Nothing
R/p2c2m.analyze.R
In P2C2M: Posterior Predictive Checks of Coalescent Models
p2c2m.analyze <-
function (inFn, inData, prmFHndl) {
# Descr: core of script, coordinates the metric calculation
# Deps: (various)
# I/p: inFn
# inData
# prmFHndl
debugBool = get("P2C2M_flg_dbgBool", envir=P2C2M_globalVars)
descrStats = get("P2C2M_flg_dscrStat", envir=P2C2M_globalVars)
mpiBool = get("P2C2M_flg_mpiBool", envir=P2C2M_globalVars)
nReps = get("P2C2M_flg_nReps", envir=P2C2M_globalVars)
singleAllele = get("P2C2M_flg_snglAl", envir=P2C2M_globalVars)
srtBool = get("P2C2M_flg_srtBool", envir=P2C2M_globalVars)
verboseBool = get("P2C2M_flg_vrbBool", envir=P2C2M_globalVars)
if (debugBool) {
cat("\n", xtermStyle::style("DEBUG> core.analyze", fg="red"), sep="")
}
##########################
# 1. Preparatory actions #
##########################
n_loci = length(inData$loci$dta)
n_sTrees = length(inData$stre$dta)
# generate empty matrices of dimensions 'matrix'
post_dist = post_pred_dist = matrix(nrow=n_sTrees, ncol=n_loci)
# label the columns of the empty matrices by the locus names
colnames(post_dist) = colnames(post_pred_dist) = inData$loci$dta
# Initilizing outdata
outD = list()
##########################################################
# 2. Metrics for the trees of the posterior distribution #
##########################################################
loghelpers.prntmngr(paste("Metrics for the trees of the",
"posterior distribution"), uprFlg=T)
##########################################################
# 2.a. Looping through genes, calc. descript. statistics #
##########################################################
# Loop through all gene tree names
for (j in 1:n_loci) {
loghelpers.prntmngr(paste("Locus '", inData$loci$dta[j], "'",
sep=""))
loghelpers.prntmngr("Analyzing gene trees:\n", nwlFlg=F)
# Loop through all species trees
for (i in 1:n_sTrees) {
# Display tree number on screen
loghelpers.prntmngr(paste(" ", i, sep=""), nwlFlg=F)
# Save tree number as replicate ID
assign("P2C2M_flg_repID", paste("post_dist", i, sep="."),
envir = P2C2M_globalVars)
# DEBUGLINES:
#cat("\ninData$gtre$dta[[j]][[i]]\n"); print(inData$gtre$dta[[j]][[i]])
#cat("\ninData$ptre$dta$tree[[i]]\n"); print(inData$ptre$dta$tree[[i]])
#cat("\ninData$ptre$dta$names\n"); print(inData$ptre$dta$names)
#cat("\ninData$stre$dta[[i]]\n"); print(inData$stre$dta[[i]])
#cat("\ninData$asoc$dta\n"); print(inData$asoc$dta)
#cat("\ninData$pldy$dta[j]\n"); print(inData$pldy$dta[j])
post_dist[i,j] = corehelpers.metrics(inData$gtre$dta[[j]][[i]],
inData$ptre$dta$tree[[i]],
inData$ptre$dta$names,
inData$stre$dta[[i]],
inData$asoc$dta[[j]],
inData$pldy$dta[j],
descrStats,
singleAllele)
}
loghelpers.prntmngr("\n", nwlFlg=F)
}
###########################
# 2.b. Assembling results #
###########################
for (s in descrStats) {
# Populating list "outD" with the results of the trees of the posterior distribution
outD[[s]]$post_dist$unsort = as.data.frame(rtnlc(post_dist, which(s==descrStats)))
# Assigning colnames
colnames(outD[[s]]$post_dist$unsort) = inData$loci$dta
# Generating a sorted result clone
outD[[s]]$post_dist$sorted = apply(outD[[s]]$post_dist$unsort, MARGIN=2, sort)
}
#####################################################################
# 3. Metrics for the trees of the posterior predictive distribution #
#####################################################################
loghelpers.prntmngr(paste("Metrics for the trees of the",
"post. predictive distribution"), uprFlg=T)
##################################
# 3.a. Initiate MPI (if applic.) #
##################################
