Nothing
This vignette will introduce you to find the critical value for comparison of observed and expected obtained last step.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(mixIndependR) library(ggplot2)
x <- mixexample p <- AlleleFreq(x) h <-Heterozygous(x) H <- RxpHetero(h,p,HWE=FALSE) AS<-AlleleShare(x,replacement=FALSE) e <-RealProAlleleShare(AS) ObsDist_K<-FreqHetero(h) ExpDist_K<- DistHetero(H) ObsDist_X<-FreqAlleleShare(AS) ExpDist_X<-DistAlleleShare(e)
Simulate_DistK
and Simulate_DistX
simulate bundles of expected distributions for number of heterozygous loci and number of shared alleles respectively.
Simu_K <- Simulate_DistK(H,100,500) Simu_X <- Simulate_DistX(e,100,500)
Dist_SimuChisq
generates a bundle of chi-square values which can be distributed. ecdf
build the cumulative probability functions for the chi-square values.
x2_K<-Dist_SimuChisq(Simu_K,ExpDist_K$Density,200) x2_X<-Dist_SimuChisq(Simu_X,ExpDist_X$Density,200) P1<-ecdf(x2_K) P2<-ecdf(x2_X)
x <- c(0:200) dfX2 <- data.frame(x=x,y=P1(x)) ggplot(dfX2,aes(x=x,y=P1(x)))+ geom_line()+ geom_hline(yintercept = 0.95,color="Red")+ ggtitle("CPF No. of Heterozygous Loci")+ xlab("Chi-square")+ylab("1-p-value") dfX22 <- data.frame(x=x,y=P2(x)) ggplot(dfX22,aes(x=x,y=P2(x)))+ geom_line()+ geom_hline(yintercept = 0.95,color="Red")+ ggtitle("CPF No. of Shared Alleles")+ xlab("Chi-square")+ylab("1-p-value")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.