Defines functions intra_influ_phylm

Documented in intra_influ_phylm

#' Interaction between intraspecific variability and influential species - Phylogenetic Linear Regression
#' Performs leave-one-out deletion analysis for phylogenetic linear regression,
#' and detects influential species, while taking into account potential
#' interactions with intraspecific variability.
#' @param formula The model formula:  \code{response~predictor}. 
#' @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 model The phylogenetic model to use (see Details). Default is \code{lambda}.
#' @param cutoff The cutoff value used to identify for influential species
#' (see Details)
#' @param n.intra Number of datasets resimulated taking into account intraspecific variation (see: \code{"intra_phylm"})
#' @param Vy Name of the column containing the standard deviation or the standard error of the response 
#' variable. When information is not available for one taxon, the value can be 0 or \code{NA}.
#' @param Vx Name of the column containing the standard deviation or the standard error of the predictor 
#' variable. When information is not available for one taxon, the value can be 0 or \code{NA}
#' @param y.transf Transformation for the response variable (e.g. \code{"log"} or \code{"sqrt"}). Please use this 
#' argument instead of transforming data in the formula directly (see also details below).
#' @param x.transf Transformation for the predictor variable (e.g. \code{"log"} or \code{"sqrt"}). Please use this 
#' argument instead of transforming data in the formula directly (see also details below).
#' @param distrib A character string indicating which distribution to use to generate a random value for the response 
#' and/or predictor variables. Default is normal distribution: "normal" (function \code{\link{rnorm}}).
#' Uniform distribution: "uniform" (\code{\link{runif}})
#' Warning: we recommend to use normal distribution with Vx or Vy = standard deviation of the mean.
#' @param track Print a report tracking function progress (default = TRUE)
#' @param ... Further arguments to be passed to \code{phylolm}
#' @details
#' This function fits a phylogenetic linear regression model using \code{\link[phylolm]{phylolm}}, and detects
#' influential species by sequentially deleting one at a time. The regression is repeated \code{n.intra} times for 
#' simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function 
#' generates a random value for each row in the dataset using the standard deviation or errors supplied, and 
#' detect the influential species within that iteration. 
#' 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{influ_phylm} detects influential species based on the standardised
#' difference in intercept and/or slope when removing a given species compared
#' to the full model including all species. Species with a standardised difference
#' above the value of \code{cutoff} are identified as influential. The default
#' value for the cutoff is 2 standardised differences change.
#' Currently, this function can only implement simple linear models (i.e. \eqn{trait~
#' predictor}). In the future we will implement more complex models.
#' Output can be visualised using \code{sensi_plot}.
#' @section Warning:  
#' When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems
#' for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. 
#' Setting \code{n.intra} at high values can take a long time to execute, since the total number of iterations 
#' equals \code{n.intra * nrow(data)}.
#' @return The function \code{intra_influ_phylm} returns a list with the following
#' components:
#' @return \code{cutoff}: The value selected for \code{cutoff}
#' @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{influential_species}: List of influential species, both
#' based on standardised difference in intercept and in the slope of the
#' regression. Species are ordered from most influential to less influential and
#' only include species with a standardised difference > \code{cutoff}.
#' @return \code{sensi.estimates}: A data frame with all simulation
#' estimates. Each row represents a deleted clade for an iteration of resimulated
#' data. Columns report the calculated regression intercept (\code{intercept}), 
#' difference between simulation intercept and full model intercept (\code{DIFintercept}), 
#' the standardised difference (\code{sDIFintercept}), 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{data}: Original full dataset.
#' @return \code{errors}: Species where deletion resulted in errors.
#' @author Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
#' @seealso \code{\link[phylolm]{phylolm}}, \code{\link{intra_phylm}},
#' \code{\link{influ_phylm}},\code{\link{intra_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.  
#' 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(alien)
#' # run analysis:
#' intra_influ <- intra_influ_phylm(formula = gestaLen ~ adultMass, phy = alien$phy[[1]],
#' data=alien$data,model="lambda",y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL,
#' n.intra=30,distrib = "normal")
#' summary(intra_influ)
#' sensi_plot(intra_influ)
#' }
#' \dontshow{
#'# run analysis:
#'intra_influ <- intra_influ_phylm(formula = gestaLen ~ adultMass, phy = alien$phy[[1]],
#'                                 data=alien$data[1:15, ],model="lambda",y.transf = log,
#'                                 x.transf = NULL,Vy="SD_gesta",Vx=NULL,
#'                                 n.intra=5,distrib = "normal")
#' }
#' @export

