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GenVarComparison.R
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GenVarComparison.R
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# This script will explore the difference in genetic variance calculated
# on a loci basis versus as the summed allele values for asexual scenarios
setwd("~/Desktop/GitHubRepos/DispersalEvolution/")
SummedResults <- read.csv("SimsWithSummedResults.csv")
# Make a two panel graph with points for each loci number for Haploid (right panel)
# and obligately selfing (left panel)
# Set up the same colors and labels as the main manuscript figures
library(RColorBrewer)
ScenCols <- brewer.pal(n = 5, name = "Dark2")
Scenarios <- c("Asexual", "Obligate selfing", "Partial selfing", "Obligate outcrossing", "Sexual (dioecious)")
HapVals <- c(TRUE, FALSE, FALSE, FALSE, FALSE)
monoVals <- c(TRUE, TRUE, TRUE, TRUE, FALSE)
omegaVals <- c(0, 1, 0.5, 0, 0)
Lseq <-c(1,2,4,8,16,32)
LseqLocs <- c(0, 0.2, 0.4, 0.6, 0.8, 1)
offset <- 0.025
xLocs <- matrix(NA, nrow = 4, ncol = 6)
xLocs[1,] <- LseqLocs - 2*offset
xLocs[2,] <- LseqLocs - offset
xLocs[3,] <- LseqLocs + offset
xLocs[4,] <- LseqLocs + 2*offset
ScenPch <- c(21, 22, 23, 24, 25)
OldExpData <- vector(mode = "list", length = 2)
NewExpData <- vector(mode = "list", length = 2)
OldShiftData <- vector(mode = "list", length = 2)
NewShiftData <- vector(mode = "list", length = 2)
for(i in 1:2){
ExpOldMeanGen <- rep(NA, 6)
ExpOldLwrGen <- rep(NA, 6)
ExpOldUprGen <- rep(NA, 6)
ExpNewMeanGen <- rep(NA, 6)
ExpNewLwrGen <- rep(NA, 6)
ExpNewUprGen <- rep(NA, 6)
ShiftOldMeanGen <- rep(NA, 6)
ShiftOldLwrGen <- rep(NA, 6)
ShiftOldUprGen <- rep(NA, 6)
ShiftNewMeanGen <- rep(NA, 6)
ShiftNewLwrGen <- rep(NA, 6)
ShiftNewUprGen <- rep(NA, 6)
for(l in 1:6){
CurRows <- which(SummedResults$L == Lseq[l] & SummedResults$Haploid == HapVals[i] & SummedResults$monoecious == monoVals[i] & SummedResults$omega == omegaVals[i])
CurData <- SummedResults[CurRows,]
OldExp <- c(CurData$DeltaGenExp_1, CurData$DeltaGenExp_2)
OldShift <- CurData$DeltaGenShift
NewExp <- c(CurData$SummedDeltaGenExp_1, CurData$SummedDeltaGenExp_2)
NewShift <- CurData$SummedDeltaGenShift
ExpOldMeanGen[l] <- mean(OldExp)
ExpOldLwrGen[l] <- quantile(OldExp, probs = 0.25)
ExpOldUprGen[l] <- quantile(OldExp, probs = 0.75)
ExpNewMeanGen[l] <- mean(NewExp, na.rm = TRUE)
ExpNewLwrGen[l] <- quantile(NewExp, probs = 0.25, na.rm = TRUE)
ExpNewUprGen[l] <- quantile(NewExp, probs = 0.75, na.rm = TRUE)
ShiftOldMeanGen[l] <- mean(OldShift, na.rm = TRUE)
ShiftOldLwrGen[l] <- quantile(OldShift, probs = 0.25, na.rm = TRUE)
ShiftOldUprGen[l] <- quantile(OldShift, probs = 0.75, na.rm = TRUE)
ShiftNewMeanGen[l] <- mean(NewShift, na.rm = TRUE)
ShiftNewLwrGen[l] <- quantile(NewShift, probs = 0.25, na.rm = TRUE)
ShiftNewUprGen[l] <- quantile(NewShift, probs = 0.75, na.rm = TRUE)
}
OldExpData[[i]] <- data.frame(L = Lseq, MeanGen = ExpOldMeanGen,
LwrGen = ExpOldLwrGen, UprGen = ExpOldUprGen)
NewExpData[[i]] <- data.frame(L = Lseq, MeanGen = ExpNewMeanGen,
LwrGen = ExpNewLwrGen, UprGen = ExpNewUprGen)
OldShiftData[[i]] <- data.frame(L = Lseq, MeanGen = ShiftOldMeanGen,
LwrGen = ShiftOldLwrGen, UprGen = ShiftOldUprGen)
NewShiftData[[i]] <- data.frame(L = Lseq, MeanGen = ShiftNewMeanGen,
LwrGen = ShiftNewLwrGen, UprGen = ShiftNewUprGen)
