Defines functions intra_clade_phylm

Documented in intra_clade_phylm

#' Interaction between intraspecific variability and influential clade detection - Phylogenetic Linear Regression
#' Estimate the impact on model estimates of phylogenetic linear regression after 
#' removing clades from the analysis, while taking into account potential
#' interactions with intraspecific variability.
#' @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 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.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 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. This operation 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. 
#' Additionally, to account for the influence of the number of species on each 
#' clade (clade sample size), this function also estimates a null distribution 
#' 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.
#' 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{intra_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. 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 iterations where deletion resulted in errors.
#' @author Gustavo Paterno, Caterina Penone
#' @seealso \code{\link[phylolm]{phylolm}}, \code{\link[sensiPhy]{intra_phylm}},
#' \code{\link{clade_phylm}}, \code{\link{intra_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(alien)
#' intra_clade <- intra_clade_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]],
#'  data = alien$data, clade.col = "family", n.sim = 30, n.intra = 5, 
#'  y.transf = log, x.transf = log, Vy="SD_gesta")
#' summary(intra_clade)
#' sensi_plot(intra_clade)
#' sensi_plot(intra_clade, clade = "Bovidae", graphs = 2)
#' sensi_plot(intra_clade, clade = "Mustelidae", graphs = 2)
#' }
#' \dontshow{
#'intra_clade <- intra_clade_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]],
#'                                 data = alien$data, clade.col = "family", n.sim = 1, n.intra = 1, 
#'                                 y.transf = log, x.transf = log, Vy="SD_gesta")
#' }
#' @export

intra_clade_phylm <-
           n.species = 5,
           n.sim = 100,
           n.intra = 2,
           Vy = NULL,
           Vx = NULL,
           distrib = "normal",
           y.transf = NULL,
           x.transf = NULL,
           model = "lambda",
           track = TRUE,
           ...) {
    # Error checking:
    if (is.null(Vx) & is.null(Vy))
      stop("Vx or Vy must be defined")
    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(phy, "phylo"))
      stop("phy must be class 'phylo'")
    if (!inherits(formula, "formula"))
      stop("formula must be class 'formula'")
    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")
      #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'")
    #Prepare data
    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)
    #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)
          "There is no clade with more than ",
          " species. Change 'n.species' to fix this
          sep = ""
    #List to store information
    intra.clade <- list ()
    #Start clade loop here
    errors <- NULL
    if (track == TRUE)
      pb <- utils::txtProgressBar(min = 0, max = n.intra, style = 3)
    counter = 1
    for (j 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))
      intra.clade[[j]] <-
          formula = respV ~ predV,
          data = full.data,
          phy = phy,
          model = model,
          clade.col = clade.col,
          n.species = n.species,
          n.sim = n.sim,
          track = FALSE,
          verbose = FALSE,
      if (track == TRUE)
        utils::setTxtProgressBar(pb, counter)
      counter = counter + 1
    if (track == TRUE)
    names(intra.clade) <- 1:n.intra
    # Merge lists into data.frames between iterations:
    full.estimates  <-
      suppressWarnings(recombine(intra.clade, slot1 = 4, slot2 = 1))
    clade.estimates <- recombine(intra.clade, slot1 = 5)
    clade.estimates$info <- NULL
    null.dist       <- recombine(intra.clade, slot1 = 6)
    null.dist$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) <- "sensiIntra_Clade"
    ### Warnings:
    if (length(res$errors) > 0) {
      warning("Some clades deletion presented errors, please check: output$errors")
    else {
      res$errors <- "No errors found."

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