intra_influ_phylm <- function(formula,
                              Vy = NULL,
                              Vx = NULL,
                              y.transf = NULL,
                              x.transf = NULL,
                              n.intra = 10,
                              distrib = "normal",
                              model = "lambda",
                              cutoff = 2,
                              track = TRUE,
                              ...) {
  #Error check
  if (is.null(Vx) & is.null(Vy))
    stop("Vx or Vy must be defined")
  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 (formula[[2]] != all.vars(formula)[1] ||
      formula[[3]] != all.vars(formula)[2])
    stop("Please use arguments y.transf or x.transf for data transformation")
  if (distrib == "normal")
    warning ("distrib=normal: make sure that standard deviation is provided for Vx and/or Vy")
  if ((model == "trend") & (sum(is.ultrametric(phy)) > 1))
    stop("Trend is unidentifiable for ultrametric trees., see ?phylolm for details")
    #Matching tree and phylogeny using utils.R
    datphy <- match_dataphy(formula, data, phy, ...)
  full.data <- datphy[[1]]
  phy <- datphy[[2]]
  resp <- all.vars(formula)[1]
  pred <- all.vars(formula)[2]
  if (!is.null(Vy) && sum(is.na(full.data[, Vy])) != 0) {
    full.data[is.na(full.data[, Vy]), Vy] <- 0
  if (!is.null(Vx) && sum(is.na(full.data[, Vx])) != 0) {
    full.data[is.na(full.data[, Vx]), Vx] <- 0
  #Function to pick a random value in the interval
  if (distrib == "normal")
    funr <- function(a, b) {
      stats::rnorm(1, a, b)
    funr <- function(a, b) {
      stats::runif(1, a - b, a + b)
  #List to store information
  intra.influ <- list ()
  N  <- nrow(full.data)
  #Start intra loop here
  species.NA <- list()
  errors <- NULL
  if (track == TRUE)
    pb <- utils::txtProgressBar(min = 0, max = n.intra, style = 3)
  counter = 1
  for (i in 1:n.intra) {
    ##Set response and predictor variables
    #Vy is not provided or is not numeric, do not pick random value
    if (!inherits(full.data[, resp], c("numeric", "integer")) ||
      full.data$respV <-
        stats::model.frame(formula, data = full.data)[, 1]
    #choose a random value in [mean-se,mean+se] if Vy is provided
    if (!is.null(Vy))
      full.data$respV <-
        apply(full.data[, c(resp, Vy)], 1, function(x)
          funr(x[1], x[2]))
    #Vx is not provided or is not numeric, do not pick random value
    if (!inherits(full.data[, pred], c("numeric", "integer")) ||
      full.data$predV <-
        stats::model.frame(formula, data = full.data)[, 2]
    #choose a random value in [mean-se,mean+se] if Vx is provided
    if (!is.null(Vx))
      full.data$predV <-
        apply(full.data[, c(pred, Vx)], 1, function(x)
          funr(x[1], x[2]))
    #transform Vy and/or Vx if x.transf and/or y.transf are provided
    if (!is.null(y.transf))
      suppressWarnings (full.data$respV <- y.transf(full.data$respV))
    if (!is.null(x.transf))
      suppressWarnings (full.data$predV <- x.transf(full.data$predV))
    #skip iteration if there are NA's in the dataset
    species.NA[[i]] <-
      rownames(full.data[with(full.data, is.na(predV) | is.na(respV)), ])
    if (sum(is.na(full.data[, c("respV", "predV")]) > 0))
    #Run the model
    intra.influ[[i]] <-
        respV ~ predV,
        data = full.data,
        phy = phy,
        track = FALSE,
        verbose = FALSE
    if (track == TRUE)
      utils::setTxtProgressBar(pb, counter)
    counter = counter + 1
  names(intra.influ) <- 1:n.intra
  if (track == TRUE)
  # Merge lists into data.frames between iterations:
  full.estimates  <-
    suppressWarnings(recombine(intra.influ, slot1 = 4, slot2 = 1))
  #influ species slope
  influ.sp.estimate <-
    (lapply(intra.influ, function(x)
  influ.sp.estimate <- as.data.frame(as.matrix(influ.sp.estimate))
  names(influ.sp.estimate) <- "influ.sp.estimate"
  influ.sp.estimate$tree <- row.names(influ.sp.estimate)
  #influ species intercept
  influ.sp.intercept <-
    (lapply(intra.influ, function(x)
  influ.sp.intercept <- as.data.frame(as.matrix(influ.sp.intercept))
  names(influ.sp.intercept) <- "influ.sp.intercept"
  influ.sp.intercept$tree <- row.names(influ.sp.intercept)
  influ.estimates <- recombine(intra.influ, slot1 = 6)
  influ.estimates$info <- NULL
  #Generates output:
  res <- list(
    call = match.call(),
    cutoff = cutoff,
    formula = formula,
    full.model.estimates = full.estimates,
    influential.species = list(
      influ.sp.estimate = influ.sp.estimate,
      influ.sp.intercept = influ.sp.intercept
    sensi.estimates = influ.estimates,
    data = full.data
  class(res) <- "sensiIntra_Influ"
  ### Warnings:
  if (length(res$errors) > 0) {
    warning("Some species deletion presented errors, please check: output$errors")
  else {
    res$errors <- "No errors found."

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sensiPhy documentation built on April 14, 2020, 7:15 p.m.