}
pdf(file = "ResultFigures/GenVarExpCompare.pdf", width = 5, height = 4, onefile = FALSE, paper = "special", useDingbats = FALSE)
plot(NA, NA, xlim = c(-0.05, 1.05), ylim = c(-3, 0), main = "", ylab = "Genetic variance",
xlab = "Number of loci", las = 1, xaxt = "n")
axis(side = 1, at = LseqLocs, labels = Lseq)
axis(side = 2, at = seq(-3, 0, by = 0.25), tcl = -0.25, labels = FALSE)
OldLocs <- c(1,3)
NewLocs <- c(2,4)
for(i in 1:2){
# Old
if(i == 1){
points(x = xLocs[OldLocs[i],2:6], y = OldExpData[[i]]$MeanGen[2:6], pch = ScenPch[i], col = ScenCols[i], bg = ScenCols[i])
segments(x0 = xLocs[OldLocs[i],2:6], y0 = OldExpData[[i]]$LwrGen[2:6], x1 = xLocs[OldLocs[i],2:6], y1 = OldExpData[[i]]$UprGen[2:6],
col = ScenCols[i], lty = 1)
}else{
points(x = xLocs[OldLocs[i],], y = OldExpData[[i]]$MeanGen, pch = ScenPch[i], col = ScenCols[i], bg = ScenCols[i])
segments(x0 = xLocs[OldLocs[i],], y0 = OldExpData[[i]]$LwrGen, x1 = xLocs[OldLocs[i],], y1 = OldExpData[[i]]$UprGen,
col = ScenCols[i], lty = 1)
}
# New
segments(x0 = xLocs[NewLocs[i],], y0 = NewExpData[[i]]$LwrGen, x1 = xLocs[NewLocs[i],], y1 = NewExpData[[i]]$UprGen,
col = ScenCols[i], lty = 1)
points(x = xLocs[NewLocs[i],], y = NewExpData[[i]]$MeanGen, pch = ScenPch[i], col = ScenCols[i], bg = "white")
}
# Put the legend on the figure
legend("top", legend = c(Scenarios[1:2], "Allele variance", "Genotype variance"),
pch = c(ScenPch[1:2], ScenPch[1], ScenPch[1]),
col = c(ScenCols[1:2], "black", "black"), bty = "n",
pt.bg = c(ScenCols[1:2], "black", "white"), inset = -0.01, ncol = 2)
dev.off()
pdf(file = "ResultFigures/GenVarShiftCompare.pdf", width = 5, height = 4, onefile = FALSE, paper = "special", useDingbats = FALSE)
plot(NA, NA, xlim = c(-0.05, 1.05), ylim = c(-3, 0), main = "", ylab = "Genetic variance",
xlab = "Number of loci", las = 1, xaxt = "n")
axis(side = 1, at = LseqLocs, labels = Lseq)
axis(side = 2, at = seq(-3, 0, by = 0.25), tcl = -0.25, labels = FALSE)
OldLocs <- c(1,3)
NewLocs <- c(2,4)
for(i in 1:2){
# Old
if(i == 1){
points(x = xLocs[OldLocs[i],2:6], y = OldShiftData[[i]]$MeanGen[2:6], pch = ScenPch[i], col = ScenCols[i], bg = ScenCols[i])
segments(x0 = xLocs[OldLocs[i],2:6], y0 = OldShiftData[[i]]$LwrGen[2:6], x1 = xLocs[OldLocs[i],2:6], y1 = OldShiftData[[i]]$UprGen[2:6],
col = ScenCols[i], lty = 1)
}else{
points(x = xLocs[OldLocs[i],], y = OldShiftData[[i]]$MeanGen, pch = ScenPch[i], col = ScenCols[i], bg = ScenCols[i])
segments(x0 = xLocs[OldLocs[i],], y0 = OldShiftData[[i]]$LwrGen, x1 = xLocs[OldLocs[i],], y1 = OldShiftData[[i]]$UprGen,
col = ScenCols[i], lty = 1)
}
# New
segments(x0 = xLocs[NewLocs[i],], y0 = NewShiftData[[i]]$LwrGen, x1 = xLocs[NewLocs[i],], y1 = NewShiftData[[i]]$UprGen,
col = ScenCols[i], lty = 1)
points(x = xLocs[NewLocs[i],], y = NewShiftData[[i]]$MeanGen, pch = ScenPch[i], col = ScenCols[i], bg = "white")
}
# Put the legend on the figure
legend("top", legend = c(Scenarios[1:2], "Allele variance", "Genotype variance"),
pch = c(ScenPch[1:2], ScenPch[1], ScenPch[1]),
col = c(ScenCols[1:2], "black", "black"), bty = "n",
pt.bg = c(ScenCols[1:2], "black", "white"), inset = -0.01, ncol = 2)
dev.off()
######################### Now do the same thing but for the initial conditions
setwd("~/Desktop/GitHubRepos/DispersalEvolution/")
SummedResults <- read.csv("SimsWithSummedInitVals.csv")
