####################################################
###File S2: Model Analyses for Duveau et al. 2014###
####################################################
#1. Packages
#2. Data
#3. Functions
#4. Power Analysis
#5. Fisher's Exact Test vs G-test
#6. False Positive Rate
#7. Deterministic Allele Frequencies

#################
###1. Packages###
#################
library(nleqslv)
library(Rsolnp)
library(RColorBrewer)

#############
###2. Data###
#############
#YFP expression from Metzger and Yuan et al. 2014 (in prep)
#Used to convert between expression as percentage and expression as standard deviation
WT <- c(0.853788726,0.020919973)
names(WT) <- c("YFP","sd")
NEG <- c(0.576945829,0.063310097)
names(NEG) <- c("YFP","sd")

##################
###3. Functions###
##################
#Determine phenotypic cutoff numerically
quantile.minimize <- function(QUANTILE, CUTOFF = CUTOFF, MU = MU, SIGMA = SIGMA, MUTANT.FREQ = MUTANT.FREQ) {
	((1-MUTANT.FREQ)*pnorm(qnorm(QUANTILE,MU,SIGMA),0,1) + MUTANT.FREQ*QUANTILE - CUTOFF)^2
}

#Determine frequences from relative abundances
overlap <- function(THRESHOLD = z.score.cutoff, MU = mutant.mean, SIGMA = mutant.variance, LOWER.TAIL = which.tail, MUTANT.FREQ = mutant.frequency.overall) {
	OVERLAP <- (MUTANT.FREQ*pnorm(THRESHOLD,MU,SIGMA,LOWER.TAIL))/((1-MUTANT.FREQ)*pnorm(THRESHOLD,0,1,LOWER.TAIL))
	FREQUENCY <- OVERLAP/(1 + OVERLAP)
	return(FREQUENCY)
}

#Perform power analysis
Power <- function(ALPHA = ALPHA, BETA = BETA, CUTOFF = CUTOFF, MU = MU, SIGMA = SIGMA, POPULATION = POPULATION, SELECTION = SELECTION, GENERATION = GENERATION, TEST = TEST) {
	I <- 1:10000
	POWER       <- vector(mode = "numeric",length = length(I))
	THETA.LOW   <- vector(mode = "numeric",length = length(I))
	THETA.HIGH  <- vector(mode = "numeric",length = length(I))
	MUTANT.HIGH <- vector(mode = "numeric",length = length(I))
	WT.HIGH     <- vector(mode = "numeric",length = length(I))
	MUTANT.LOW  <- vector(mode = "numeric",length = length(I))
	WT.LOW      <- vector(mode = "numeric",length = length(I))

	QUANTILES.START.LOW  <- CUTOFF
	QUANTILES.START.HIGH <- 1-CUTOFF
	
	MUTANT.FITNESS <- (1-SELECTION)^(GENERATION/2)
	MUTANT.FREQ <- (0.5*MUTANT.FITNESS)/(0.5*MUTANT.FITNESS + 0.5*1)

	QUANTILES.LOW  <- solnp(QUANTILES.START.LOW,  fun = quantile.minimize, LB = 10^-20, UB = 1 - 10^-20, MU = MU, SIGMA = SIGMA, MUTANT.FREQ = MUTANT.FREQ, CUTOFF = CUTOFF, control = list(trace = 0))$pars
	QUANTILES.HIGH <- solnp(QUANTILES.START.HIGH, fun = quantile.minimize, LB = 10^-20, UB = 1 - 10^-20, MU = MU, SIGMA = SIGMA, MUTANT.FREQ = MUTANT.FREQ, CUTOFF = 1 - CUTOFF, control = list(trace = 0))$pars

	THRESHOLD.LOW  <- qnorm(QUANTILES.LOW,  MU, SIGMA)
	THRESHOLD.HIGH <- qnorm(QUANTILES.HIGH, MU, SIGMA)
		
	LOW.MUTANT.FREQ.EXPECTED   <- overlap(THRESHOLD = THRESHOLD.LOW,  MU = MU, SIGMA = SIGMA, MUTANT.FREQ = MUTANT.FREQ, LOWER.TAIL = TRUE)
	HIGH.MUTANT.FREQ.EXPECTED  <- overlap(THRESHOLD = THRESHOLD.HIGH, MU = MU, SIGMA = SIGMA, MUTANT.FREQ = MUTANT.FREQ, LOWER.TAIL = FALSE)

	LOW.MUTANT.FREQ.GROWTH  <- (LOW.MUTANT.FREQ.EXPECTED*MUTANT.FITNESS)/(LOW.MUTANT.FREQ.EXPECTED*MUTANT.FITNESS + (1-LOW.MUTANT.FREQ.EXPECTED)*1)
	HIGH.MUTANT.FREQ.GROWTH <- (HIGH.MUTANT.FREQ.EXPECTED*MUTANT.FITNESS)/(HIGH.MUTANT.FREQ.EXPECTED*MUTANT.FITNESS + (1-HIGH.MUTANT.FREQ.EXPECTED)*1)

	POP.SIZE <- POPULATION*CUTOFF

	for(i in I) {
		LOW.MUTANT.FREQ  <- rbinom(1, POP.SIZE, LOW.MUTANT.FREQ.GROWTH)/POP.SIZE
		HIGH.MUTANT.FREQ <- rbinom(1, POP.SIZE, HIGH.MUTANT.FREQ.GROWTH)/POP.SIZE

		LOW.COVERAGE  <- rnbinom(1, size = ALPHA, prob = BETA/(1 + BETA))
		HIGH.COVERAGE <- rnbinom(1, size = ALPHA, prob = BETA/(1 + BETA))

