/
influ_Discrete.R
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influ_Discrete.R
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#' Influential Species Detection - Trait Evolution Discrete Characters
#'
#' Fits models for trait evolution of discrete (binary) characters,
#' detecting influential species.
#'
#' @param data Data vector for a single binary trait, with names matching tips in \code{phy}.
#' @param phy A phylogeny (class 'phylo') matching \code{data}.
#' @param model The Mkn model to use (see Details).
#' @param transform The evolutionary model to transform the tree (see Details). Default is \code{none}.
#' @param cutoff The cut-off parameter for influential species (see Details).
#' @param bounds settings to constrain parameter estimates. See \code{\link[geiger]{fitDiscrete}}
#' @param n.cores number of cores to use. If 'NULL', number of cores is detected.
#' @param track Print a report tracking function progress (default = TRUE)
#' @param ... Further arguments to be passed to \code{\link[geiger]{fitDiscrete}}
#' @details
#' This function sequentially removes one species at a time,
#' fits a model of discrete character evolution using \code{\link[geiger]{fitDiscrete}},
#' stores the results and calculates the effects on model parameters. Currently, only
#' binary discrete traits are supported.
#'
#' \code{influ_discrete} detects influential species based on the standardised
#' difference in q12 or q21 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.
#'
#' Different character model from \code{fitDiscrete} can be used, including \code{ER} (equal-rates),
#' \code{SYM} (symmetric), \code{ARD} (all-rates-different) and \code{meristic} (stepwise fashion).
#'
#' Different transformations to the phylogenetic tree from \code{fitDiscrete} can be used, i.e. \code{none},
#' \code{EB}, \code{lambda}, \code{kappa} and\code{delta}.
#'
#' See \code{\link[geiger]{fitDiscrete}} for more details on character models and tree transformations.
#'
#' @return The function \code{tree_discrete} returns a list with the following
#' components:
#' @return \code{call}: The function call
#' @return \code{cutoff}: The value selected for \code{cutoff}
#' @return \code{data}: The original full data vector
#' @return \code{optpar}: Transformation parameter used (e.g. \code{lambda}, \code{kappa} etc.)
#' @return \code{full.model.estimates}: Parameter estimates (transition rates q12 and q21),
#' AICc and the optimised value of the phylogenetic transformation parameter (e.g. \code{lambda})
#' for the full model.
#' @return \code{influential_species}: List of influential species, based on standardised
#' difference in estimates for q12 and q21. Species are ordered from most influential to
#' less influential and only include species with a standardised difference > \code{cutoff}.
#' @return \code{sensi.estimates}: Parameter estimates (transition rates q12 and q21),,(percentual) difference
#' in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21),
#' AICc and the optimised value of the phylogenetic transformation parameter (e.g. \code{lambda})
#' for each analysis with a species deleted.
#' @author Gijsbert Werner & Gustavo Paterno
#' @seealso \code{\link[geiger]{fitDiscrete}}
#' @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.
#'
#' Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.
#'
#' Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008.
#' GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.
#'
#' @examples
#' \dontrun{
#' #Load data:
#' data("primates")
#' #Create a binary trait factor
#' adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small")
#' adultMass_binary<-as.factor(as.factor(adultMass_binary))
#' names(adultMass_binary)<-rownames(primates$data)
#' #Model trait evolution accounting for influential species
#' influ_binary<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
#' model = "SYM",transform = "none",cutoff = 2,n.cores = 2,track = TRUE)
#' #Print summary statistics
#' summary(influ_binary)
#' sensi_plot(influ_binary) #q12 and q21 are, as expected, exactly the same in symmetrical model.
#' #Use a different evolutionary model.
#' influ_binary2<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
#' model = "SYM",transform = "delta",n.cores = 2,track = TRUE)
#' summary(influ_binary2)
#' sensi_plot(influ_binary2)
#' #Or change the cutoff and transformation
#' influ_binary3<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
#' model = "ARD",transform = "none",cutoff = 1.2,n.cores = 2,track = TRUE)
#' summary(influ_binary3)
#' sensi_plot(influ_binary3)
#' }
#' @export
influ_discrete <- function(data,
phy,
model,
transform = "none",
bounds = list(),
cutoff = 2,
n.cores = NULL,
track = TRUE,
...) {
#Error check
if (is.null(model))
stop("model must be specified (e.g. 'ARD' or 'SYM'")
if (!inherits(data, "factor"))
stop("data must supplied as a factor with species as names. Consider as.factor()")
if (length(levels(data)) > 2)
stop("discrete data can have maximal two levels")
if (!inherits(phy, "phylo"))
stop("phy must be class 'phylo'")
if (transform == "white")
stop("the white-noise (non-phylogenetic) model is not allowed")
else
#Matching tree
full.data <- data
phy <- phy
#Calculates the full model, extracts model parameters
N <- length(full.data)
mod.0 <-
geiger::fitDiscrete(
phy = phy,
dat = full.data,
model = model,
transform = transform,
bounds = bounds,
ncores = n.cores,
...