# Make a two panel graph with points for each loci number for Haploid (right panel)
# and obligately selfing (left panel)
# Set up the same colors and labels as the main manuscript figures
library(RColorBrewer)
ScenCols <- brewer.pal(n = 5, name = "Dark2")
Scenarios <- c("Asexual", "Obligate selfing", "Partial selfing", "Obligate outcrossing", "Sexual (dioecious)")
HapVals <- c(TRUE, FALSE, FALSE, FALSE, FALSE)
monoVals <- c(TRUE, TRUE, TRUE, TRUE, FALSE)
omegaVals <- c(0, 1, 0.5, 0, 0)
Lseq <-c(1,2,4,8,16,32)
LseqLocs <- c(0, 0.2, 0.4, 0.6, 0.8, 1)
offset <- 0.025
xLocs <- matrix(NA, nrow = 4, ncol = 6)
xLocs[1,] <- LseqLocs - 2*offset
xLocs[2,] <- LseqLocs - offset
xLocs[3,] <- LseqLocs + offset
xLocs[4,] <- LseqLocs + 2*offset
ScenPch <- c(21, 22, 23, 24, 25)
OldData <- vector(mode = "list", length = 2)
NewData <- vector(mode = "list", length = 2)
for(i in 1:2){
OldMeanGen <- rep(NA, 6)
OldLwrGen <- rep(NA, 6)
OldUprGen <- rep(NA, 6)
NewMeanGen <- rep(NA, 6)
NewLwrGen <- rep(NA, 6)
NewUprGen <- rep(NA, 6)
for(l in 1:6){
CurRows <- which(SummedResults$L == Lseq[l] & SummedResults$Haploid == HapVals[i] & SummedResults$monoecious == monoVals[i] & SummedResults$omega == omegaVals[i])
CurData <- SummedResults[CurRows,]
OldVar <- CurData$GenVar
NewVar <- CurData$SummedGenVar
OldMeanGen[l] <- mean(OldVar)
OldLwrGen[l] <- quantile(OldVar, probs = 0.25)
OldUprGen[l] <- quantile(OldVar, probs = 0.75)
NewMeanGen[l] <- mean(NewVar, na.rm = TRUE)
NewLwrGen[l] <- quantile(NewVar, probs = 0.25, na.rm = TRUE)
NewUprGen[l] <- quantile(NewVar, probs = 0.75, na.rm = TRUE)
}
OldData[[i]] <- data.frame(L = Lseq, MeanGen = OldMeanGen,
LwrGen = OldLwrGen, UprGen = OldUprGen)
NewData[[i]] <- data.frame(L = Lseq, MeanGen = NewMeanGen,
LwrGen = NewLwrGen, UprGen = NewUprGen)
}
pdf(file = "ResultFigures/InitGenVarExpCompare.pdf", width = 5, height = 4, onefile = FALSE, paper = "special", useDingbats = FALSE)
plot(NA, NA, xlim = c(-0.05, 1.05), ylim = c(0, 3.5), main = "", ylab = "Genetic variance",
xlab = "Number of loci", las = 1, xaxt = "n")
axis(side = 1, at = LseqLocs, labels = Lseq)
axis(side = 2, at = seq(0, 3.5, by = 0.125), tcl = -0.25, labels = FALSE)
OldLocs <- c(1,3)
NewLocs <- c(2,4)
for(i in 1:2){
# Old
if(i == 1){
points(x = xLocs[OldLocs[i],2:6], y = OldData[[i]]$MeanGen[2:6], pch = ScenPch[i], col = ScenCols[i], bg = ScenCols[i])
segments(x0 = xLocs[OldLocs[i],2:6], y0 = OldData[[i]]$LwrGen[2:6], x1 = xLocs[OldLocs[i],2:6], y1 = OldData[[i]]$UprGen[2:6],
col = ScenCols[i], lty = 1)
}else{
points(x = xLocs[OldLocs[i],], y = OldData[[i]]$MeanGen, pch = ScenPch[i], col = ScenCols[i], bg = ScenCols[i])
segments(x0 = xLocs[OldLocs[i],], y0 = OldData[[i]]$LwrGen, x1 = xLocs[OldLocs[i],], y1 = OldData[[i]]$UprGen,
col = ScenCols[i], lty = 1)
}
# New
segments(x0 = xLocs[NewLocs[i],], y0 = NewData[[i]]$LwrGen, x1 = xLocs[NewLocs[i],], y1 = NewData[[i]]$UprGen,
col = ScenCols[i], lty = 1)
points(x = xLocs[NewLocs[i],], y = NewData[[i]]$MeanGen, pch = ScenPch[i], col = ScenCols[i], bg = "white")
}
# Put the legend on the figure
legend("top", legend = c(Scenarios[1:2], "Allele variance", "Genotype variance"),
pch = c(ScenPch[1:2], ScenPch[1], ScenPch[1]),
col = c(ScenCols[1:2], "black", "black"), bty = "n",
pt.bg = c(ScenCols[1:2], "black", "white"), inset = -0.01, ncol = 2)
dev.off()