		MUTANT.LOW[i]  <- rbinom(1,LOW.COVERAGE,LOW.MUTANT.FREQ)
		WT.LOW[i]	  <- LOW.COVERAGE - MUTANT.LOW[i]
		MUTANT.HIGH[i] <- rbinom(1,HIGH.COVERAGE,HIGH.MUTANT.FREQ)
		WT.HIGH[i]     <- HIGH.COVERAGE - MUTANT.HIGH[i]
	
		THETA.LOW[i]  <- MUTANT.LOW[i]/(WT.LOW[i] + MUTANT.LOW[i] + 1)
		THETA.HIGH[i] <- MUTANT.HIGH[i]/(WT.HIGH[i] + MUTANT.HIGH[i] + 1)

		matrix <- matrix(c(MUTANT.LOW[i],WT.LOW[i],MUTANT.HIGH[i],WT.HIGH[i]), nrow = 2)
		
		if(TEST == "F") {
			POWER[i] <- fisher.test(matrix, alternative = "less")$p.value < 0.001
		}else if(TEST == "G") {
			matrix <- matrix + 0.5
			if(sum(matrix) == 0) {
				POWER[i] <- 1
			}else {
				row.sum <- apply(matrix,1,sum)
				col.sum <- apply(matrix,2,sum)
				n = sum(matrix)
				EXPECTED <- (row.sum %o% col.sum)/n
				G.STAT <- 2*sum(matrix*log(matrix/EXPECTED))
				POWER[i] <- pchisq(G.STAT, df=1, lower.tail=FALSE) < 0.001
			}
		}
	}
	return(list(Power = POWER, Mutant.High = MUTANT.HIGH, WT.High = WT.HIGH, Mutant.Low = MUTANT.LOW, WT.Low = WT.LOW))
}

#Transform data in SD to data in percentage
convert.mu.to.percent <- function(MU) {
	PERCENT <- (MU*WT[2] + WT[1] - NEG[1])/(WT[1] - NEG[1]) - 1
	return(PERCENT)
}

#Transform data in percentage to data in SD
convert.percent.to.mu <- function(PERCENT) {
	MU <- ((PERCENT + 1)*(WT[1] - NEG[1]) - WT[1] + NEG[1])/(WT[2])
	return(MU)
}

#######################
###4. Power Anlaysis###
#######################
#Parameters and values tested
ALPHA <- 80
MU.test <- c(convert.percent.to.mu(seq(0,.15,.01)),convert.percent.to.mu(c(0.2,0.25)))
SIGMA.test <- c(0.25,0.5,0.75,1,1.25,1.5,2,3)
CUTOFF.test <- c(0.5,0.1,0.05,0.01)
BETA.test  <- c(0.16,0.32,0.8,1.6,3.2,8)
POPULATION.test <- c(10^7,10^5,10^3,10^2)
SELECTION.test <- c(-.01,0,0.01,0.03,0.07,0.1,0.15,0.2)
GENERATION.test <- c(10,20,50)
COLOR <- rainbow(8)

###PLOT I
#X-axis MU
#Y-axis POWER
#Lines  SIGMA
#Get set of parameters to test
try <- expand.grid(MU.test,SIGMA.test,CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
#Determine power for each set of parameters
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

#Pull out subsets that will correspond to lines
MU.SIGMA <- total
SIGMA.LEVEL.1 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[1])
SIGMA.LEVEL.2 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[2])
SIGMA.LEVEL.3 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[3])
SIGMA.LEVEL.4 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[4])
SIGMA.LEVEL.5 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[5])
SIGMA.LEVEL.6 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[6])
SIGMA.LEVEL.7 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[7])
SIGMA.LEVEL.8 <- subset(MU.SIGMA, MU.SIGMA$SIGMA == SIGMA.test[8])

#Plot results
plot(  convert.mu.to.percent(SIGMA.LEVEL.1$MU),SIGMA.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, 
	ylim = c(0,1), xlab = "Mean Effect (%)", ylab = "Power")
points(convert.mu.to.percent(SIGMA.LEVEL.2$MU),SIGMA.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(convert.mu.to.percent(SIGMA.LEVEL.3$MU),SIGMA.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(convert.mu.to.percent(SIGMA.LEVEL.4$MU),SIGMA.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(convert.mu.to.percent(SIGMA.LEVEL.5$MU),SIGMA.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(convert.mu.to.percent(SIGMA.LEVEL.6$MU),SIGMA.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
points(convert.mu.to.percent(SIGMA.LEVEL.7$MU),SIGMA.LEVEL.7$POWER, typ = "l", col = COLOR[7], lwd = 2)
points(convert.mu.to.percent(SIGMA.LEVEL.8$MU),SIGMA.LEVEL.8$POWER, typ = "l", col = COLOR[8], lwd = 2)
abline(h = 0.9, lty = 2)
legend(0.2, 0.4, legend = SIGMA.test, fill = COLOR, title = "Mutant Variance")
rect(xleft = 0.01, xright=0.11, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT II
#X-axis MU
#Y-axis POWER
#Lines  POPULATION

try <- expand.grid(MU.test,SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test,SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

