R/power.analysis.R
In nicksard/fitr: Functions to aid in the analysis of fitness data generated using genetically reconstructed pedigrees.
Defines functions
power.analysis
########################
### power.analysis() ###
########################
#Description
#This fuction is a modified version of Mark Christie's script to calculate the exclusion power within a dataset
# It will calculates the expected number of false parent offspring pairs (POPs) within the dataset and
# it also calculates the exclusion power per locus
#Input Parameters:
#adults - this is a data frame with parents ids and genotypes
#offspring - this is a data frame with offspring ids and genotypes
#loci_number - define as the number of loci you want to analyze
# NOTE: from what I understand it randomly selects which ones to analyze
#opt - has three versions
# opt 1 - writes both Exp. Number of false POPs and Locus.Power to file
# opt 2 - returns Exp. Number of false POPs
# opt 3 - reutnrs Locus.Power
power.analysis <- function(adults,offspring, loci_number=0,opt=1){
if(length(loci_number)==1){
if(loci_number == 0){print("Need to define the number of loci to analyze before I can continue..."); stop}
multi <- T
} else {
multi <- F
}
if(multi==F){
#a long winded verson of getting the adults and offspring gts without their sample names
Adults1 <- adults
a <- ncol(Adults1)
lociset <- seq(from= 2,to= a,by= 2)
loci <- as.numeric(loci_number)
s1 <- sample(lociset,loci,replace= FALSE)
s2 <- sort(c(s1,s1+1))
Adults <- Adults1[,s2]
Offspring1 <- offspring
Offspring <- Offspring1[,s2]
head(Offspring)
head(Adults)
a <- 1
#Begin calcualtion of Pr(Z) and Pr(delta)=======================================#
massive <- function(massive){
#making a table with unique alleles, their counts, and frequencies for parents
AAT40 <- c(Adults[,i],Adults[,(i+1)])
locus <- as.data.frame(table(AAT40))
Frequency <- locus[,2]/sum(locus[,2])
Data <- cbind(locus, Frequency)
Data
#removing missing data from the table
if (Data[1,1]==0) {
Data <- Data[-1,]
} else {
Data <- Data
} #end of missing data remover
#calculating the expected number of each homozygote allele
n1 <- ((length(AAT40))/2)
A1 <- (Data[,3]*(2*n1))
A2 <- (Data[,3]^2)*n1
AA <- A1-A2
AAA <- cbind(Data,AA)
AAA
#making a table with unique alleles, their counts, and frequencies for offspring
AAT402 <- c(Offspring[,i],Offspring[,(i+1)])
locus2 <- as.data.frame(table(AAT402))
Frequency2 <- locus2[,2]/sum(locus2[,2])
Data2 <- cbind(locus2, Frequency2)
Data2
#removing missing data again
if (Data2[1,1]==0){
Data2=Data2[-1,]
} else {
Data2=Data2
} #end of missing data remover
#calculating the expected number of each homozygote allele
n2 <- ((length(AAT402))/2)
B1 <- (Data2[,3]*(2*n2))
B2 <- (Data2[,3]^2)*n2
BB <- B1-B2
BBB <- cbind(Data2,BB)
BBB
#getting the alleles in the parents that are found in the kids
AAA1 <- match(BBB[,1],AAA[,1])
g <- which(AAA1>0)
g1 <- AAA1[g]
AAA1 <- AAA[g1,]
AAA1
#doing the same thing for the kids
BBB1 <- match(AAA[,1],BBB[,1])
j <- which(BBB1>0)
j1 <- BBB1[j]
BBB1 <- BBB[j1,]
#multiplying the number of expected from parents and kids and summing
AB <- AAA1[,4]*BBB1[,4]
AB <- sum(AB)
AB
#getting all possible combinations of alleles and removing homozygotes for the parents
y <- AAA1[,1]
Agenotypes <- expand.grid(y,y)
remove <- which(Agenotypes[,1]==Agenotypes[,2])
Agenotypes <- Agenotypes[-remove,]
Agenotypes
#doing the same for the kids
