R/tree_clade_phylm.R

In paternogbc/sensiPhy: Sensitivity Analysis for Comparative Methods

#' Interaction between phylogenetic uncertainty and influential clade detection - Phylogenetic Linear Regression
#'
#' Estimate the impact on model estimates of phylogenetic linear regression after 
#' removing clades from the analysis and evaluating uncertainty in trees topology. 
#'
#' @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 'multiPhylo', see ?\code{ape}).
#' @param clade.col The column in the provided data frame which specifies the
#' clades (a character vector with clade names).
#' @param n.species Minimum number of species in a clade for the clade to be
#' included in the leave-one-out deletion analysis. Default is \code{5}.
#' @param n.sim Number of simulations for the randomization test.
#' @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 model The phylogenetic model to use (see Details). Default is \code{lambda}.
#' @param track Print a report tracking function progress (default = TRUE)
#' @param ... Further arguments to be passed to \code{phylolm}
#' @details
#' This function sequentially removes one clade at a time, fits a phylogenetic
#' linear regression model using \code{\link[phylolm]{phylolm}} and stores the
#' results. The impact of of a specific clade on model estimates is calculated by the
#' comparison between the full model (with all species) and the model without 
#' the species belonging to a clade. It repeats this operation using n trees, 
#' randomly picked in a multiPhylo file.
#' 
#'  Additionally, to account for the influence of the number of species on each 
#'  clade (clade sample size), this function also estimate a null distribution of slopes
#'  expected for the number of species in a given clade. This is done by fitting
#'  models without the same number of species in the given clade. 
#'  The number of simulations to be performed is set by 'n.sim'. To test if the 
#'  clade influence differs from the null expectation for a clade of that size, 
#'  a randomization test can be performed using 'summary(x)'. 
#'
#' All phylogenetic models from \code{phylolm} can be used, i.e. \code{BM},
#' \code{OUfixedRoot}, \code{OUrandomRoot}, \code{lambda}, \code{kappa},
#' \code{delta}, \code{EB} and \code{trend}. See ?\code{phylolm} for details.
#'
#' \code{clade_phylm} detects influential clades based on
#' difference in intercept and/or slope when removing a given clade compared
#' to the full model including all species. This is done for n trees in the multiphylo file.
#' 
#' Currently, this function can only implement simple linear models (i.e. 
#' \eqn{y = a + bx}). In the future we will implement more complex models.
#'
#' Output can be visualised using \code{sensi_plot}.
#'
#' @return The function \code{clade_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}) for the full model
#' without deleted species.
#' @return \code{sensi.estimates}: A data frame with all simulation
#' estimates. Each row represents a deleted clade for a tree iteration. 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.
#' @return \code{null.dist}: A data frame with estimates for the null distributions
#' for all clades analysed.
#' @return \code{data}: Original full dataset.
#' @return \code{errors}: Clades and/or trees where deletion resulted in errors.
#' @author Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
#' @seealso \code{\link[phylolm]{phylolm}}, \code{\link[sensiPhy]{tree_phylm}},
#'  \code{\link{clade_phylm}}, \code{\link{tree_clade_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
#'
#' 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 
#' \dontrun{
#'# Load data:
#'data(primates)
#'# run analysis:
#'clade_tree <- tree_clade_phylm(log(sexMaturity) ~ log(adultMass), 
#'phy = primates$phy, data = primates$data, clade.col = "family", n.sim = 50, n.tree = 5)
#'# To check summary results and most influential clades:
#'summary(clade_tree)
#'# Visual diagnostics for clade removal:
#'sensi_plot(clade_tree)
#'# Specify which clade removal to plot:
#'sensi_plot(clade_tree)
#'sensi_plot(clade_tree, "Cercopithecidae")
#'sensi_plot(clade_tree, clade = "Cebidae", graphs = 2)
#'}
#' \dontshow{
#'data(primates)
#'# run analysis:
#'clade_tree <- tree_clade_phylm(log(sexMaturity) ~ log(adultMass), 
#'                               phy = primates$phy, data = primates$data, 
#'                               clade.col = "family", n.sim = 10, n.tree = 2)
#'# To check summary results and most influential clades:
#'summary(clade_tree)
#'}
#' @export

tree_clade_phylm <-
  function(formula,
           data,
           phy,
           clade.col,
           n.species = 5,
           n.sim = 100,
           n.tree = 2,
           model = "lambda",
           track = TRUE,
           ...) {
    # Error checking:
    if (!inherits(data, "data.frame"))
      stop("data must be class 'data.frame'")
    if (missing(clade.col))
      stop("clade.col not defined. Please, define the column with clade names.")
    if (!inherits(formula, "formula"))
      stop("formula must be class 'formula'")
    if (!inherits(phy, "multiPhylo"))
      stop("phy must be class 'multiPhylo'")
    if (length(phy) < n.tree)
      stop("'times' must be smaller (or equal) than the number of trees in the 'multiPhylo' object")
    if ((model == "trend") && (sum(is.ultrametric(phy)) > 1))
      stop("Trend is unidentifiable for ultrametric trees., see ?phylolm for details")
    else
      
      #Match data and phy
      data_phy <- match_dataphy(formula, data, phy, ...)
    phy <- data_phy$phy
    full.data <- data_phy$data
    if (is.na(match(clade.col, names(full.data)))) {
      stop("Names column '", clade.col, "' not found in data frame'")
    }
    
    
    # If the class of tree is multiphylo pick n=times random trees
    trees <- sample(length(phy), n.tree, replace = F)
    
    
    # Identify CLADES to use and their sample size
    wc <- table(full.data[, clade.col]) > n.species
    uc <- table(full.data[, clade.col])[wc]
    
    if (length(uc) == 0)
      stop(
        paste(
          "There is no clade with more than ",
          n.species,
          " species. Change 'n.species' to fix this
                                  problem",
          sep = ""
        )
      )
    
    #List to store information
    tree.clade <- list ()
    
    #Start tree loop here
    errors <- NULL
    if (track == TRUE)
      pb <- utils::txtProgressBar(min = 0, max = n.tree, style = 3)
    counter = 1
    
    for (j in trees) {
      #Match data order to tip order
      full.data <- full.data[phy[[j]]$tip.label, ]
      
      #Select tree
      tree <- phy[[j]]
      
      tree.clade[[counter]] <-
        clade_phylm(
          formula,
          data = full.data,
          phy = tree,
          model,
          track = FALSE,
          clade.col,
          n.species,
          n.sim,
          verbose = FALSE,
          ...
        )
      
      if (track == TRUE)
        utils::setTxtProgressBar(pb, counter)
      counter = counter + 1
    }
    
    if (track == TRUE)
      close(pb)
    names(tree.clade) <- trees
    
    # Merge lists into data.frames between iterations:
    full.estimates  <-
      suppressWarnings(recombine(tree.clade, slot1 = 4, slot2 = 1))
    clade.estimates <- recombine(tree.clade, slot1 = 5)
    clade.estimates$info <- NULL
    null.dist       <- recombine(tree.clade, slot1 = 6)
    null.dist$info <- NULL
    
    #Generates output:
    res <- list(
      call = match.call(),
      model = model,
      formula = formula,
      full.model.estimates = full.estimates,
      sensi.estimates = clade.estimates,
      null.dist = null.dist,
      data = full.data,
      errors = errors,
      clade.col = clade.col
    )
    
    class(res) <- "sensiTree_Clade"
    
    ### Warnings:
    if (length(res$errors) > 0) {
      warning("Some clades deletion presented errors, please check: output$errors")
    }
    else {
      res$errors <- "No errors found."
    }
    return(res)
  }