# Determining number of processors (i.e. CPUs) available
if (Sys.info()['sysname'] == "Linux") {
nproc = as.numeric(system("nproc", intern=T))
}
# MacOS option
if (Sys.info()['sysname'] == "Darwin") {
nproc = as.numeric(system("sysctl -n hw.ncpu", intern=T))
}
if (mpiBool && nproc >= 3) {
loghelpers.prntmngr(paste("\n", "Initialize parallelization", "|",
"N of processors:", toString(nproc)))
# Specifying one master and nproc-1 slaves
#Rmpi:: mpi.spawn.Rslaves(nslaves=nproc-1, needlog=F)
Rmpi:: mpi.spawn.Rslaves(nslaves=nproc-1)
# Passing separate functions to MPI environment
mpiEnvir = c(calc.lcwt,
calc.gsi,
calc.ndc,
calc.parse,
calc.coal,
calchelpers.brprob,
calchelpers.descend,
calchelpers.dmvparse,
calchelpers.gtreeparse,
calchelpers.nodetips,
calchelpers.probcalc,
corehelpers.metrics,
# LEGACY: corehelpers.repl,
frmtMntse,
# LEGACY: apTreeshape::as.treeshape.phylo,
ape::branching.times,
ape::drop.tip,
genealogicalSorting::gsi,
phybase::loglikeSP)
lapply(mpiEnvir, Rmpi::mpi.bcast.Robj2slave)
}
##############################
# 3.b. Looping through genes #
##############################
simTrees_across_loci = list()
for (j in 1:n_loci) {
loghelpers.prntmngr(paste("Locus '", inData$loci$dta[j], "'",
sep=""))
loghelpers.prntmngr("Analyzing the posterior predictive trees:\n",
nwlFlg=F)
n_tips = length(inData$gtre$dta[[j]][[1]]$tip.label)
simTrees_across_sTrees = list()
for (i in 1:n_sTrees) {
# Display tree number on screen
loghelpers.prntmngr(paste(" ", i, sep=""), nwlFlg=F)
# Save tree number as replicate ID
assign("P2C2M_flg_repID", paste("post_pred_dist", j, i, sep="."),
envir = P2C2M_globalVars)
############################
# 3.c. Simulation of trees #
############################
# print(inData$assoc)
# speciesName alleleName
# [1,] "A" "a1"
# [2,] "A" "a10"
# [3,] "A" "a11"
# [4,] "A" "a12"
# [5,] "A" "a2"
# A constituent-frequency table is required for the simulations in ms,
# because ms saves the alleles as numbers such as "1", "2", "3", ...
CFTable = readhelpers.makeCFtable(inData$asoc$dta[[j]])
# print(CFTable)
# aList V2
# Var1 "A" "1"
# Var1 "A" "2"
# Var1 "A" "3"
# Simulate gene trees
simTrees = ms.exec(CFTable, inData$stre$dta[[i]], n_tips,
inData$pldy$dta[j], prmFHndl)
class(simTrees) = "multiPhylo"
## Possible improvement in TFL: why not "length(simTrees)" instead of
## "as.numeric(nReps)"
simReps = matrix(nrow=as.numeric(nReps), ncol=1)
##################################################################
# 3.d. Calculation of descript. statistics depend. on MPI status #
##################################################################
if (mpiBool && nproc >= 3) {
# Applying function "corehelpers.metrics" in parallel
# The second element in mpi.parLapply has to be the function that
# the data is to be applied
simReps = Rmpi::mpi.parLapply(simTrees,
corehelpers.metrics,
inData$ptre$dta$tree[[i]],
inData$ptre$dta$names,
inData$stre$dta[[i]],
CFTable,
inData$pldy$dta[j],
descrStats,
singleAllele)
simReps = as.matrix(as.character(simReps))
}
else {
for (k in 1:nReps) {
simReps[k,1] = corehelpers.metrics(simTrees[[k]],
inData$ptre$dta$tree[[i]],
inData$ptre$dta$names,
inData$stre$dta[[i]],
CFTable,
inData$pldy$dta[j],
descrStats,
singleAllele)
}
}
#########################################
# 3.e. Parsing of average metric values #
#########################################
# Extracting the listcolumns of the matrix simReps via GSO.rtnlc
sumD = list()
for (s in descrStats) {
sumD[[s]] = Mode(rtnlc(simReps, which(s==descrStats)))
}
sumD = unlist(sumD)
names(sumD) = NULL