z <- BBB1[,1]
Bgenotypes <- expand.grid(z,z)
remove <- which(Bgenotypes[,1]==Bgenotypes[,2])
Bgenotypes <- Bgenotypes[-remove,]
Bgenotypes
#getting the expected number of parents that would have each heterozygous genotype
one <- match(Agenotypes[,1],AAA1[,1])
two <- match(Agenotypes[,2],AAA1[,1])
one <- AAA1[one,3]
two <- AAA1[two,3]
Agfreq <- one*two*n1*2
Agfreq
#getting the expected number of kids that would have each heterozygous genotype
oneb <- match(Bgenotypes[,1],BBB1[,1])
twob <- match(Bgenotypes[,2],BBB1[,1])
oneb <- BBB1[oneb,3]
twob <- BBB1[twob,3]
Ogfreq <- oneb*twob*n2*2
Ogfreq
#combining the two and calculating the probability of being false at that locus
Gfreqs <- Agfreq*Ogfreq
Gfreqs <- floor(Gfreqs)
Gfreq <- sum(Gfreqs)/2
PrB <- (AB-Gfreq)/(n1*n2)
write.table(PrB,file="Output_Per_Locus_Exclusion_Probabilities.txt",row.names=FALSE,col.names=F,sep="\t",append=T)
} # end of massive function
a <- ncol(Adults)
loci.locs <- seq(from=1,to=ncol(Adults)-1, by=2)
loci.locs
#applying massive function to all loci
for(i in loci.locs) {
lapply(i,massive)
} # end of for loop calculating exclusion probs
#reading the file with each locus' exclusion prob in
PrBs <- read.table("Output_Per_Locus_Exclusion_Probabilities.txt", header=F, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
#getting the number of parents and offspring
nn1 <- length(Adults[,1])
nn2 <- length(Offspring[,1])
#getting the product of all the independent probs
Pr_delta <- prod(PrBs[,1])
#getting exclusion power per locus
Locus.Power <- data.frame(colnames(Offspring)[loci.locs],PrBs,stringsAsFactors=F)
colnames(Locus.Power) <- c("Loci","Exc.Power")
Locus.Power
#making table with information in it
Expected_Number_of_False_Pairs <- Pr_delta*(nn1*nn2)
pvalue1 <- data.frame(loci_number,Expected_Number_of_False_Pairs,Pr_delta,stringsAsFactors=F)
#removing the saved file
unlink("Output_Per_Locus_Exclusion_Probabilities.txt")
} else {
pvalue1 <- NULL
for(i in 1:length(loci_number)){
#a long winded verson of getting the adults and offspring gts without their sample names
Adults1 <- adults
a <- ncol(Adults1)
lociset <- seq(from= 2,to= a,by= 2)
loci <- as.numeric(loci_number[i])
s1 <- sample(lociset,loci,replace= FALSE)
s2 <- sort(c(s1,s1+1))
Adults <- Adults1[,s2]
Offspring1 <- offspring
Offspring <- Offspring1[,s2]
head(Offspring)
head(Adults)
a <- 1
#Begin calcualtion of Pr(Z) and Pr(delta)=======================================#
massive <- function(massive){
#making a table with unique alleles, their counts, and frequencies for parents
AAT40 <- c(Adults[,i],Adults[,(i+1)])
locus <- as.data.frame(table(AAT40))
Frequency <- locus[,2]/sum(locus[,2])
Data <- cbind(locus, Frequency)
Data
#removing missing data from the table
if (Data[1,1]==0) {
Data <- Data[-1,]
} else {
Data <- Data
} #end of missing data remover
#calculating the expected number of each homozygote allele
n1 <- ((length(AAT40))/2)
A1 <- (Data[,3]*(2*n1))
A2 <- (Data[,3]^2)*n1
AA <- A1-A2
AAA <- cbind(Data,AA)
AAA
#making a table with unique alleles, their counts, and frequencies for offspring
AAT402 <- c(Offspring[,i],Offspring[,(i+1)])
locus2 <- as.data.frame(table(AAT402))
Frequency2 <- locus2[,2]/sum(locus2[,2])
Data2 <- cbind(locus2, Frequency2)
Data2
#removing missing data again
if (Data2[1,1]==0){
Data2=Data2[-1,]
} else {
Data2=Data2
} #end of missing data remover
#calculating the expected number of each homozygote allele
