \code{n.tree} = 2
#' @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 fits a phylogenetic logistic regression model using \code{\link[phylolm]{phyloglm}}
#' to n trees, randomly picked in a multiPhylo file.
#'
#' 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{tree_phyglm} returns a list with the following
#' components:
#' @return \code{formula}: The formula
#' @return \code{data}: Original full dataset
#' @return \code{sensi.estimates}: Coefficients, aic and the optimised
#' value of the phylogenetic parameter (e.g. \code{lambda}) for each regression with a 
#' different phylogenetic tree.
#' @return \code{N.obs}: Size of the dataset after matching it with tree tips and removing NA's.
#' @return \code{stats}: Main statistics for model parameters.\code{CI_low} and \code{CI_high} are the lower 
#' and upper limits of the 95% confidence interval.
#' @return \code{all.stats}: Complete statistics for model parameters. \code{sd_intra} is the standard deviation 
#' due to intraspecific variation. \code{CI_low} and \code{CI_high} are the lower and upper limits 
#' of the 95% confidence interval.
#' @author Caterina Penone & Pablo Ariel Martinez
#' @seealso \code{\link[phylolm]{phyloglm}}, \code{\link{sensi_plot}},\code{\link{tree_phylm}}
#' @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
#'
#' Donoghue, M.J. & Ackerly, D.D. (1996). Phylogenetic Uncertainties and 
#' Sensitivity Analyses in Comparative Biology. Philosophical Transactions:
#'  Biological Sciences, pp. 1241-1249.
#'  
#' 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 
#'### Simulating Data:
#'set.seed(6987)
#'mphy = rmtree(150, N = 30)
#'x = rTrait(n=1,phy=mphy[[1]])
#'X = cbind(rep(1,150),x)
#'y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X)
#'dat = data.frame(y, x)
#'# Run sensitivity analysis:
#'tree <- tree_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 30)
#'# summary results:
#'summary(tree)
#'# Visual diagnostics for phylogenetic uncertainty:
#'sensi_plot(tree)
#' @export

tree_phyglm <- function(formula,
                        data,
                        phy,
                        n.tree = 2,
                        btol = 50,
                        track = TRUE,
                        ...) {
  #Error check
  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, "multiPhylo"))
    stop("phy must be class 'multiPhylo'")
  if (length(phy) < n.tree)
    stop("'n.tree' must be smaller (or equal) than the number of trees in the 'multiPhylo' object")
  else
    
    #Matching tree and phylogeny using utils.R
    datphy <- match_dataphy(formula, data, phy, ...)
  full.data <- datphy[[1]]
  phy <- datphy[[2]]
  
  # If the class of tree is multiphylo pick n=n.tree random trees
  trees <- sample(length(phy), n.tree, replace = F)
  
  #Create the results data.frame
  sensi.estimates <-
    data.frame(
      "n.tree" = numeric(),
      "intercept" = numeric(),
      "se.intercept" = numeric(),
      "pval.intercept" = numeric(),
      "estimate" = numeric(),
      "se.estimate" = numeric(),
      "pval.estimate" = numeric(),
      "aic" = numeric(),
      "optpar" = numeric()
    )
  
  #Model calculation
  counter = 1
  errors <- NULL
  c.data <- list()
  if (track == TRUE)
    pb <- utils::txtProgressBar(min = 0, max = n.tree, style = 3)
  for (j in trees) {
    #Match data order to tip order
    full.data <- full.data[phy[[j]]$tip.label, ]
    
    #phyloglm model
    mod = try(phylolm::phyloglm(
      formula,
      data = full.data,
      phy = phy[[j]],
      method = "logistic_MPLE",
      btol = btol
    ),
    FALSE)
    
    if (isTRUE(class(mod) == "try-error")) {
      error <- j
      names(error) <- rownames(c.data$full.data)[j]
      errors <- c(errors, error)
      next
    }
    
    
    else{
      intercept            <-
        phylolm::summary.phyloglm(mod)$coefficients[[1, 1]]
      se.intercept         <-
        phylolm::summary.phyloglm(mod)$coefficients[[1, 2]]
      estimate             <-
        phylolm::summary.phyloglm(mod)$coefficients[[2, 1]]
      se.estimate          <-
        phylolm::summary.phyloglm(mod)$coefficients[[2, 2]]
      pval.intercept       <-
        phylolm::summary.phyloglm(mod)$coefficients[[1, 4]]
      pval.estimate        <-
        phylolm::summary.phyloglm(mod)$coefficients[[2, 4]]
      aic.mod              <- mod$aic
      n                    <- mod$n
      #d                   <- mod$d
      optpar               <- mod$alpha
      
      if (track == TRUE)
        utils::setTxtProgressBar(pb, counter)
      
      #write in a table
      estim.simu <-
        data.frame(
          j,
          intercept,
          se.intercept,
          pval.intercept,
          estimate,
          se.estimate,
          pval.estimate,
          aic.mod,
          optpar,
          stringsAsFactors = F
        )
      sensi.estimates[counter,]  <- estim.simu
      counter = counter + 1
      
    }
  }
  if (track == TRUE)
    on.exit(close(pb))
  #calculate mean and sd for each parameter
  #variation due to tree choice
  statresults <- data.frame(
    min = apply(sensi.estimates, 2, min),
    max = apply(sensi.estimates, 2, max),
    mean = apply(sensi.estimates, 2, mean),
    sd_tree = apply(sensi.estimates, 2, stats::sd)
  )[-1,]
  
  statresults$CI_low  <-
    statresults$mean - qt(0.975, df = n.tree - 1) * statresults$sd_tree / sqrt(n.tree)
  statresults$CI_high <-
    statresults$mean + qt(0.975, df = n.tree - 1) * statresults$sd_tree / sqrt(n.tree)
  
  res <- list(
    call = match.call(),
    formula = formula,
    data = full.data,
    sensi.estimates = sensi.estimates,
    N.obs = n,
    stats = round(statresults[c(1:6), c(3, 5, 6)], digits = 3),
    all.stats = statresults
  )
  class(res) <- c("sensiTree", "sensiTreeL")
  return(res)
}