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#' Species Sampling uncertainty - Trait Evolution Continuous Characters
#'
#' Fits models for trait evolution of continuous characters,
#' evaluating sampling uncertainty.
#'
#' @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 n.sim The number of times species are randomly deleted for each \code{break}.
#' @param breaks A vector containing the percentages of species to remove.
#' @param model The evolutionary model (see Details).
#' @param bounds settings to constrain parameter estimates. See \code{\link[geiger]{fitContinuous}}
#' @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]{fitContinuous}}
#' @details
#' This function randomly removes a given percentage of species (controlled by \code{breaks}),
#' fits different models of continuous character evolution using \code{\link[geiger]{fitContinuous}},
#' repeats this this many times (controlled by \code{n.sim}), stores the results and calculates
#' the effects on model parameters.
#'
#' Different evolutionary models from \code{fitContinuous} can be used, i.e. \code{BM},\code{OU},
#' \code{EB}, \code{trend}, \code{lambda}, \code{kappa}, \code{delta} and \code{drift}.
#'
#' See \code{\link[geiger]{fitContinuous}} for more details on character models and tree transformations.
#'
#' Output can be visualised using \code{sensi_plot}.
#'
#' @return The function \code{tree_continuous} returns a list with the following
#' components:
#' @return \code{call}: The function call
#' @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 (rate of evolution \code{sigsq}
#' and where applicable \code{optpar}), root state \code{z0},
#' AICc for the full model without deleted species.
#' @return \code{break.summary.tab}: Summary per \code{break} of the mean and median effects
#' of species removal on percentage and absolute change parameter estimates.
#' @return \code{sensi.estimates}: Parameter estimates (sigsq and optpar),(percentual) difference
#' in parameter estimate compared to the full model (DIFsigsq, sigsq.perc,sDIFsigsq,
#' DIFoptpar, optpar.perc,sDIFoptpar),
#' AICc and z0 for each repeat with random species removed.
#' @return \code{optpar}: Transformation parameter used (e.g. \code{lambda}, \code{kappa} etc.)
#' @author Gijsbert Werner & Gustavo Paterno
#' @seealso \code{\link[geiger]{fitContinuous}}
#' @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.
#'
#' Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014).
#' A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.
#'
#' @examples
#' \dontshow{
#' #Load data:
#' data("primates")
#' #Model trait evolution accounting for phylogenetic uncertainty
#' adultMass<-primates$data$adultMass
#' names(adultMass)<-rownames(primates$data)
#' samp_cont<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
#' model = "BM",n.sim=1,breaks=c(.1,.2),n.cores = 2, track = TRUE)
#' }
#' \dontrun{
#' #Load data:
#' data("primates")
#' #Model trait evolution accounting for sampling size
#' adultMass<-primates$data$adultMass
#' names(adultMass)<-rownames(primates$data)
#' samp_cont<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
#' model = "OU",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2, track = TRUE)
#' #Print summary statistics
#' summary(samp_cont)
#' sensi_plot(samp_cont)
#' sensi_plot(samp_cont, graphs = 1)
#' #Use a different evolutionary model
#' samp_cont2<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
#' model = "kappa",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE)
#' summary(samp_cont2)
#' sensi_plot(samp_cont2)
#' sensi_plot(samp_cont2, graphs = 2)
#' samp_cont3<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
#' model = "BM",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE)
#' summary(samp_cont3)
#' }
#' @export
samp_continuous <- function(data,
phy,
n.sim = 30,
breaks = seq(.1, .5, .1),
model,
n.cores = NULL,
bounds = list(),
track = TRUE,
...) {
#Error check
if (is.null(model))
stop("model must be specified, e.g. 'OU' or 'lambda'")
if (!inherits(data, "numeric") |
is.null(names(data)))
stop("data must supplied as a numeric vector with species as names")
if (!inherits(phy, "phylo"))
stop("phy must be class 'phylo'")
if (model == "white")
stop("the white-noise (non-phylogenetic) model is not allowed")
if ((model == "drift") &
(ape::is.ultrametric(phy)))
stop(
"A drift model is unidentifiable for ultrametric trees., see ?fitContinuous for details"
)
if (length(breaks) < 2)
stop("Please include more than one break, e.g. breaks=c(.3,.5)")
else
#Matching tree and phylogeny
full.data <- data
phy <- phy
#Calculates the full model, extracts model parameters
N <- length(full.data)
mod.0 <-
geiger::fitContinuous(
phy = phy,
dat = full.data,
model = model,
bounds = bounds,
ncores = n.cores,
...