n2 <- ((length(AAT402))/2)
B1 <- (Data2[,3]*(2*n2))
B2 <- (Data2[,3]^2)*n2
BB <- B1-B2
BBB <- cbind(Data2,BB)
BBB
#getting the alleles in the parents that are found in the kids
AAA1 <- match(BBB[,1],AAA[,1])
g <- which(AAA1>0)
g1 <- AAA1[g]
AAA1 <- AAA[g1,]
AAA1
#doing the same thing for the kids
BBB1 <- match(AAA[,1],BBB[,1])
j <- which(BBB1>0)
j1 <- BBB1[j]
BBB1 <- BBB[j1,]
#multiplying the number of expected from parents and kids and summing
AB <- AAA1[,4]*BBB1[,4]
AB <- sum(AB)
AB
#getting all possible combinations of alleles and removing homozygotes for the parents
y <- AAA1[,1]
Agenotypes <- expand.grid(y,y)
remove <- which(Agenotypes[,1]==Agenotypes[,2])
Agenotypes <- Agenotypes[-remove,]
Agenotypes
#doing the same for the kids
z <- BBB1[,1]
Bgenotypes <- expand.grid(z,z)
remove <- which(Bgenotypes[,1]==Bgenotypes[,2])
Bgenotypes <- Bgenotypes[-remove,]
Bgenotypes
#getting the expected number of parents that would have each heterozygous genotype
one <- match(Agenotypes[,1],AAA1[,1])
two <- match(Agenotypes[,2],AAA1[,1])
one <- AAA1[one,3]
two <- AAA1[two,3]
Agfreq <- one*two*n1*2
Agfreq
#getting the expected number of kids that would have each heterozygous genotype
oneb <- match(Bgenotypes[,1],BBB1[,1])
twob <- match(Bgenotypes[,2],BBB1[,1])
oneb <- BBB1[oneb,3]
twob <- BBB1[twob,3]
Ogfreq <- oneb*twob*n2*2
Ogfreq
#combining the two and calculating the probability of being false at that locus
Gfreqs <- Agfreq*Ogfreq
Gfreqs <- floor(Gfreqs)
Gfreq <- sum(Gfreqs)/2
PrB <- (AB-Gfreq)/(n1*n2)
write.table(PrB,file="Output_Per_Locus_Exclusion_Probabilities.txt",row.names=FALSE,col.names=F,sep="\t",append=T)
} # end of massive function
a <- ncol(Adults)
loci.locs <- seq(from=1,to=ncol(Adults)-1, by=2)
loci.locs
#applying massive function to all loci
for(i in loci.locs) {
lapply(i,massive)
} # end of for loop calculating exclusion probs
#reading the file with each locus' exclusion prob in
PrBs <- read.table("Output_Per_Locus_Exclusion_Probabilities.txt", header=F, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
#getting the number of parents and offspring
nn1 <- length(Adults[,1])
nn2 <- length(Offspring[,1])
#getting the product of all the independent probs
Pr_delta <- prod(PrBs[,1])
#getting exclusion power per locus
Locus.Power <- data.frame(colnames(Offspring)[loci.locs],PrBs,stringsAsFactors=F)
colnames(Locus.Power) <- c("Loci","Exc.Power")
Locus.Power
#making table with information in it
Expected_Number_of_False_Pairs <- Pr_delta*(nn1*nn2)
pvalue1.1 <- data.frame(Expected_Number_of_False_Pairs,Pr_delta,stringsAsFactors=F)
pvalue1 <- rbind(pvalue1, pvalue1.1)
#removing the saved file
unlink("Output_Per_Locus_Exclusion_Probabilities.txt")
}
pvalue1 <- cbind(loci_number,pvalue1)
}
#writing both to file
if(opt == 1){
write.table(Locus.Power,file="Locus power.txt",row.names=FALSE,col.names=T,sep="\t",append=T)
write.table(pvalue1,file="Expected Number of False Pairs.txt",row.names=FALSE,col.names=T,sep="\t",append=T)
print("Locus power.txt saved to current working directory")
print("Expected Number of False Pairs.txt saved to current working directory")
} #end of option 1
#returning only the expected number of false pairs
if(opt == 2){
return(pvalue1)
} #end of option 2
#returning the exc. power per locus
if(opt == 3){
return(Locus.Power)
} #end of option 3
} #end of power analysis function
nicksard/fitr documentation built on May 20, 2019, 11:16 a.m.