# Save the replicate values to variable "post_pred_dist"
post_pred_dist[i,j] = toString(sumD)
#############################################
# 3.f. Preparing simulated trees for saving #
#############################################
# Loop over simulated replicates
## Possible improvement: Use apply function instead of loop
# Generate set of print-ready simtrees
simTrees_wLabels = list()
for (k in 1:nReps) {
# LEGA!CY: # Generating a simTree version where tips have been replaced with the correct allele names
# LEGA!CY: assoc_ids = unlist(lapply(inData$asoc$dta[[j]][,2], substring, first=2))
# LEGA!CY: # TFL removes leading zeros
# LEGA!CY: assoc_ids = gsub("^[0]+", "", assoc_ids)
# LEGA!CY: # TFL removes all non-numeric characters
# LEGA!CY: oldLbls = gsub("[^0-9]", "", simTrees[[k]]$tip.label)
# LEGA!CY: # DEBUGLINES:
# LEGA!CY: #cat("\nassoc_ids\n"); print(assoc_ids)
# LEGA!CY: #cat("\nsimTrees[[k]]$tip.label\n"); print(simTrees[[k]]$tip.label)
# LEGA!CY: #cat("\noldLbls\n"); print(oldLbls)
# LEGA!CY: match_indcs = match(oldLbls, assoc_ids)
# LEGA!CY: # Error handling
# LEGA!CY: if (NA %in% match_indcs | "NA" %in% match_indcs) {
# LEGA!CY: stop(cat("\nERROR: Cannot label the terminals of the posterior predictive trees correctly.\n"))
# LEGA!CY: }
# LEGA!CY: nwLbls = inData$asoc$dta[[j]][,2][match_indcs]
# LEGA!CY: simTrees_wLabels[[k]] = simTrees[[k]]
# LEGA!CY: simTrees_wLabels[[k]]$tip.label = mapply(toString, nwLbls)
simLbls = gsub("[^0-9]", "", simTrees[[k]]$tip.label)
nwLbls = inData$asoc$dta[[j]][as.integer(simLbls),2]
simTrees_wLabels[[k]] = simTrees[[k]]
simTrees_wLabels[[k]]$tip.label = mapply(toString, nwLbls)
class(simTrees_wLabels) = "multiPhylo"
}
simTrees_across_sTrees[[paste("sTree_", i, sep="")]] = simTrees_wLabels
# End of looping through sTrees
}
simTrees_across_loci[[inData$loci$dta[[j]]]] = simTrees_across_sTrees
loghelpers.prntmngr("\n", nwlFlg=F)
# End of looping through loci
}
##################
# 3.g. Close MPI #
##################
if (mpiBool && nproc >= 3) {
# Closing slaves
loghelpers.prntmngr(paste("\n", "Close parallelization", "\n",
sep=""))
Rmpi::mpi.close.Rslaves(dellog=T)
}
#############################
# 3.g. Save simulated trees #
#############################
outD$simTrees = simTrees_across_loci
###########################
# 3.h. Assembling results #
###########################
for (s in descrStats) {
# Populating list "outD" with the simulated results
# Both "as.matrix" and "as.data.frame" are critical below
outD[[s]]$post_pred_dist$unsort = as.matrix(as.data.frame(rtnlc(post_pred_dist, which(s==descrStats))))
# Assigning colnames
colnames(outD[[s]]$post_pred_dist$unsort) = inData$loci$dta
# Generating a sorted result clone
outD[[s]]$post_pred_dist$sorted = apply(outD[[s]]$post_pred_dist$unsort, MARGIN=2, sort)
}
#####################################
# 4. Calculating metric differences #
#####################################
loghelpers.prntmngr(paste("Calculating differences btw. the",
"posterior and the posterior predictive",
"distribution"))
for (s in descrStats) {
# Populating list "outD" with the differences
if (srtBool) {
outD[[s]]$dif = statshelpers.diffrnce(outD[[s]]$post_dist$sorted,
outD[[s]]$post_pred_dist$sorted)
# Remove the outD set not chosen
outD[[s]]$post_dist$unsort = NULL
outD[[s]]$post_pred_dist$unsort = NULL
}
else {
outD[[s]]$dif = statshelpers.diffrnce(outD[[s]]$post_dist$unsort,
outD[[s]]$post_pred_dist$unsort)
# Remove the outD set not chosen
outD[[s]]$post_dist$sorted = NULL
outD[[s]]$post_pred_dist$sorted = NULL
}
}
return(outD)
}
