Partial Correlations
Data
load("~/Desktop/DiversityRecombObjects.Robj")
library(ppcor)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
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## filter, lag
##
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(cowplot)## Loading required package: ggplot2
##
## Attaching package: 'cowplot'
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## The following object is masked from 'package:ggplot2':
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## ggsaveteoPi=data.frame(teoPi)
colnames(teoPi)=colnames(maizePi)
maizePi=na.omit(maizePi)
teoPi=na.omit(teoPi)
maizeSingle=na.omit(maizeSingle)
teoSingle=na.omit(teoSingle)Function
par_cor<-function(x,y,z,taxa,type){
xres=lm(x~z)$residuals
yres=lm(y~z)$residuals
bob=data.frame(xres,yres) %>% mutate(taxa,type)
colnames(bob)=c("diversity","recombination","taxa","type")
#bob.plot=ggplot(bob,aes(x=xres,y=yres))+geom_smooth()+xlab(xlab)+ylab(ylab)+ggtitle(taxa)
#return(list(bob,bob.plot))
return(bob)
}Partial Cors \(\pi\) teo
res<-data.frame()
test_teoPi=data.frame(teoPi$tP,teoPi$CmPerMb,teoPi$gene_no_500kb)
pcor(test_teoPi,method="spearman")$estimate## teoPi.tP teoPi.CmPerMb teoPi.gene_no_500kb
## teoPi.tP 1.00000000 0.04367917 0.04704015
## teoPi.CmPerMb 0.04367917 1.00000000 0.81932888
## teoPi.gene_no_500kb 0.04704015 0.81932888 1.00000000pcor(test_teoPi,method="spearman")$p.value## teoPi.tP teoPi.CmPerMb teoPi.gene_no_500kb
## teoPi.tP 0 0 0
## teoPi.CmPerMb 0 0 0
## teoPi.gene_no_500kb 0 0 0pcor(test_teoPi,method="pearson")$estimate## teoPi.tP teoPi.CmPerMb teoPi.gene_no_500kb
## teoPi.tP 1.00000000 -0.0250708 0.09878174
## teoPi.CmPerMb -0.02507080 1.0000000 0.70296208
## teoPi.gene_no_500kb 0.09878174 0.7029621 1.00000000pcor(test_teoPi,method="pearson")$p.value## teoPi.tP teoPi.CmPerMb teoPi.gene_no_500kb
## teoPi.tP 0.000000e+00 1.356132e-111 0
## teoPi.CmPerMb 1.356132e-111 0.000000e+00 0
## teoPi.gene_no_500kb 0.000000e+00 0.000000e+00 0res=par_cor(teoPi$CmPerMb,teoPi$tP,teoPi$gene_no_500kb,"teo","pi")Partial Cors \(\pi\) maize
test_maizePi=data.frame(maizePi$tP,maizePi$CmPerMb,maizePi$gene_no_500kb)
pcor(test_maizePi,method="spearman")$estimate## maizePi.tP maizePi.CmPerMb maizePi.gene_no_500kb
## maizePi.tP 1.00000000 0.04247499 0.07913287
## maizePi.CmPerMb 0.04247499 1.00000000 0.81269637
## maizePi.gene_no_500kb 0.07913287 0.81269637 1.00000000pcor(test_maizePi,method="spearman")$p.value## maizePi.tP maizePi.CmPerMb maizePi.gene_no_500kb
## maizePi.tP 0 0 0
## maizePi.CmPerMb 0 0 0
## maizePi.gene_no_500kb 0 0 0pcor(test_maizePi,method="pearson")$estimate## maizePi.tP maizePi.CmPerMb maizePi.gene_no_500kb
## maizePi.tP 1.0000000000 -0.0001001366 0.1206627
## maizePi.CmPerMb -0.0001001366 1.0000000000 0.6899455
## maizePi.gene_no_500kb 0.1206626595 0.6899454869 1.0000000pcor(test_maizePi,method="pearson")$p.value## maizePi.tP maizePi.CmPerMb maizePi.gene_no_500kb
## maizePi.tP 0.0000000 0.9236404 0
## maizePi.CmPerMb 0.9236404 0.0000000 0
## maizePi.gene_no_500kb 0.0000000 0.0000000 0res=rbind(res,par_cor(maizePi$CmPerMb,maizePi$tP,maizePi$gene_no_500kb,"maize","pi"))Partial Cors \(\eta_1\) teo
test_teoSingle=data.frame(teoSingle$tF,teoSingle$CmPerMb,teoSingle$gene_no_500kb)