MU.POPULATION <- total
POPULATION.LEVEL.1 <- subset(MU.POPULATION, MU.POPULATION$POPULATION == POPULATION.test[1])
POPULATION.LEVEL.2 <- subset(MU.POPULATION, MU.POPULATION$POPULATION == POPULATION.test[2])
POPULATION.LEVEL.3 <- subset(MU.POPULATION, MU.POPULATION$POPULATION == POPULATION.test[3])
POPULATION.LEVEL.4 <- subset(MU.POPULATION, MU.POPULATION$POPULATION == POPULATION.test[4])

plot(  convert.mu.to.percent(POPULATION.LEVEL.1$MU),POPULATION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, 
	ylim = c(0,1), xlab = "Mean Effect", ylab = "Power")
points(convert.mu.to.percent(POPULATION.LEVEL.2$MU),POPULATION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(convert.mu.to.percent(POPULATION.LEVEL.3$MU),POPULATION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(convert.mu.to.percent(POPULATION.LEVEL.4$MU),POPULATION.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
abline(h = 0.9, lty = 2)
legend(0.2, 0.4, legend = POPULATION.test, fill = COLOR, title = "Population Size")
rect(xleft = 0.01, xright=0.11, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT III
#X-axis MU
#Y-axis POWER
#Lines  COVERAGE

try <- expand.grid(MU.test,SIGMA.test[4],CUTOFF.test[3],BETA.test,POPULATION.test[1],SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

MU.BETA <- total
BETA.LEVEL.1 <- subset(MU.BETA, MU.BETA$BETA == BETA.test[1])
BETA.LEVEL.2 <- subset(MU.BETA, MU.BETA$BETA == BETA.test[2])
BETA.LEVEL.3 <- subset(MU.BETA, MU.BETA$BETA == BETA.test[3])
BETA.LEVEL.4 <- subset(MU.BETA, MU.BETA$BETA == BETA.test[4])
BETA.LEVEL.5 <- subset(MU.BETA, MU.BETA$BETA == BETA.test[5])
BETA.LEVEL.6 <- subset(MU.BETA, MU.BETA$BETA == BETA.test[6])

plot(  convert.mu.to.percent(BETA.LEVEL.1$MU),BETA.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2,
	ylim = c(0,1), xlab = "Mutant Effect (%)", ylab = "Power" )
points(convert.mu.to.percent(BETA.LEVEL.2$MU),BETA.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(convert.mu.to.percent(BETA.LEVEL.3$MU),BETA.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(convert.mu.to.percent(BETA.LEVEL.4$MU),BETA.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(convert.mu.to.percent(BETA.LEVEL.5$MU),BETA.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(convert.mu.to.percent(BETA.LEVEL.6$MU),BETA.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
abline(h = 0.9, lty = 2)
legend(.2, 0.4, legend = ALPHA/BETA.test, fill = COLOR, title = "Average Coverage")
rect(xleft = 0.01, xright=0.11, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT IV
#X-axis MU
#Y-axis POWER
#Lines  CUTOFF

try <- expand.grid(MU.test,SIGMA.test[4],CUTOFF.test,BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

MU.CUTOFF <- total
CUTOFF.LEVEL.1 <- subset(MU.CUTOFF, MU.CUTOFF$CUTOFF == CUTOFF.test[1])
CUTOFF.LEVEL.2 <- subset(MU.CUTOFF, MU.CUTOFF$CUTOFF == CUTOFF.test[2])
CUTOFF.LEVEL.3 <- subset(MU.CUTOFF, MU.CUTOFF$CUTOFF == CUTOFF.test[3])
CUTOFF.LEVEL.4 <- subset(MU.CUTOFF, MU.CUTOFF$CUTOFF == CUTOFF.test[4])

plot(  convert.mu.to.percent(CUTOFF.LEVEL.1$MU),CUTOFF.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, 
	ylim = c(0,1), xlab = "Mean Effect", ylab = "Power")
points(convert.mu.to.percent(CUTOFF.LEVEL.2$MU),CUTOFF.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(convert.mu.to.percent(CUTOFF.LEVEL.3$MU),CUTOFF.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(convert.mu.to.percent(CUTOFF.LEVEL.4$MU),CUTOFF.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
abline(h = 0.9, lty = 2)
legend(0.2, 0.4, legend = CUTOFF.test, fill = COLOR, title = "Cutoff Percent")
rect(xleft = 0.01, xright=0.11, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT V
#X-axis MU
#Y-axis POWER
#Lines  SELECTION

try <- expand.grid(MU.test,SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

MU.SELECTION <- total
SELECTION.LEVEL.1 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[1])
SELECTION.LEVEL.2 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[2])
SELECTION.LEVEL.3 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[3])
SELECTION.LEVEL.4 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[4])
SELECTION.LEVEL.5 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[5])
SELECTION.LEVEL.6 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[6])
SELECTION.LEVEL.7 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[7])
SELECTION.LEVEL.8 <- subset(MU.SELECTION, MU.SELECTION$SELECTION == SELECTION.test[8])

plot(  convert.mu.to.percent(SELECTION.LEVEL.1$MU),SELECTION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, 
	ylim = c(0,1), xlab = "Mean Effect", ylab = "Power")
points(convert.mu.to.percent(SELECTION.LEVEL.2$MU),SELECTION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(convert.mu.to.percent(SELECTION.LEVEL.3$MU),SELECTION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(convert.mu.to.percent(SELECTION.LEVEL.4$MU),SELECTION.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(convert.mu.to.percent(SELECTION.LEVEL.5$MU),SELECTION.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(convert.mu.to.percent(SELECTION.LEVEL.6$MU),SELECTION.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
points(convert.mu.to.percent(SELECTION.LEVEL.7$MU),SELECTION.LEVEL.7$POWER, typ = "l", col = COLOR[7], lwd = 2)
points(convert.mu.to.percent(SELECTION.LEVEL.8$MU),SELECTION.LEVEL.8$POWER, typ = "l", col = COLOR[8], lwd = 2)
abline(h = 0.9, lty = 2)
legend(0.2, 0.4, legend = SELECTION.test, fill = COLOR, title = "Selection Coefficient")
rect(xleft = 0.01, xright=0.11, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT VI
#X-axis MU
#Y-axis POWER
#Lines  GENERATIONS

try <- expand.grid(MU.test,SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test)
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

