R/influ_phylm.R

In sensiPhy: Sensitivity Analysis for Comparative Methods

#' Influential species detection - Phylogenetic Linear Regression
#'
#' Performs leave-one-out deletion analysis for phylogenetic linear regression,
#' and detects influential species.
#'
#' @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 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 track Print a report tracking function progress (default = TRUE)
#' @param ... Further arguments to be passed to \code{phylolm}
#' @details
#' This function sequentially removes one species at a time, fits a phylogenetic
#' linear regression model using \code{\link[phylolm]{phylolm}}, stores the
#' results and detects influential species.
#'
#' 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}.
#'
#' @return The function \code{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. 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 & Gijsbert D.A. Werner
#' @seealso \code{\link[phylolm]{phylolm}}, \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
#' 
#' 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 
#' # Load data:
#' data(alien)
#' # run analysis:
#' influ <- influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], 
#' data = alien$data)
#' # To check summary results:
#'summary(influ)
#'# Most influential speciesL
#'influ$influential.species
#'# Visual diagnostics
#'sensi_plot(influ)
#'# You can specify which graph and parameter ("estimate" or "intercept") to print: 
#'sensi_plot(influ, param = "estimate", graphs = 2)
#' @export

influ_phylm <-
        function(formula,
                 data,
                 phy,
                 model = "lambda",
                 cutoff = 2,
                 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 ((model == "trend") && (ape::is.ultrametric(phy)))
                        stop("Trend is unidentifiable for ultrametric trees., see ?phylolm for details")
                else
                        
                        # Check match between data and phy
                        data_phy <- match_dataphy(formula, data, phy, ...)
                #Calculates the full model, extracts model parameters
                full.data <- data_phy$data
                phy <- data_phy$phy
                N               <- nrow(full.data)
                mod.0           <- phylolm::phylolm(formula,
                                                    data = full.data,
                                                    model = model,
                                                    phy = phy)
                intercept.0      <- mod.0$coefficients[[1]]
                estimate.0          <- mod.0$coefficients[[2]]
                pval.intercept.0 <-
                        phylolm::summary.phylolm(mod.0)$coefficients[[1, 4]]
                pval.estimate.0     <-
                        phylolm::summary.phylolm(mod.0)$coefficients[[2, 4]]
                optpar.0 <- mod.0$optpar
                
                
                #Creates empty data frame to store model outputs
                sensi.estimates <-
                        data.frame(
                                "species" = 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 all species, and removes each one individually
                counter <- 1
                errors <- NULL
                if (track == TRUE)
                        pb <- utils::txtProgressBar(min = 0,
                                                    max = N,
                                                    style = 3)
                for (i in 1:N) {
                        crop.data <- full.data[c(1:N)[-i], ]
                        crop.phy <-  ape::drop.tip(phy, phy$tip.label[i])
                        mod = try(phylolm::phylolm(
                                formula,
                                data = crop.data,
                                model = model,
                                phy = crop.phy
                        ),
                        TRUE)
                        if (isTRUE(class(mod) == "try-error")) {
                                error <- i
                                names(error) <- rownames(full.data$data)[i]
                                errors <- c(errors, error)
                                next
                        }
                        else {
                                sp                   <- phy$tip.label[i]
                                intercept            <-
                                        mod$coefficients[[1]]
                                estimate                <-
                                        mod$coefficients[[2]]
                                DIFintercept          <-
                                        intercept - intercept.0
                                DIFestimate              <-
                                        estimate - estimate.0
                                intercept.perc       <-
                                        round((abs(
                                                DIFintercept / intercept.0
                                        )) * 100, digits = 1)
                                estimate.perc           <-
                                        round((abs(
                                                DIFestimate / estimate.0
                                        )) * 100, digits = 1)
                                pval.intercept       <-
                                        phylolm::summary.phylolm(mod)$coefficients[[1, 4]]
                                pval.estimate           <-
                                        phylolm::summary.phylolm(mod)$coefficients[[2, 4]]
                                aic.mod              <- mod$aic
                                if (model == "BM" | model == "trend") {
                                        optpar <- NA
                                }
                                if (model != "BM" & model != "trend") {
                                        optpar               <- mod$optpar
                                }
                                
                                if (track == TRUE)
                                        utils::setTxtProgressBar(pb, i)
                                # Stores values for each simulation
                                estim.simu <-
                                        data.frame(
                                                sp,
                                                intercept,
                                                DIFintercept,
                                                intercept.perc,
                                                pval.intercept,
                                                estimate,
                                                DIFestimate,
                                                estimate.perc,
                                                pval.estimate,
                                                aic.mod,
                                                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$sDIFestimate     <- sDIFestimate
                sensi.estimates$sDIFintercept <- sDIFintercept
                
                #Creates a list with full model estimates:
                param0 <-
                        list(
                                coef = phylolm::summary.phylolm(mod.0)$coefficients,
                                aic = phylolm::summary.phylolm(mod.0)$aic,
                                optpar = mod.0$optpar
                        )
                
                #Identifies influencital species (sDF > cutoff) and orders by influence
                reorder.on.estimate         <- sensi.estimates[order(abs(sensi.estimates$sDIFestimate), decreasing =
                                                                             T), c("species", "sDIFestimate")]
                influ.sp.estimate           <-
                        as.character(reorder.on.estimate$species[abs(reorder.on.estimate$sDIFestimate) >
                                                                         cutoff])
                reorder.on.intercept     <- sensi.estimates[order(abs(sensi.estimates$sDIFintercept), decreasing =
                                                                          T), c("species", "sDIFintercept")]
                influ.sp.intercept       <-
                        as.character(reorder.on.intercept$species[abs(reorder.on.intercept$sDIFintercept) >
                                                                          cutoff])
                
                #Generates output:
                res <- list(
                        call = match.call(),
                        cutoff = cutoff,
                        formula = formula,
                        full.model.estimates = param0,
                        influential.species = list(
                                influ.sp.estimate = influ.sp.estimate,
                                influ.sp.intercept = influ.sp.intercept
                        ),
                        sensi.estimates = sensi.estimates,
                        data = full.data,
                        errors = errors
                )
                class(res) <- "sensiInflu"
                ### Warnings:
                if (length(res$errors) > 0) {
                        warning("Some species deletion presented errors, please check: output$errors")
                }
                else {
                        res$errors <- "No errors found."
                }
                
                return(res)
                
        }