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p2c2m.analyze <-
function (inFn, inData, prmFHndl) {
# Descr: core of script, coordinates the metric calculation
# Deps: (various)
# I/p: inFn
# inData
# prmFHndl
debugBool = get("P2C2M_flg_dbgBool", envir=P2C2M_globalVars)
descrStats = get("P2C2M_flg_dscrStat", envir=P2C2M_globalVars)
mpiBool = get("P2C2M_flg_mpiBool", envir=P2C2M_globalVars)
nReps = get("P2C2M_flg_nReps", envir=P2C2M_globalVars)
singleAllele = get("P2C2M_flg_snglAl", envir=P2C2M_globalVars)
srtBool = get("P2C2M_flg_srtBool", envir=P2C2M_globalVars)
verboseBool = get("P2C2M_flg_vrbBool", envir=P2C2M_globalVars)
if (debugBool) {
cat("\n", xtermStyle::style("DEBUG> core.analyze", fg="red"), sep="")
}
##########################
# 1. Preparatory actions #
##########################
n_loci = length(inData$loci$dta)
n_sTrees = length(inData$stre$dta)
# generate empty matrices of dimensions 'matrix'
post_dist = post_pred_dist = matrix(nrow=n_sTrees, ncol=n_loci)
# label the columns of the empty matrices by the locus names
colnames(post_dist) = colnames(post_pred_dist) = inData$loci$dta
# Initilizing outdata
outD = list()
##########################################################
# 2. Metrics for the trees of the posterior distribution #
##########################################################
loghelpers.prntmngr(paste("Metrics for the trees of the",
"posterior distribution"), uprFlg=T)
##########################################################
# 2.a. Looping through genes, calc. descript. statistics #
##########################################################
# Loop through all gene tree names
for (j in 1:n_loci) {
loghelpers.prntmngr(paste("Locus '", inData$loci$dta[j], "'",
sep=""))
loghelpers.prntmngr("Analyzing gene trees:\n", nwlFlg=F)
# Loop through all species trees
for (i in 1:n_sTrees) {
# Display tree number on screen
loghelpers.prntmngr(paste(" ", i, sep=""), nwlFlg=F)
# Save tree number as replicate ID
assign("P2C2M_flg_repID", paste("post_dist", i, sep="."),
envir = P2C2M_globalVars)
# DEBUGLINES:
#cat("\ninData$gtre$dta[[j]][[i]]\n"); print(inData$gtre$dta[[j]][[i]])
#cat("\ninData$ptre$dta$tree[[i]]\n"); print(inData$ptre$dta$tree[[i]])
#cat("\ninData$ptre$dta$names\n"); print(inData$ptre$dta$names)
#cat("\ninData$stre$dta[[i]]\n"); print(inData$stre$dta[[i]])
#cat("\ninData$asoc$dta\n"); print(inData$asoc$dta)
#cat("\ninData$pldy$dta[j]\n"); print(inData$pldy$dta[j])
post_dist[i,j] = corehelpers.metrics(inData$gtre$dta[[j]][[i]],
inData$ptre$dta$tree[[i]],
inData$ptre$dta$names,
inData$stre$dta[[i]],
inData$asoc$dta[[j]],
inData$pldy$dta[j],
descrStats,
singleAllele)
}
loghelpers.prntmngr("\n", nwlFlg=F)
}
###########################
# 2.b. Assembling results #
###########################
for (s in descrStats) {
# Populating list "outD" with the results of the trees of the posterior distribution
outD[[s]]$post_dist$unsort = as.data.frame(rtnlc(post_dist, which(s==descrStats)))
# Assigning colnames
colnames(outD[[s]]$post_dist$unsort) = inData$loci$dta
# Generating a sorted result clone
outD[[s]]$post_dist$sorted = apply(outD[[s]]$post_dist$unsort, MARGIN=2, sort)
}
#####################################################################
# 3. Metrics for the trees of the posterior predictive distribution #
#####################################################################
loghelpers.prntmngr(paste("Metrics for the trees of the",
"post. predictive distribution"), uprFlg=T)
##################################
# 3.a. Initiate MPI (if applic.) #
##################################