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Defines functions power.analysis
########################
### power.analysis() ###
########################
#Description
#This fuction is a modified version of Mark Christie's script to calculate the exclusion power within a dataset
# It will calculates the expected number of false parent offspring pairs (POPs) within the dataset and
# it also calculates the exclusion power per locus
#Input Parameters:
#adults - this is a data frame with parents ids and genotypes
#offspring - this is a data frame with offspring ids and genotypes
#loci_number - define as the number of loci you want to analyze
# NOTE: from what I understand it randomly selects which ones to analyze
#opt - has three versions
# opt 1 - writes both Exp. Number of false POPs and Locus.Power to file
# opt 2 - returns Exp. Number of false POPs
# opt 3 - reutnrs Locus.Power
power.analysis <- function(adults,offspring, loci_number=0,opt=1){
if(length(loci_number)==1){
if(loci_number == 0){print("Need to define the number of loci to analyze before I can continue..."); stop}
multi <- T
} else {
multi <- F
}
if(multi==F){
#a long winded verson of getting the adults and offspring gts without their sample names
Adults1 <- adults
a <- ncol(Adults1)
lociset <- seq(from= 2,to= a,by= 2)
loci <- as.numeric(loci_number)
s1 <- sample(lociset,loci,replace= FALSE)
s2 <- sort(c(s1,s1+1))
Adults <- Adults1[,s2]
Offspring1 <- offspring
Offspring <- Offspring1[,s2]
head(Offspring)
head(Adults)
a <- 1
#Begin calcualtion of Pr(Z) and Pr(delta)=======================================#
massive <- function(massive){
#making a table with unique alleles, their counts, and frequencies for parents
AAT40 <- c(Adults[,i],Adults[,(i+1)])
locus <- as.data.frame(table(AAT40))
Frequency <- locus[,2]/sum(locus[,2])
Data <- cbind(locus, Frequency)
Data
#removing missing data from the table
if (Data[1,1]==0) {
Data <- Data[-1,]
} else {
Data <- Data
} #end of missing data remover
#calculating the expected number of each homozygote allele
n1 <- ((length(AAT40))/2)
A1 <- (Data[,3]*(2*n1))
A2 <- (Data[,3]^2)*n1
AA <- A1-A2
AAA <- cbind(Data,AA)
AAA
#making a table with unique alleles, their counts, and frequencies for offspring
AAT402 <- c(Offspring[,i],Offspring[,(i+1)])
locus2 <- as.data.frame(table(AAT402))
Frequency2 <- locus2[,2]/sum(locus2[,2])
Data2 <- cbind(locus2, Frequency2)
Data2
#removing missing data again
if (Data2[1,1]==0){
Data2=Data2[-1,]
} else {
Data2=Data2
} #end of missing data remover
#calculating the expected number of each homozygote allele
n2 <- ((length(AAT402))/2)
B1 <- (Data2[,3]*(2*n2))
B2 <- (Data2[,3]^2)*n2
BB <- B1-B2
BBB <- cbind(Data2,BB)
BBB
#getting the alleles in the parents that are found in the kids
AAA1 <- match(BBB[,1],AAA[,1])
g <- which(AAA1>0)
g1 <- AAA1[g]
AAA1 <- AAA[g1,]
AAA1
#doing the same thing for the kids
BBB1 <- match(AAA[,1],BBB[,1])
j <- which(BBB1>0)
j1 <- BBB1[j]
BBB1 <- BBB[j1,]
#multiplying the number of expected from parents and kids and summing
AB <- AAA1[,4]*BBB1[,4]
AB <- sum(AB)
AB
#getting all possible combinations of alleles and removing homozygotes for the parents
y <- AAA1[,1]
Agenotypes <- expand.grid(y,y)
remove <- which(Agenotypes[,1]==Agenotypes[,2])
Agenotypes <- Agenotypes[-remove,]
Agenotypes
#doing the same for the kids
z <- BBB1[,1]
Bgenotypes <- expand.grid(z,z)