)
q12.0 <- mod.0$opt$q12
q21.0 <- mod.0$opt$q21
aicc.0 <- mod.0$opt$aicc
if (transform == "none") {
optpar.0 <- NA
}
if (transform == "EB") {
optpar.0 <- mod.0$opt$a
}
if (transform == "lambda") {
optpar.0 <- mod.0$opt$lambda
}
if (transform == "kappa") {
optpar.0 <- mod.0$opt$kappa
}
if (transform == "delta") {
optpar.0 <- mod.0$opt$delta
}
#Creates empty data frame to store model outputs
sensi.estimates <- data.frame(
"species" = numeric(),
"q12" = numeric(),
"DIFq12" = numeric(),
"q12.perc" = numeric(),
"q21" = numeric(),
"DIFq21" = numeric(),
"q21.perc" = numeric(),
"aicc" = 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, setdiff(phy$tip.label, names(crop.data)))
mod = try(geiger::fitDiscrete(
phy = crop.phy,
dat = crop.data,
model = model,
transform = transform,
bounds = bounds,
ncores = n.cores,
...
),
TRUE)
if (isTRUE(class(mod) == "try-error")) {
error <- i
names(error) <- rownames(full.data$data)[i]
errors <- c(errors, error)
next
}
else {
sp <- phy$tip.label[i]
q12 <- mod$opt$q12
q21 <- mod$opt$q21
DIFq12 <- q12 - q12.0
DIFq21 <- q21 - q21.0
q12.perc <-
round((abs(DIFq12 / q12.0)) * 100,
digits = 1)
q21.perc <-
round((abs(DIFq21 / q21.0)) * 100,
digits = 1)
aicc <- mod$opt$aicc
if (transform == "none") {
optpar <- NA
}
if (transform == "EB") {
optpar <- mod$opt$a
}
if (transform == "lambda") {
optpar <- mod$opt$lambda
}
if (transform == "kappa") {
optpar <- mod$opt$kappa
}
if (transform == "delta") {
optpar <- mod$opt$delta
}
if (track == TRUE)
utils::setTxtProgressBar(pb, i)
# Stores values for each simulation
# Store reduced model parameters:
estim.simu <- data.frame(sp,
q12,
DIFq12,
q12.perc,
q21,
DIFq21,
q21.perc,
aicc,
optpar,
stringsAsFactors = F)
sensi.estimates[counter,] <- estim.simu
counter = counter + 1
}
}
if (track == TRUE)
on.exit(close(pb))
#Calculates Standardized DFbeta and DIFq12
sDIFq12 <- sensi.estimates$DIFq12 /
stats::sd(sensi.estimates$DIFq12)
sDIFq21 <- sensi.estimates$DIFq21 /
stats::sd(sensi.estimates$DIFq21)
sensi.estimates$sDIFq21 <- sDIFq21
sensi.estimates$sDIFq12 <- sDIFq12
#Creates a list with full model estimates:
#full model estimates:
param0 <- list(
q12 = q12.0,
q21 = q21.0,
aicc = aicc.0,
optpar = optpar.0
)
#Identifies influencital species (sDF > cutoff) and orders by influence
reorder.on.q21 <- sensi.estimates[order(abs(sensi.estimates$sDIFq21), decreasing =
T), c("species", "sDIFq21")]
influ.sp.q21 <-
as.character(reorder.on.q21$species[abs(reorder.on.q21$sDIFq21) > cutoff])
reorder.on.q12 <- sensi.estimates[order(abs(sensi.estimates$sDIFq12), decreasing =
T), c("species", "sDIFq12")]
influ.sp.q12 <-
as.character(reorder.on.q12$species[abs(reorder.on.q12$sDIFq12) > cutoff])
#Generates output:
res <- list(
call = match.call(),
cutoff = cutoff,
data = full.data,
optpar = transform,
full.model.estimates = param0,
influential.species = list(influ.sp.q12 = influ.sp.q12,
influ.sp.q21 = influ.sp.q21),
sensi.estimates = sensi.estimates,
errors = errors
)
class(res) <- "sensiInflu.TraitEvol"
### Warnings:
if (length(res$errors) > 0) {
warning("Some species deletion presented errors, please check: output$errors")
}
else {
res$errors <- "No errors found."
}
return(res)
}