MU.GENERATION <- total
GENERATION.LEVEL.1 <- subset(MU.GENERATION, MU.GENERATION$GENERATION == GENERATION.test[1])
GENERATION.LEVEL.2 <- subset(MU.GENERATION, MU.GENERATION$GENERATION == GENERATION.test[2])
GENERATION.LEVEL.3 <- subset(MU.GENERATION, MU.GENERATION$GENERATION == GENERATION.test[3])

plot(  convert.mu.to.percent(GENERATION.LEVEL.1$MU),GENERATION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, 
	ylim = c(0,1), xlab = "Mean Effect", ylab = "Power")
points(convert.mu.to.percent(GENERATION.LEVEL.2$MU),GENERATION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(convert.mu.to.percent(GENERATION.LEVEL.3$MU),GENERATION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
abline(h = 0.9, lty = 2)
legend(0.2, 0.4, legend = GENERATION.test, fill = COLOR, title = "Generations")
rect(xleft = 0.01, xright=0.11, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT VII
#X-axis SIGMA
#Y-axis POWER
#Lines  MU

try <- expand.grid(MU.test,SIGMA.test,CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SIGMA.MU <- total
MU.LEVEL.1 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[1])
MU.LEVEL.2 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[2])
MU.LEVEL.3 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[3])
MU.LEVEL.4 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[4])
MU.LEVEL.5 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[5])
MU.LEVEL.6 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[6])
MU.LEVEL.7 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[7])
MU.LEVEL.8 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[8])
MU.LEVEL.9 <- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[9])
MU.LEVEL.10<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[10])
MU.LEVEL.11<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[11])
MU.LEVEL.12<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[12])
MU.LEVEL.13<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[13])
MU.LEVEL.14<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[14])
MU.LEVEL.15<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[15])
MU.LEVEL.16<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[16])
MU.LEVEL.17<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[17])
MU.LEVEL.18<- subset(SIGMA.MU, SIGMA.MU$MU == MU.test[18])

plot(  MU.LEVEL.1$SIGMA, MU.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, lty = 1, xlim = c(0.25,4), 
	ylim = c(0,1), xlab = "Mutant Variance", ylab = "Power")
points(MU.LEVEL.2$SIGMA, MU.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2, lty = 1)
points(MU.LEVEL.3$SIGMA, MU.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2, lty = 1)
points(MU.LEVEL.4$SIGMA, MU.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2, lty = 1)
points(MU.LEVEL.5$SIGMA, MU.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2, lty = 1)
points(MU.LEVEL.6$SIGMA, MU.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2, lty = 1)
points(MU.LEVEL.7$SIGMA, MU.LEVEL.7$POWER, typ = "l", col = COLOR[7], lwd = 2, lty = 1)
points(MU.LEVEL.8$SIGMA, MU.LEVEL.8$POWER, typ = "l", col = COLOR[8], lwd = 2, lty = 1)
points(MU.LEVEL.9$SIGMA, MU.LEVEL.9$POWER, typ = "l", col = COLOR[1], lwd = 2, lty = 2)
points(MU.LEVEL.10$SIGMA, MU.LEVEL.10$POWER, typ = "l", col = COLOR[2], lwd = 2, lty = 2)
points(MU.LEVEL.11$SIGMA, MU.LEVEL.11$POWER, typ = "l", col = COLOR[3], lwd = 2, lty = 2)
points(MU.LEVEL.12$SIGMA, MU.LEVEL.12$POWER, typ = "l", col = COLOR[4], lwd = 2, lty = 2)
points(MU.LEVEL.13$SIGMA, MU.LEVEL.13$POWER, typ = "l", col = COLOR[5], lwd = 2, lty = 2)
points(MU.LEVEL.14$SIGMA, MU.LEVEL.14$POWER, typ = "l", col = COLOR[6], lwd = 2, lty = 2)
points(MU.LEVEL.15$SIGMA, MU.LEVEL.15$POWER, typ = "l", col = COLOR[7], lwd = 2, lty = 2)
points(MU.LEVEL.16$SIGMA, MU.LEVEL.16$POWER, typ = "l", col = COLOR[8], lwd = 2, lty = 2)
points(MU.LEVEL.17$SIGMA, MU.LEVEL.17$POWER, typ = "l", col = COLOR[1], lwd = 2, lty = 3)
points(MU.LEVEL.18$SIGMA, MU.LEVEL.18$POWER, typ = "l", col = COLOR[2], lwd = 2, lty = 3)
abline(h = 0.9, lty = 2)
legend(3.2, 1, legend = round(convert.mu.to.percent(MU.test),3), fill = COLOR, title = "Main Effect")
rect(xleft = .75, xright=1.5, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT VIII
#X-axis SIGMA
#Y-axis POWER
#Lines  POPULATION

try <- expand.grid(MU.test[6],SIGMA.test,CUTOFF.test[3],BETA.test[3],POPULATION.test,SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SIGMA.POPULATION <- total
POPULATION.LEVEL.1 <- subset(SIGMA.POPULATION, SIGMA.POPULATION$POPULATION == POPULATION.test[1])
POPULATION.LEVEL.2 <- subset(SIGMA.POPULATION, SIGMA.POPULATION$POPULATION == POPULATION.test[2])
POPULATION.LEVEL.3 <- subset(SIGMA.POPULATION, SIGMA.POPULATION$POPULATION == POPULATION.test[3])
POPULATION.LEVEL.4 <- subset(SIGMA.POPULATION, SIGMA.POPULATION$POPULATION == POPULATION.test[4])

