source('~/.active-rstudio-document')
source('~/.active-rstudio-document')
rs<-Rand.Between(rmin,rmax,n.iters)
Ks<-Rand.Between(Kmin,Kmax,n.iters)
Nos<-Rand.Between(Nomin,Nomax,n.iters)
qs<-Rand.Between(qmin,qmax,n.iters)
rs
source('~/.active-rstudio-document')
run.logistic<-function(No,q,r,K,E,n.years=25){
N.list<-rep(NA,length=nyears)
N.list[1]=No
for (i in 2:n.years){
N.list[i]<-N.list[i-1]*(1+r-r*N.list[i-1]/K-q*E)
}
percent.change<-100*(N.list[n.years]-No)/No
return(list(N.list=N.list,percent.change=percent.change))
}
for (j in 1:n.iters){
Model.output<-run.logistic(Nos[j],qs[j],rs[j],Ks[j],E,n.years)
output[j, 5]<- Model.output$percent.change
}
for (j in 1:n.iters){
Model.output<-run.logistic(Nos[j],qs[j],rs[j],Ks[j],E,n.years)
output[j, 5]<- Model.output$percent.change
}
source('~/.active-rstudio-document')
install.packages("BDgraph")
install.packages("ape")
install.packages("ade4")
install.packages("adegenet")
install.packages("diveRsity")
install.packages("doParallel")
install.packages("foreach")
install.packages("genetics")
install.packages("hierfstat")
install.packages("iterators")
install.packages("parallel")### This may not be available but it's ok.
install.packages("sendplot")
install.packages("xlsx")
library(ape)
library(ade4)
library(adegenet)
library(diveRsity)
library(doParallel)
library(foreach)
library(genetics)
library(hierfstat)
library(iterators)
library(parallel)
library(sendplot)
library(xlsx)
library(BDgraph)
setwd("C:/users/jheare/Desktop/researchproject")
salmon <- read.genepop("Class_data_genepop_new.gen", missing=NA)
pop_stats <- fstOnly(infile="class_data_genepop_new.gen",outfile="Salmon_Pairwise_Fst",
gp=3,bs_pairwise=TRUE,bootstraps=100,parallel=TRUE)
pairwise_fst <- read.xlsx("class_dataset.xlsx",header=TRUE,row.names=1)
pairwise_fst <- read.csv("class_dataset.xlsx",header=TRUE,row.names=1)
pairwise_fst <- read.csv("class_dataset",header=TRUE,row.names=1)
pairwise_fst <- read.xlsx("class_dataset.xlsx",1)
pairwise_fst
fst <- as.dist(pairwise_fst) ## Convert pairwise_fst to a distance object
fst  ### we'll use this matrix later in the analysis
pop_stats <- fstOnly(infile="class_data_genepop_new.gen",outfile="Salmon_Pairwise_Fst",
gp=3,bs_pairwise=TRUE,bootstraps=100,parallel=TRUE)
pop_stats <- fstOnly(infile="class_data_genepop_new.gen",outfile="Salmon_Pairwise_Fst",
gp=3,bs_pairwise=TRUE,bootstraps=100,parallel=TRUE)
pop_labels <- c(rep("Founders1998",50),rep("Int2002",50),
rep("Seg2002",50),rep("Int2006",50),rep("Seg2006",50),rep("Int2010",50),
rep("Seg2010",50)
rep("Seg2010",50))
pop_labels <- c(rep("Founders1998",50),rep("Int2002",50),
rep("Seg2002",50),rep("Int2006",50),rep("Seg2006",50),rep("Int2010",50),
rep("Seg2010",50))
## Creates a vector containing the population assignments of each individual
pop_labels <- c(rep("Founders1998",50),rep("Int2002",50),
rep("Seg2002",50),rep("Int2006",50),rep("Seg2006",50),rep("Int2010",50),
rep("Seg2010",50) )
pops_separated <- seppop(salmon,pop=pop_labels)
names(pops_separated)
data_Founders1998 <-pops_separated$Founders1998
data_Founders1998
data_Int2002 <-pops_separated$Int2002
data_Seg2002 <-pops_separated$Seg2002
data_Int2006 <-pops_separated$Int2006
data_Seg2006 <-pops_separated$Seg2006
data_Int2010 <-pops_separated$Int2010
data_Seg2010 <-pops_separated$Seg2010
summary_Founders1998 <- summary(data_Founders1998)
mean(summary_Founders1998$Hexp)
mean(summary_Founders1998$Hobs)
t.test(summary_Founders1998$Hobs,summary_AD$Hexp,paired=TRUE)
t.test(summary_Founders1998$Hobs,summary_Founders1998$Hexp,paired=TRUE)
summary_Int2002 <- summary(data_Int2002)
mean(summary_Int2002$Hexp)
mean(summary_Int2002$Hobs)
t.test(summary_Int2002$Hobs,summary_Int2002$Hexp,paired=TRUE)
summary_Seg2002 <- summary(data_Seg2002)
mean(summary_Seg2002$Hexp)
mean(summary_Seg2002$Hobs)
t.test(summary_Seg2002$Hobs,summary_Seg2002$Hexp,paired=TRUE)
summary_Int2006 <- summary(data_Int2006)
mean(summary_Int2006$Hexp)
mean(summary_Int2006$Hobs)
t.test(summary_Int2006$Hobs,summary_Int2006$Hexp,paired=TRUE)
summary_Seg2006 <- summary(data_Seg2006)
mean(summary_Seg2006$Hexp)
mean(summary_Seg2006$Hobs)
t.test(summary_Seg2006$Hobs,summary_Seg2006$Hexp,paired=TRUE)
summary_Int2010 <- summary(data_Int2010)
mean(summary_Int2010$Hexp)
mean(summary_Int2010$Hobs)
t.test(summary_Int2010$Hobs,summary_Int2010$Hexp,paired=TRUE)
summary_Seg2010 <- summary(data_Seg2010)
mean(summary_Seg2010$Hexp)
mean(summary_Seg2010$Hobs)
t.test(summary_Seg2010$Hobs,summary_Seg2010$Hexp,paired=TRUE)
install.packages(knitr)
install.packages (knitr)
HWE_test_results <- HWE.test(salmon,pop=NULL,permut=TRUE,
nsim=10000,res.type="matrix")
corrected_pval <- 0.05/(11*11)
corrected_pval
HWE_logical <- HWE_test_results<corrected_pval
HWE_test_results <- HWE.test(salmon,pop=NULL,permut=TRUE,
nsim=10000,res.type="matrix")
corrected_pval <- 0.05/(11*11)
corrected_pval
HWE_logical <- HWE_test_results<corrected_pval
HWE_logical
write.table(HWE_test_results,file="HWresults.csv",sep=",",row.names=F)
pop_stats <- fstOnly(infile="class_data_genepop_new.gen",outfile="Salmon_Pairwise_Fst",
gp=3,bs_pairwise=TRUE,bootstraps=100,parallel=TRUE)
load("C:/Users/jheare/Desktop/researchproject/salmonanalysis.RData")