)
sigsq.0 <- mod.0$opt$sigsq
z0.0 <- mod.0$opt$z0
aicc.0 <- mod.0$opt$aicc
if (model == "BM") {
optpar.0 <- NA
}
if (model == "OU") {
optpar.0 <- mod.0$opt$alpha
}
if (model == "EB") {
optpar.0 <- mod.0$opt$a
}
if (model == "trend") {
optpar.0 <- mod.0$opt$slope
}
if (model == "lambda") {
optpar.0 <- mod.0$opt$lambda
}
if (model == "kappa") {
optpar.0 <- mod.0$opt$kappa
}
if (model == "delta") {
optpar.0 <- mod.0$opt$delta
}
if (model == "drift") {
optpar.0 <- mod.0$opt$drift
}
#Creates empty data frame to store model outputs
sensi.estimates <-
data.frame(
"n.remov" = numeric(),
"n.percent" = numeric(),
"sigsq" = numeric(),
"DIFsigsq" = numeric(),
"sigsq.perc" = numeric(),
"optpar" = numeric(),
"DIFoptpar" = numeric(),
"optpar.perc" = numeric(),
"z0" = numeric(),
"aicc" = numeric()
)
#Loops over breaks, remove percentage of species determined by 'breaks
#and repeat determined by 'n.sim'.
counter <- 1
limit <- sort(round((breaks) * length(full.data), digits = 0))
NL <- length(breaks) * n.sim
if (track == TRUE)
pb <- utils::txtProgressBar(min = 0, max = NL, style = 3)
for (i in limit) {
for (j in 1:n.sim) {
#Prep simulation data
exclude <- sample(1:N, i)
crop.data <- full.data[-exclude]
crop.phy <-
ape::drop.tip(phy, setdiff(phy$tip.label, names(crop.data)))
#Run the model
mod = try(geiger::fitContinuous(
phy = crop.phy,
dat = crop.data,
model = model,
bounds = bounds,
ncores = n.cores,
...
),
TRUE)
if (isTRUE(class(mod) == "try-error")) {
next
}
else {
sigsq <- mod$opt$sigsq
z0 <- mod$opt$z0
aicc <- mod$opt$aicc
DIFsigsq <- sigsq - sigsq.0
sigsq.perc <-
round((abs(DIFsigsq / sigsq.0)) * 100,
digits = 1)
if (model == "BM") {
optpar <- NA
}
if (model == "OU") {
optpar <- mod$opt$alpha
}
if (model == "EB") {
optpar <- mod$opt$a
}
if (model == "trend") {
optpar <- mod$opt$slope
}
if (model == "lambda") {
optpar <- mod$opt$lambda
}
if (model == "kappa") {
optpar <- mod$opt$kappa
}
if (model == "delta") {
optpar <- mod$opt$delta
}
if (model == "drift") {
optpar <- mod$opt$drift
}
DIFoptpar <- optpar - optpar.0
optpar.perc <-
round((abs(DIFoptpar / optpar.0)) * 100,
digits = 1)
n.remov <- i
n.percent <- round((n.remov / N) * 100, digits = 0)
#rep <- j
if (track == TRUE)
(utils::setTxtProgressBar(pb, counter))
# Store reduced model parameters:
estim.simu <- data.frame(
n.remov,
n.percent,
sigsq,
DIFsigsq,
sigsq.perc,
optpar,
DIFoptpar,
optpar.perc,
z0,
aicc,
stringsAsFactors = F
)
sensi.estimates[counter,] <- estim.simu
counter <- counter + 1
}
}
}
if (track == TRUE)
on.exit(close(pb))
#Calculates Standardized DFs
sDIFsigsq <- sensi.estimates$DIFsigsq /
stats::sd(sensi.estimates$DIFsigsq)
sDIFoptpar <- sensi.estimates$DIFoptpar /
stats::sd(sensi.estimates$DIFoptpar)
sensi.estimates$sDIFsigsq <- sDIFsigsq
sensi.estimates$sDIFoptpar <- sDIFoptpar
#Calculates stats
res <- sensi.estimates
n.sim <- table(res$n.remov)
breaks <- unique(res$n.percent)
mean.sDIFsigsq <- with(res, tapply(sDIFsigsq, n.remov, mean))
mean.sDIFoptpar <-
with(res, tapply(sDIFoptpar, n.remov, mean))
mean.perc.optpar <-
with(res, tapply(optpar.perc, n.remov, mean))
mean.perc.sigsq <-
with(res, tapply(sigsq.perc, n.remov, mean))
median.sDIFsigsq <-
with(res, tapply(sDIFsigsq, n.remov, median))
median.sDIFoptpar <-
with(res, tapply(sDIFoptpar, n.remov, median))
breaks.summary.tab <-
data.frame(
percent_sp_removed = breaks,
mean.perc.sigsq = as.numeric(mean.perc.sigsq),
mean.sDIFsigsq = as.numeric(mean.sDIFsigsq),
median.sDIFsigsq = as.numeric(median.sDIFsigsq),
mean.perc.optpar = as.numeric(mean.perc.optpar),
mean.sDIFoptpar = as.numeric(mean.sDIFoptpar),
median.sDIFoptpar = as.numeric(median.sDIFoptpar)
)
#Creates a list with full model estimates:
param0 <- list(
sigsq = sigsq.0,
optpar = optpar.0,
z0 = z0.0,
aicc = aicc.0
)
#Generates output:
res <- list(
call = match.call(),
data = full.data,
optpar = model,
full.model.estimates = param0,
breaks.summary.tab = breaks.summary.tab,
sensi.estimates = sensi.estimates
)
class(res) <- "sensiSamp.TraitEvol"
return(res)
}
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