plot(  POPULATION.LEVEL.1$SIGMA,POPULATION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim = c(0.25,4), 
	ylim = c(0,1), xlab = "Mutant Variance", ylab = "Power")
points(POPULATION.LEVEL.2$SIGMA,POPULATION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(POPULATION.LEVEL.3$SIGMA,POPULATION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(POPULATION.LEVEL.4$SIGMA,POPULATION.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
abline(h = 0.9, lty = 2)
legend(3.2, 0.8, legend = POPULATION.test, fill = COLOR, title = "Population Size")
rect(xleft = .75, xright=1.5, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT IX
#X-axis SIGMA
#Y-axis POWER
#Lines  COVERAGE

try <- expand.grid(MU.test[6],SIGMA.test,CUTOFF.test[3],BETA.test,POPULATION.test[1],SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SIGMA.BETA <- total
BETA.LEVEL.1 <- subset(SIGMA.BETA, SIGMA.BETA$BETA == BETA.test[1])
BETA.LEVEL.2 <- subset(SIGMA.BETA, SIGMA.BETA$BETA == BETA.test[2])
BETA.LEVEL.3 <- subset(SIGMA.BETA, SIGMA.BETA$BETA == BETA.test[3])
BETA.LEVEL.4 <- subset(SIGMA.BETA, SIGMA.BETA$BETA == BETA.test[4])
BETA.LEVEL.5 <- subset(SIGMA.BETA, SIGMA.BETA$BETA == BETA.test[5])
BETA.LEVEL.6 <- subset(SIGMA.BETA, SIGMA.BETA$BETA == BETA.test[6])

plot(  BETA.LEVEL.1$SIGMA,BETA.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim = c(0.25,4),
	ylim = c(0,1), xlab = "Mutant Variance", ylab = "Power" )
points(BETA.LEVEL.2$SIGMA,BETA.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(BETA.LEVEL.3$SIGMA,BETA.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(BETA.LEVEL.4$SIGMA,BETA.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(BETA.LEVEL.5$SIGMA,BETA.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(BETA.LEVEL.6$SIGMA,BETA.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
abline(h = 0.9, lty = 2)
legend(3.2, 0.8, legend = ALPHA/BETA.test, fill = COLOR, title = "Average Coverage")
rect(xleft = .75, xright=1.5, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT X
#X-axis SIGMA
#Y-axis POWER
#Lines  CUTOFF

try <- expand.grid(MU.test[6],SIGMA.test,CUTOFF.test,BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SIGMA.CUTOFF <- total
CUTOFF.LEVEL.1 <- subset(SIGMA.CUTOFF, SIGMA.CUTOFF$CUTOFF == CUTOFF.test[1])
CUTOFF.LEVEL.2 <- subset(SIGMA.CUTOFF, SIGMA.CUTOFF$CUTOFF == CUTOFF.test[2])
CUTOFF.LEVEL.3 <- subset(SIGMA.CUTOFF, SIGMA.CUTOFF$CUTOFF == CUTOFF.test[3])
CUTOFF.LEVEL.4 <- subset(SIGMA.CUTOFF, SIGMA.CUTOFF$CUTOFF == CUTOFF.test[4])

plot(  CUTOFF.LEVEL.1$SIGMA,CUTOFF.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim =c(0.25,4), 
	ylim = c(0,1), xlab = "Mutant Variance", ylab = "Power")
points(CUTOFF.LEVEL.2$SIGMA,CUTOFF.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(CUTOFF.LEVEL.3$SIGMA,CUTOFF.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(CUTOFF.LEVEL.4$SIGMA,CUTOFF.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
abline(h = 0.9, lty = 2)
legend(3.2, 0.8, legend = CUTOFF.test, fill = COLOR, title = "Cutoff Percent")
rect(xleft = .75, xright=1.5, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT XI
#X-axis SIGMA
#Y-axis POWER
#Lines  SELECTION

try <- expand.grid(MU.test[6],SIGMA.test,CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SIGMA.SELECTION <- total
SELECTION.LEVEL.1 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[1])
SELECTION.LEVEL.2 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[2])
SELECTION.LEVEL.3 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[3])
SELECTION.LEVEL.4 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[4])
SELECTION.LEVEL.5 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[5])
SELECTION.LEVEL.6 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[6])
SELECTION.LEVEL.7 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[7])
SELECTION.LEVEL.8 <- subset(SIGMA.SELECTION, SIGMA.SELECTION$SELECTION == SELECTION.test[8])

plot(  SELECTION.LEVEL.1$SIGMA,SELECTION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim=c(0.25,4), 
	ylim = c(0,1), xlab = "Mutant Variance", ylab = "Power")
points(SELECTION.LEVEL.2$SIGMA,SELECTION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(SELECTION.LEVEL.3$SIGMA,SELECTION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(SELECTION.LEVEL.4$SIGMA,SELECTION.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(SELECTION.LEVEL.5$SIGMA,SELECTION.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(SELECTION.LEVEL.6$SIGMA,SELECTION.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
points(SELECTION.LEVEL.7$SIGMA,SELECTION.LEVEL.7$POWER, typ = "l", col = COLOR[7], lwd = 2)
points(SELECTION.LEVEL.8$SIGMA,SELECTION.LEVEL.8$POWER, typ = "l", col = COLOR[8], lwd = 2)
abline(h = 0.9, lty = 2)
legend(3.2, 0.8, legend = SELECTION.test, fill = COLOR, title = "Selection Coefficient")
rect(xleft = .75, xright=1.5, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT XII
#X-axis SIGMA
#Y-axis POWER
#Lines  GENERATIONS