remove <- which(Bgenotypes[,1]==Bgenotypes[,2])
Bgenotypes <- Bgenotypes[-remove,]
Bgenotypes
#getting the expected number of parents that would have each heterozygous genotype
one <- match(Agenotypes[,1],AAA1[,1])
two <- match(Agenotypes[,2],AAA1[,1])
one <- AAA1[one,3]
two <- AAA1[two,3]
Agfreq <- one*two*n1*2
Agfreq
#getting the expected number of kids that would have each heterozygous genotype
oneb <- match(Bgenotypes[,1],BBB1[,1])
twob <- match(Bgenotypes[,2],BBB1[,1])
oneb <- BBB1[oneb,3]
twob <- BBB1[twob,3]
Ogfreq <- oneb*twob*n2*2
Ogfreq
#combining the two and calculating the probability of being false at that locus
Gfreqs <- Agfreq*Ogfreq
Gfreqs <- floor(Gfreqs)
Gfreq <- sum(Gfreqs)/2
PrB <- (AB-Gfreq)/(n1*n2)
write.table(PrB,file="Output_Per_Locus_Exclusion_Probabilities.txt",row.names=FALSE,col.names=F,sep="\t",append=T)
} # end of massive function
a <- ncol(Adults)
loci.locs <- seq(from=1,to=ncol(Adults)-1, by=2)
loci.locs
#applying massive function to all loci
for(i in loci.locs) {
lapply(i,massive)
} # end of for loop calculating exclusion probs
#reading the file with each locus' exclusion prob in
PrBs <- read.table("Output_Per_Locus_Exclusion_Probabilities.txt", header=F, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
#getting the number of parents and offspring
nn1 <- length(Adults[,1])
nn2 <- length(Offspring[,1])
#getting the product of all the independent probs
Pr_delta <- prod(PrBs[,1])
#getting exclusion power per locus
Locus.Power <- data.frame(colnames(Offspring)[loci.locs],PrBs,stringsAsFactors=F)
colnames(Locus.Power) <- c("Loci","Exc.Power")
Locus.Power
#making table with information in it
Expected_Number_of_False_Pairs <- Pr_delta*(nn1*nn2)
pvalue1 <- data.frame(loci_number,Expected_Number_of_False_Pairs,Pr_delta,stringsAsFactors=F)
#removing the saved file
unlink("Output_Per_Locus_Exclusion_Probabilities.txt")
} else {
pvalue1 <- NULL
for(i in 1:length(loci_number)){
#a long winded verson of getting the adults and offspring gts without their sample names
Adults1 <- adults
a <- ncol(Adults1)
lociset <- seq(from= 2,to= a,by= 2)
loci <- as.numeric(loci_number[i])
s1 <- sample(lociset,loci,replace= FALSE)
s2 <- sort(c(s1,s1+1))
Adults <- Adults1[,s2]
Offspring1 <- offspring
Offspring <- Offspring1[,s2]
head(Offspring)
head(Adults)
a <- 1
#Begin calcualtion of Pr(Z) and Pr(delta)=======================================#
massive <- function(massive){
#making a table with unique alleles, their counts, and frequencies for parents
AAT40 <- c(Adults[,i],Adults[,(i+1)])
locus <- as.data.frame(table(AAT40))
Frequency <- locus[,2]/sum(locus[,2])
Data <- cbind(locus, Frequency)
Data
#removing missing data from the table
if (Data[1,1]==0) {
Data <- Data[-1,]
} else {
Data <- Data
} #end of missing data remover
#calculating the expected number of each homozygote allele
n1 <- ((length(AAT40))/2)
A1 <- (Data[,3]*(2*n1))
A2 <- (Data[,3]^2)*n1
AA <- A1-A2
AAA <- cbind(Data,AA)
AAA
#making a table with unique alleles, their counts, and frequencies for offspring
AAT402 <- c(Offspring[,i],Offspring[,(i+1)])
locus2 <- as.data.frame(table(AAT402))
Frequency2 <- locus2[,2]/sum(locus2[,2])
Data2 <- cbind(locus2, Frequency2)
Data2
#removing missing data again
if (Data2[1,1]==0){
Data2=Data2[-1,]
} else {
Data2=Data2
} #end of missing data remover
#calculating the expected number of each homozygote allele
n2 <- ((length(AAT402))/2)