# Determining number of processors (i.e. CPUs) available
if (Sys.info()['sysname'] == "Linux") {
nproc = as.numeric(system("nproc", intern=T))
}
# MacOS option
if (Sys.info()['sysname'] == "Darwin") {
nproc = as.numeric(system("sysctl -n hw.ncpu", intern=T))
}
if (mpiBool && nproc >= 3) {
loghelpers.prntmngr(paste("\n", "Initialize parallelization", "|",
"N of processors:", toString(nproc)))
# Specifying one master and nproc-1 slaves
#Rmpi:: mpi.spawn.Rslaves(nslaves=nproc-1, needlog=F)
Rmpi:: mpi.spawn.Rslaves(nslaves=nproc-1)
# Passing separate functions to MPI environment
mpiEnvir = c(calc.lcwt,
calc.gsi,
calc.ndc,
calc.parse,
calc.coal,
calchelpers.brprob,
calchelpers.descend,
calchelpers.dmvparse,
calchelpers.gtreeparse,
calchelpers.nodetips,
calchelpers.probcalc,
corehelpers.metrics,
# LEGACY: corehelpers.repl,
frmtMntse,
# LEGACY: apTreeshape::as.treeshape.phylo,
ape::branching.times,
ape::drop.tip,
genealogicalSorting::gsi,
phybase::loglikeSP)
lapply(mpiEnvir, Rmpi::mpi.bcast.Robj2slave)
}
##############################
# 3.b. Looping through genes #
##############################
simTrees_across_loci = list()
for (j in 1:n_loci) {
loghelpers.prntmngr(paste("Locus '", inData$loci$dta[j], "'",
sep=""))
loghelpers.prntmngr("Analyzing the posterior predictive trees:\n",
nwlFlg=F)
n_tips = length(inData$gtre$dta[[j]][[1]]$tip.label)
simTrees_across_sTrees = list()
for (i in 1:n_sTrees) {
# Display tree number on screen
loghelpers.prntmngr(paste(" ", i, sep=""), nwlFlg=F)
# Save tree number as replicate ID
assign("P2C2M_flg_repID", paste("post_pred_dist", j, i, sep="."),
envir = P2C2M_globalVars)
############################
# 3.c. Simulation of trees #
############################
# print(inData$assoc)
# speciesName alleleName
# [1,] "A" "a1"
# [2,] "A" "a10"
# [3,] "A" "a11"
# [4,] "A" "a12"
# [5,] "A" "a2"
# A constituent-frequency table is required for the simulations in ms,
# because ms saves the alleles as numbers such as "1", "2", "3", ...
CFTable = readhelpers.makeCFtable(inData$asoc$dta[[j]])
# print(CFTable)
# aList V2
# Var1 "A" "1"
# Var1 "A" "2"
# Var1 "A" "3"
# Simulate gene trees
simTrees = ms.exec(CFTable, inData$stre$dta[[i]], n_tips,
inData$pldy$dta[j], prmFHndl)
class(simTrees) = "multiPhylo"
## Possible improvement in TFL: why not "length(simTrees)" instead of
## "as.numeric(nReps)"
simReps = matrix(nrow=as.numeric(nReps), ncol=1)
##################################################################
# 3.d. Calculation of descript. statistics depend. on MPI status #
##################################################################
if (mpiBool && nproc >= 3) {
# Applying function "corehelpers.metrics" in parallel
# The second element in mpi.parLapply has to be the function that
# the data is to be applied
simReps = Rmpi::mpi.parLapply(simTrees,
corehelpers.metrics,
inData$ptre$dta$tree[[i]],
inData$ptre$dta$names,
inData$stre$dta[[i]],
CFTable,
inData$pldy$dta[j],
descrStats,
singleAllele)
simReps = as.matrix(as.character(simReps))
}
else {
for (k in 1:nReps) {
simReps[k,1] = corehelpers.metrics(simTrees[[k]],
inData$ptre$dta$tree[[i]],
inData$ptre$dta$names,
inData$stre$dta[[i]],
CFTable,
inData$pldy$dta[j],
descrStats,
singleAllele)
}
}
#########################################
# 3.e. Parsing of average metric values #
#########################################
# Extracting the listcolumns of the matrix simReps via GSO.rtnlc
sumD = list()
for (s in descrStats) {
sumD[[s]] = Mode(rtnlc(simReps, which(s==descrStats)))
}
sumD = unlist(sumD)
names(sumD) = NULL