try <- expand.grid(MU.test[6],SIGMA.test,CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test)
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SIGMA.GENERATION <- total
GENERATION.LEVEL.1 <- subset(SIGMA.GENERATION, SIGMA.GENERATION$GENERATION == GENERATION.test[1])
GENERATION.LEVEL.2 <- subset(SIGMA.GENERATION, SIGMA.GENERATION$GENERATION == GENERATION.test[2])
GENERATION.LEVEL.3 <- subset(SIGMA.GENERATION, SIGMA.GENERATION$GENERATION == GENERATION.test[3])

plot(  GENERATION.LEVEL.1$SIGMA,GENERATION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim=c(0.25,4),
	ylim = c(0,1), xlab = "Mutant Variance", ylab = "Power")
points(GENERATION.LEVEL.2$SIGMA,GENERATION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(GENERATION.LEVEL.3$SIGMA,GENERATION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
abline(h = 0.9, lty = 2)
legend(3.2, 0.8, legend = GENERATION.test, fill = COLOR, title = "Generations")
rect(xleft = .75, xright=1.5, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT XIII
#X-axis SELECTION
#Y-axis POWER
#Lines  MU

try <- expand.grid(MU.test,SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SELECTION.MU <- total
MU.LEVEL.1 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[1])
MU.LEVEL.2 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[2])
MU.LEVEL.3 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[3])
MU.LEVEL.4 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[4])
MU.LEVEL.5 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[5])
MU.LEVEL.6 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[6])
MU.LEVEL.7 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[7])
MU.LEVEL.8 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[8])
MU.LEVEL.9 <- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[9])
MU.LEVEL.10<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[10])
MU.LEVEL.11<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[11])
MU.LEVEL.12<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[12])
MU.LEVEL.13<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[13])
MU.LEVEL.14<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[14])
MU.LEVEL.15<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[15])
MU.LEVEL.16<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[16])
MU.LEVEL.17<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[17])
MU.LEVEL.18<- subset(SELECTION.MU, SELECTION.MU$MU == MU.test[18])

plot(  MU.LEVEL.1$SELECTION, MU.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, lty = 1, xlim = c(-.01,.25), 
	ylim = c(0,1), xlab = "Mutant Selection Coefficient", ylab = "Power")
points(MU.LEVEL.2$SELECTION, MU.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2, lty = 1)
points(MU.LEVEL.3$SELECTION, MU.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2, lty = 1)
points(MU.LEVEL.4$SELECTION, MU.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2, lty = 1)
points(MU.LEVEL.5$SELECTION, MU.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2, lty = 1)
points(MU.LEVEL.6$SELECTION, MU.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2, lty = 1)
points(MU.LEVEL.7$SELECTION, MU.LEVEL.7$POWER, typ = "l", col = COLOR[7], lwd = 2, lty = 1)
points(MU.LEVEL.8$SELECTION, MU.LEVEL.8$POWER, typ = "l", col = COLOR[8], lwd = 2, lty = 1)
points(MU.LEVEL.9$SELECTION, MU.LEVEL.9$POWER, typ = "l", col = COLOR[1], lwd = 2, lty = 2)
points(MU.LEVEL.10$SELECTION, MU.LEVEL.10$POWER, typ = "l", col = COLOR[2], lwd = 2, lty = 2)
points(MU.LEVEL.11$SELECTION, MU.LEVEL.11$POWER, typ = "l", col = COLOR[3], lwd = 2, lty = 2)
points(MU.LEVEL.12$SELECTION, MU.LEVEL.12$POWER, typ = "l", col = COLOR[4], lwd = 2, lty = 2)
points(MU.LEVEL.13$SELECTION, MU.LEVEL.13$POWER, typ = "l", col = COLOR[5], lwd = 2, lty = 2)
points(MU.LEVEL.14$SELECTION, MU.LEVEL.14$POWER, typ = "l", col = COLOR[6], lwd = 2, lty = 2)
points(MU.LEVEL.15$SELECTION, MU.LEVEL.15$POWER, typ = "l", col = COLOR[7], lwd = 2, lty = 2)
points(MU.LEVEL.16$SELECTION, MU.LEVEL.16$POWER, typ = "l", col = COLOR[8], lwd = 2, lty = 2)
points(MU.LEVEL.17$SELECTION, MU.LEVEL.17$POWER, typ = "l", col = COLOR[1], lwd = 2, lty = 3)
points(MU.LEVEL.18$SELECTION, MU.LEVEL.18$POWER, typ = "l", col = COLOR[2], lwd = 2, lty = 3)
abline(h = 0.9, lty = 2)
legend(.2, 1, legend = round(convert.mu.to.percent(MU.test),3), fill = COLOR, title = "Main Effect")
rect(xleft = -.01, xright=0.1, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#PLOT XIV
#X-axis SELECTION
#Y-axis POWER
#Lines  POPULATION

try <- expand.grid(MU.test[6],SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test,SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SELECTION.POPULATION <- total
POPULATION.LEVEL.1 <- subset(SELECTION.POPULATION, SELECTION.POPULATION$POPULATION == POPULATION.test[1])
POPULATION.LEVEL.2 <- subset(SELECTION.POPULATION, SELECTION.POPULATION$POPULATION == POPULATION.test[2])
POPULATION.LEVEL.3 <- subset(SELECTION.POPULATION, SELECTION.POPULATION$POPULATION == POPULATION.test[3])
POPULATION.LEVEL.4 <- subset(SELECTION.POPULATION, SELECTION.POPULATION$POPULATION == POPULATION.test[4])