B1 <- (Data2[,3]*(2*n2))
B2 <- (Data2[,3]^2)*n2
BB <- B1-B2
BBB <- cbind(Data2,BB)
BBB
#getting the alleles in the parents that are found in the kids
AAA1 <- match(BBB[,1],AAA[,1])
g <- which(AAA1>0)
g1 <- AAA1[g]
AAA1 <- AAA[g1,]
AAA1
#doing the same thing for the kids
BBB1 <- match(AAA[,1],BBB[,1])
j <- which(BBB1>0)
j1 <- BBB1[j]
BBB1 <- BBB[j1,]
#multiplying the number of expected from parents and kids and summing
AB <- AAA1[,4]*BBB1[,4]
AB <- sum(AB)
AB
#getting all possible combinations of alleles and removing homozygotes for the parents
y <- AAA1[,1]
Agenotypes <- expand.grid(y,y)
remove <- which(Agenotypes[,1]==Agenotypes[,2])
Agenotypes <- Agenotypes[-remove,]
Agenotypes
#doing the same for the kids
z <- BBB1[,1]
Bgenotypes <- expand.grid(z,z)
remove <- which(Bgenotypes[,1]==Bgenotypes[,2])
Bgenotypes <- Bgenotypes[-remove,]
Bgenotypes
#getting the expected number of parents that would have each heterozygous genotype
one <- match(Agenotypes[,1],AAA1[,1])
two <- match(Agenotypes[,2],AAA1[,1])
one <- AAA1[one,3]
two <- AAA1[two,3]
Agfreq <- one*two*n1*2
Agfreq
#getting the expected number of kids that would have each heterozygous genotype
oneb <- match(Bgenotypes[,1],BBB1[,1])
twob <- match(Bgenotypes[,2],BBB1[,1])
oneb <- BBB1[oneb,3]
twob <- BBB1[twob,3]
Ogfreq <- oneb*twob*n2*2
Ogfreq
#combining the two and calculating the probability of being false at that locus
Gfreqs <- Agfreq*Ogfreq
Gfreqs <- floor(Gfreqs)
Gfreq <- sum(Gfreqs)/2
PrB <- (AB-Gfreq)/(n1*n2)
write.table(PrB,file="Output_Per_Locus_Exclusion_Probabilities.txt",row.names=FALSE,col.names=F,sep="\t",append=T)
} # end of massive function
a <- ncol(Adults)
loci.locs <- seq(from=1,to=ncol(Adults)-1, by=2)
loci.locs
#applying massive function to all loci
for(i in loci.locs) {
lapply(i,massive)
} # end of for loop calculating exclusion probs
#reading the file with each locus' exclusion prob in
PrBs <- read.table("Output_Per_Locus_Exclusion_Probabilities.txt", header=F, sep="\t", na.strings="NA", dec=".", strip.white=TRUE)
#getting the number of parents and offspring
nn1 <- length(Adults[,1])
nn2 <- length(Offspring[,1])
#getting the product of all the independent probs
Pr_delta <- prod(PrBs[,1])
#getting exclusion power per locus
Locus.Power <- data.frame(colnames(Offspring)[loci.locs],PrBs,stringsAsFactors=F)
colnames(Locus.Power) <- c("Loci","Exc.Power")
Locus.Power
#making table with information in it
Expected_Number_of_False_Pairs <- Pr_delta*(nn1*nn2)
pvalue1.1 <- data.frame(Expected_Number_of_False_Pairs,Pr_delta,stringsAsFactors=F)
pvalue1 <- rbind(pvalue1, pvalue1.1)
#removing the saved file
unlink("Output_Per_Locus_Exclusion_Probabilities.txt")
}
pvalue1 <- cbind(loci_number,pvalue1)
}
#writing both to file
if(opt == 1){
write.table(Locus.Power,file="Locus power.txt",row.names=FALSE,col.names=T,sep="\t",append=T)
write.table(pvalue1,file="Expected Number of False Pairs.txt",row.names=FALSE,col.names=T,sep="\t",append=T)
print("Locus power.txt saved to current working directory")
print("Expected Number of False Pairs.txt saved to current working directory")
} #end of option 1
#returning only the expected number of false pairs
if(opt == 2){
return(pvalue1)
} #end of option 2
#returning the exc. power per locus
if(opt == 3){
return(Locus.Power)
} #end of option 3
} #end of power analysis function
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