# Save the replicate values to variable "post_pred_dist"
post_pred_dist[i,j] = toString(sumD)
#############################################
# 3.f. Preparing simulated trees for saving #
#############################################
# Loop over simulated replicates
## Possible improvement: Use apply function instead of loop
# Generate set of print-ready simtrees
simTrees_wLabels = list()
for (k in 1:nReps) {
# LEGA!CY: # Generating a simTree version where tips have been replaced with the correct allele names
# LEGA!CY: assoc_ids = unlist(lapply(inData$asoc$dta[[j]][,2], substring, first=2))
# LEGA!CY: # TFL removes leading zeros
# LEGA!CY: assoc_ids = gsub("^[0]+", "", assoc_ids)
# LEGA!CY: # TFL removes all non-numeric characters
# LEGA!CY: oldLbls = gsub("[^0-9]", "", simTrees[[k]]$tip.label)
# LEGA!CY: # DEBUGLINES:
# LEGA!CY: #cat("\nassoc_ids\n"); print(assoc_ids)
# LEGA!CY: #cat("\nsimTrees[[k]]$tip.label\n"); print(simTrees[[k]]$tip.label)
# LEGA!CY: #cat("\noldLbls\n"); print(oldLbls)
# LEGA!CY: match_indcs = match(oldLbls, assoc_ids)
# LEGA!CY: # Error handling
# LEGA!CY: if (NA %in% match_indcs | "NA" %in% match_indcs) {
# LEGA!CY: stop(cat("\nERROR: Cannot label the terminals of the posterior predictive trees correctly.\n"))
# LEGA!CY: }
# LEGA!CY: nwLbls = inData$asoc$dta[[j]][,2][match_indcs]
# LEGA!CY: simTrees_wLabels[[k]] = simTrees[[k]]
# LEGA!CY: simTrees_wLabels[[k]]$tip.label = mapply(toString, nwLbls)
simLbls = gsub("[^0-9]", "", simTrees[[k]]$tip.label)
nwLbls = inData$asoc$dta[[j]][as.integer(simLbls),2]
simTrees_wLabels[[k]] = simTrees[[k]]
simTrees_wLabels[[k]]$tip.label = mapply(toString, nwLbls)
class(simTrees_wLabels) = "multiPhylo"
}
simTrees_across_sTrees[[paste("sTree_", i, sep="")]] = simTrees_wLabels
# End of looping through sTrees
}
simTrees_across_loci[[inData$loci$dta[[j]]]] = simTrees_across_sTrees
loghelpers.prntmngr("\n", nwlFlg=F)
# End of looping through loci
}
##################
# 3.g. Close MPI #
##################
if (mpiBool && nproc >= 3) {
# Closing slaves
loghelpers.prntmngr(paste("\n", "Close parallelization", "\n",
sep=""))
Rmpi::mpi.close.Rslaves(dellog=T)
}
#############################
# 3.g. Save simulated trees #
#############################
outD$simTrees = simTrees_across_loci
###########################
# 3.h. Assembling results #
###########################
for (s in descrStats) {
# Populating list "outD" with the simulated results
# Both "as.matrix" and "as.data.frame" are critical below
outD[[s]]$post_pred_dist$unsort = as.matrix(as.data.frame(rtnlc(post_pred_dist, which(s==descrStats))))
# Assigning colnames
colnames(outD[[s]]$post_pred_dist$unsort) = inData$loci$dta
# Generating a sorted result clone
outD[[s]]$post_pred_dist$sorted = apply(outD[[s]]$post_pred_dist$unsort, MARGIN=2, sort)
}
#####################################
# 4. Calculating metric differences #
#####################################
loghelpers.prntmngr(paste("Calculating differences btw. the",
"posterior and the posterior predictive",
"distribution"))
for (s in descrStats) {
# Populating list "outD" with the differences
if (srtBool) {
outD[[s]]$dif = statshelpers.diffrnce(outD[[s]]$post_dist$sorted,
outD[[s]]$post_pred_dist$sorted)
# Remove the outD set not chosen
outD[[s]]$post_dist$unsort = NULL
outD[[s]]$post_pred_dist$unsort = NULL
}
else {
outD[[s]]$dif = statshelpers.diffrnce(outD[[s]]$post_dist$unsort,
outD[[s]]$post_pred_dist$unsort)
# Remove the outD set not chosen
outD[[s]]$post_dist$sorted = NULL
outD[[s]]$post_pred_dist$sorted = NULL
}
}
return(outD)
}
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