plot(  POPULATION.LEVEL.1$SELECTION,POPULATION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim = c(-.01,.25), 
	ylim = c(0,1), xlab = "Mutant Selection Coefficient", ylab = "Power")
points(POPULATION.LEVEL.2$SELECTION,POPULATION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(POPULATION.LEVEL.3$SELECTION,POPULATION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(POPULATION.LEVEL.4$SELECTION,POPULATION.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
abline(h = 0.9, lty = 2)
legend(.2, 1, legend = POPULATION.test, fill = COLOR, title = "Population Size")
rect(xleft = -.01, xright=0.1, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT XV
#X-axis SELECTION
#Y-axis POWER
#Lines  COVERAGE

try <- expand.grid(MU.test[6],SIGMA.test[4],CUTOFF.test[3],BETA.test,POPULATION.test[1],SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SELECTION.BETA <- total
BETA.LEVEL.1 <- subset(SELECTION.BETA, SELECTION.BETA$BETA == BETA.test[1])
BETA.LEVEL.2 <- subset(SELECTION.BETA, SELECTION.BETA$BETA == BETA.test[2])
BETA.LEVEL.3 <- subset(SELECTION.BETA, SELECTION.BETA$BETA == BETA.test[3])
BETA.LEVEL.4 <- subset(SELECTION.BETA, SELECTION.BETA$BETA == BETA.test[4])
BETA.LEVEL.5 <- subset(SELECTION.BETA, SELECTION.BETA$BETA == BETA.test[5])
BETA.LEVEL.6 <- subset(SELECTION.BETA, SELECTION.BETA$BETA == BETA.test[6])

plot(  BETA.LEVEL.1$SELECTION,BETA.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim = c(-0.01,.25),
	ylim = c(0,1), xlab = "Mutant Selection Coefficient", ylab = "Power" )
points(BETA.LEVEL.2$SELECTION,BETA.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(BETA.LEVEL.3$SELECTION,BETA.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(BETA.LEVEL.4$SELECTION,BETA.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(BETA.LEVEL.5$SELECTION,BETA.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(BETA.LEVEL.6$SELECTION,BETA.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
abline(h = 0.9, lty = 2)
legend(.2, 1, legend = ALPHA/BETA.test, fill = COLOR, title = "Average Coverage")
rect(xleft = -.01, xright=0.1, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT XVI
#X-axis SELECTION
#Y-axis POWER
#Lines  CUTOFF

try <- expand.grid(MU.test[6],SIGMA.test[4],CUTOFF.test,BETA.test[3],POPULATION.test[1],SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SELECTION.CUTOFF <- total
CUTOFF.LEVEL.1 <- subset(SELECTION.CUTOFF, SELECTION.CUTOFF$CUTOFF == CUTOFF.test[1])
CUTOFF.LEVEL.2 <- subset(SELECTION.CUTOFF, SELECTION.CUTOFF$CUTOFF == CUTOFF.test[2])
CUTOFF.LEVEL.3 <- subset(SELECTION.CUTOFF, SELECTION.CUTOFF$CUTOFF == CUTOFF.test[3])
CUTOFF.LEVEL.4 <- subset(SELECTION.CUTOFF, SELECTION.CUTOFF$CUTOFF == CUTOFF.test[4])

plot(  CUTOFF.LEVEL.1$SELECTION,CUTOFF.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim =c(-0.01,.25), 
	ylim = c(0,1), xlab = "Mutant Selection Coefficient", ylab = "Power")
points(CUTOFF.LEVEL.2$SELECTION,CUTOFF.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(CUTOFF.LEVEL.3$SELECTION,CUTOFF.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(CUTOFF.LEVEL.4$SELECTION,CUTOFF.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
abline(h = 0.9, lty = 2)
legend(.2, 1, legend = CUTOFF.test, fill = COLOR, title = "Cutoff Percent")
rect(xleft = -.01, xright=0.1, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT XVII
#X-axis SELECTION
#Y-axis POWER
#Lines  SIGMA

try <- expand.grid(MU.test[6],SIGMA.test,CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test,GENERATION.test[2])
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SELECTION.SIGMA <- total
SIGMA.LEVEL.1 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[1])
SIGMA.LEVEL.2 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[2])
SIGMA.LEVEL.3 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[3])
SIGMA.LEVEL.4 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[4])
SIGMA.LEVEL.5 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[5])
SIGMA.LEVEL.6 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[6])
SIGMA.LEVEL.7 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[7])
SIGMA.LEVEL.8 <- subset(SELECTION.SIGMA, SELECTION.SIGMA$SIGMA == SIGMA.test[8])

plot(  SIGMA.LEVEL.1$SELECTION,SELECTION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim=c(-0.01,.25), 
	ylim = c(0,1), xlab = "Mutant Selection Coefficient", ylab = "Power")
points(SIGMA.LEVEL.2$SELECTION,SIGMA.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(SIGMA.LEVEL.3$SELECTION,SIGMA.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
points(SIGMA.LEVEL.4$SELECTION,SIGMA.LEVEL.4$POWER, typ = "l", col = COLOR[4], lwd = 2)
points(SIGMA.LEVEL.5$SELECTION,SIGMA.LEVEL.5$POWER, typ = "l", col = COLOR[5], lwd = 2)
points(SIGMA.LEVEL.6$SELECTION,SIGMA.LEVEL.6$POWER, typ = "l", col = COLOR[6], lwd = 2)
points(SIGMA.LEVEL.7$SELECTION,SIGMA.LEVEL.7$POWER, typ = "l", col = COLOR[7], lwd = 2)
points(SIGMA.LEVEL.8$SELECTION,SIGMA.LEVEL.8$POWER, typ = "l", col = COLOR[8], lwd = 2)
abline(h = 0.9, lty = 2)
legend(.2, 1, legend = SIGMA.test, fill = COLOR, title = "Mutant Variance")
rect(xleft = -0.1, xright=0.1, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

