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intra_samp_phylm.R
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intra_samp_phylm.R
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#' Interaction between intraspecific variability and species sampling - Phylogenetic Linear Regression
#'
#' Performs analyses of sensitivity to species sampling by randomly removing
#' species and detecting the effects on parameter estimates in a phylogenetic
#' linear regression, 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 n.sim The number of times species are randomly deleted for each
#' \code{break}.
#' @param n.intra Number of datasets resimulated taking into account intraspecific variation (see: \code{"intra_phylm"})
#' @param breaks A vector containing the percentages of species to remove.
#' @param model The phylogenetic model to use (see Details). Default is \code{lambda}.
#' #' @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 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 randomly removes a given percentage of species (controlled by
#' \code{breaks}) from the full phylogenetic linear regression, fits a phylogenetic
#' linear regression model without these species using \code{\link[phylolm]{phylolm}},
#' repeats this many times (controlled by \code{n.sim}), stores the results and
#' calculates the effects on model parameters.
#' 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
#' evaluates the effects of sampling 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.
#'
#' 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{samp_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} or \code{kappa}) for
#' the full model without deleted species.
#' @return \code{sensi.estimates}: A data frame with all simulation
#' estimates. Each row represents a model rerun with a given number of species
#' \code{n.remov} removed, representing \code{n.percent} of the full dataset.
#' 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. Lastly we reported the standardised difference in intercept
#' (\code{sDIFintercept}) and slope (\code{sDIFestimate}).
#' @return \code{sign.analysis} For each break (i.e. each percentage of species
#' removed) this reports the percentage of statistically significant (at p<0.05)
#' intercepts (\code{perc.sign.intercept}) over all repetitions as well as the
#' percentage of statisticaly significant (at p<0.05) slopes (\code{perc.sign.estimate}).
#' @return \code{data}: Original full dataset.
#' @note Please be aware that dropping species may reduce power to detect
#' significant slopes/intercepts and may partially be responsible for a potential
#' effect of species removal on p-values. Please also consult standardised differences
#' in the (summary) output.
#' @author Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone
#' @seealso \code{\link[phylolm]{phylolm}},\code{\link{samp_phylm}},
#' \code{\link{intra_phylm}}, \code{\link{intra_samp_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
#'
#' 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.
#'
#' 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.
#'
#' @import ape phylolm
#'
#' @examples
#' \dontrun{
#' # Load data:
#' data(alien)
#' # Run analysis:
#' samp <- intra_samp_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]],
#' y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL,
#' data = alien$data, n.intra = 5, n.sim=10)
#' summary(samp)
#' head(samp$sensi.estimates)
#' # Visual diagnostics
#' sensi_plot(samp)
#' # You can specify which graph and parameter ("estimate" or "intercept") to print:
#' sensi_plot(samp, graphs = 1)
#' sensi_plot(samp, graphs = 2)
#' }
#' @export
intra_samp_phylm <-
function(formula,
data,
phy,
n.sim = 10,
n.intra = 3,
breaks = seq(.1, .5, .1),
model = "lambda",
Vy = NULL,
Vx = NULL,
distrib = "normal",
y.transf = NULL,
x.transf = NULL,
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 (length(breaks) < 2)
stop("Please include more than one break, e.g. breaks=c(.3,.5)")
if ((model == "trend") && (sum(is.ultrametric(phy)) > 1))
stop("Trend is unidentifiable for ultrametric trees., see ?phylolm for details")
else
#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)
}
else
funr <- function(a, b) {
stats::runif(1, a - b, a + b)
}
#List to store information
intra.samp <- list()
species.NA <- list()
#Start intra loop here
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")) ||
is.null(Vy))
{
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$respV <-
apply(full.data[, c(resp, Vy)], 1, function(x)
funr(x[1], x[2]))
}
#Vx is not provided or is not numeric, do not pick random value
if (!inherits(full.data[, pred], c("numeric", "integer")) ||
is.null(Vx))
{
full.data$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$predV))
}
#skip iteration if there are NA's in the dataset
species.NA[[i]] <-
rownames(full.data[with(full.data, is.na(predV) | is.na(respV)), ])
if (sum(is.na(full.data[, c("respV", "predV")]) > 0))
next
#Run the model
intra.samp[[i]] <-
samp_phylm(
respV ~ predV,
data = full.data,
phy = phy,
n.sim = n.sim,
model,
breaks = breaks,
track = FALSE,
verbose = FALSE,
...
)
if (track == TRUE)
utils::setTxtProgressBar(pb, counter)
counter = counter + 1
}
if (track == TRUE)
close(pb)
names(intra.samp) <- 1:n.intra
# Merge lists into data.frames between iterations:
full.estimates <-
suppressWarnings(recombine(intra.samp, slot1 = 4, slot2 = 1))
influ.estimates <- recombine(intra.samp, slot1 = 5)
influ.estimates$info <- NULL
perc.sign <- recombine(intra.samp, slot1 = 6)
perc.sign$info <- NULL
#Generates output:
res <- list(
call = match.call(),
model = model,
formula = formula,
full.model.estimates = full.estimates,
sensi.estimates = influ.estimates,
sign.analysis = perc.sign,
data = full.data
)
class(res) <- "sensiIntra_Samp"
### Warnings:
if (length(res$errors) > 0) {
warning("Some species deletion presented errors, please check: output$errors")
}
else {
res$errors <- "No errors found."
}
return(res)
}