###PLOT XVIII
#X-axis SELECTION
#Y-axis POWER
#Lines  GENERATIONS

try <- expand.grid(MU.test[6],SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test,GENERATION.test)
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST="G")
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
}
total <- cbind(try,power,diff)
colnames(total) <- c("MU","SIGMA","CUTOFF","BETA","POPULATION","SELECTION","GENERATION","POWER","5%LB","5%UB")

SELECTION.GENERATION <- total
GENERATION.LEVEL.1 <- subset(SELECTION.GENERATION, SELECTION.GENERATION$GENERATION == GENERATION.test[1])
GENERATION.LEVEL.2 <- subset(SELECTION.GENERATION, SELECTION.GENERATION$GENERATION == GENERATION.test[2])
GENERATION.LEVEL.3 <- subset(SELECTION.GENERATION, SELECTION.GENERATION$GENERATION == GENERATION.test[3])

plot(  GENERATION.LEVEL.1$SELECTION,GENERATION.LEVEL.1$POWER, typ = "l", col = COLOR[1], lwd = 2, xlim=c(-0.01,.25),
	ylim = c(0,1), xlab = "Mutant Selection Coefficient", ylab = "Power")
points(GENERATION.LEVEL.2$SELECTION,GENERATION.LEVEL.2$POWER, typ = "l", col = COLOR[2], lwd = 2)
points(GENERATION.LEVEL.3$SELECTION,GENERATION.LEVEL.3$POWER, typ = "l", col = COLOR[3], lwd = 2)
abline(h = 0.9, lty = 2)
abline(h = 0.9, lty = 2)
legend(.2, 1, legend = GENERATION.test, fill = COLOR, title = "Generations")
rect(xleft = -0.1, xright=0.1, ybottom=0, ytop=1, col = "#88888844", border = "#88888844")

#####################################################
###5. Comparison of Fisher's Exact Test and G-test###
#####################################################
try <- expand.grid(MU.test[1:10],SIGMA.test[4],CUTOFF.test[3],BETA.test[c(3:4,6)],POPULATION.test[1],SELECTION.test[4],GENERATION.test[2],TEST=c("F","G"))
K <- 1:nrow(try)
power <- numeric(length(K))
diff <- matrix(0, nrow = length(K), ncol = 2)
for(k in K) {
	x <- Power(ALPHA = 80, MU = try[k,1], SIGMA = try[k,2], CUTOFF = try[k,3], BETA = try[k,4], POPULATION = try[k,5], SELECTION = try[k,6], GENERATION = try[k,7], TEST = try[k,8])
	diff[k,1]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.025)
	diff[k,2]  <- quantile(x$Mutant.High/(x$Mutant.High + x$WT.High + 1) - x$Mutant.Low/(x$Mutant.Low + x$WT.Low + 1),0.975)
	power[k] <- mean(x$Power)
} 

G.100 <- power[which(try[,8] == "G" & try[,4] == 0.80)]
G.50  <- power[which(try[,8] == "G" & try[,4] == 1.6)]
G.10  <- power[which(try[,8] == "G" & try[,4] == 8)]
F.100 <- power[which(try[,8] == "F" & try[,4] == 0.80)]
F.50  <- power[which(try[,8] == "F" & try[,4] == 1.6)]
F.10  <- power[which(try[,8] == "F" & try[,4] == 8)]

#Plot
plot(  G.100, F.100, type="b", lwd=3, col="red", pch = 19, xlab="G-Test Power", ylab="Fisher Exact Test Power")
points(G.100, F.100, type="b", lwd=3, col="red", pch = 19)
points(G.50,  F.50,  type="b", lwd=3, col="green", pch = 19)
points(G.10,  F.10,  type="b", lwd=3, col="blue", pch = 19)
abline(a = 0, b= 1, lwd = 2, lty = 2)
legend(.2,1,fill=c("red","green","blue"), legend = c("100","50","10"))

############################
###6. False Positive Rate###
############################
#Calculate power assuming a mutation of no effect on mean or variance
x <- Power(ALPHA = 80, MU = MU.test[1], SIGMA.test[4],CUTOFF.test[3],BETA.test[3],POPULATION.test[1],SELECTION.test[4],GENERATION.test[2],TEST="G")

#########################################
###7. Deterministic Allele Frequencies###
#########################################
#Estimate distribution of effects prior to sampling
x <- rnorm((1-MUTANT.FREQ)*20000000,0,1)
y <- rnorm(MUTANT.FREQ*20000000,MU,SIGMA)
hist(c(x,y), breaks = 100, xlim = c(-6,6), ylim = c(0,1000000), col = "#000000AA", main = "", xlab = "", xaxt="n")
axis(1,convert.percent.to.mu(c(-.45,-.3,-.15,0,.15,.3,.45)),c("-45","-30","-15","0","15","30","45"))
par(new = TRUE)
hist(x, breaks = 100, xlim = c(-6,6), ylim = c(0,1000000), col = "#FF0000AA", main = "", xlab = "",xaxt="n")
par(new = TRUE)
hist(y, breaks = 100, xlim = c(-6,6), ylim = c(0,1000000), col = "#0000FFAA", main = "", xlab = "",xaxt="n")
abline(v = THRESHOLD.LOW, lwd = 2)
abline(v = THRESHOLD.HIGH, lwd = 2)
legend(-6,500000,fill = c("red","blue","black"), legend = c("WT","Mutant","Total"))
#Get power
z <- Power(ALPHA,BETA,CUTOFF,MU,SIGMA,POPULATION,SELECTION,GENERATION,TEST)