pcor(test_teoSingle,method="spearman")$estimate## teoSingle.tF teoSingle.CmPerMb
## teoSingle.tF 1.000000000 0.01732321
## teoSingle.CmPerMb 0.017323208 1.00000000
## teoSingle.gene_no_500kb 0.002736886 0.83054261
## teoSingle.gene_no_500kb
## teoSingle.tF 0.002736886
## teoSingle.CmPerMb 0.830542610
## teoSingle.gene_no_500kb 1.000000000pcor(test_teoSingle,method="spearman")$p.value## teoSingle.tF teoSingle.CmPerMb
## teoSingle.tF 0.000000e+00 3.899812e-43
## teoSingle.CmPerMb 3.899812e-43 0.000000e+00
## teoSingle.gene_no_500kb 2.962360e-02 0.000000e+00
## teoSingle.gene_no_500kb
## teoSingle.tF 0.0296236
## teoSingle.CmPerMb 0.0000000
## teoSingle.gene_no_500kb 0.0000000pcor(test_teoSingle,method="pearson")$estimate## teoSingle.tF teoSingle.CmPerMb
## teoSingle.tF 1.00000000 -0.03276168
## teoSingle.CmPerMb -0.03276168 1.00000000
## teoSingle.gene_no_500kb 0.03073661 0.71066159
## teoSingle.gene_no_500kb
## teoSingle.tF 0.03073661
## teoSingle.CmPerMb 0.71066159
## teoSingle.gene_no_500kb 1.00000000pcor(test_teoSingle,method="pearson")$p.value## teoSingle.tF teoSingle.CmPerMb
## teoSingle.tF 0.000000e+00 1.324496e-149
## teoSingle.CmPerMb 1.324496e-149 0.000000e+00
## teoSingle.gene_no_500kb 6.623908e-132 0.000000e+00
## teoSingle.gene_no_500kb
## teoSingle.tF 6.623908e-132
## teoSingle.CmPerMb 0.000000e+00
## teoSingle.gene_no_500kb 0.000000e+00res=rbind(res,par_cor(teoSingle$CmPerMb,teoSingle$tF,teoSingle$gene_no_500kb,"teo","single"))Partial Cors \(\eta_1\) maize
test_maizeSingle=data.frame(maizeSingle$tF,maizeSingle$CmPerMb,maizeSingle$gene_no_500kb)
pcor(test_maizeSingle,method="spearman")$estimate## maizeSingle.tF maizeSingle.CmPerMb
## maizeSingle.tF 1.00000000 0.02031323
## maizeSingle.CmPerMb 0.02031323 1.00000000
## maizeSingle.gene_no_500kb 0.01616088 0.82026485
## maizeSingle.gene_no_500kb
## maizeSingle.tF 0.01616088
## maizeSingle.CmPerMb 0.82026485
## maizeSingle.gene_no_500kb 1.00000000pcor(test_maizeSingle,method="spearman")$p.value## maizeSingle.tF maizeSingle.CmPerMb
## maizeSingle.tF 0.000000e+00 2.063631e-73
## maizeSingle.CmPerMb 2.063631e-73 0.000000e+00
## maizeSingle.gene_no_500kb 3.987090e-47 0.000000e+00
## maizeSingle.gene_no_500kb
## maizeSingle.tF 3.98709e-47
## maizeSingle.CmPerMb 0.00000e+00
## maizeSingle.gene_no_500kb 0.00000e+00pcor(test_maizeSingle,method="pearson")$estimate## maizeSingle.tF maizeSingle.CmPerMb
## maizeSingle.tF 1.00000000 -0.01774765
## maizeSingle.CmPerMb -0.01774765 1.00000000
## maizeSingle.gene_no_500kb 0.04018700 0.69570391
## maizeSingle.gene_no_500kb
## maizeSingle.tF 0.0401870
## maizeSingle.CmPerMb 0.6957039
## maizeSingle.gene_no_500kb 1.0000000pcor(test_maizeSingle,method="pearson")$p.value## maizeSingle.tF maizeSingle.CmPerMb
## maizeSingle.tF 0.000000e+00 1.810992e-56
## maizeSingle.CmPerMb 1.810992e-56 0.000000e+00
## maizeSingle.gene_no_500kb 6.898197e-282 0.000000e+00
## maizeSingle.gene_no_500kb
## maizeSingle.tF 6.898197e-282
## maizeSingle.CmPerMb 0.000000e+00
## maizeSingle.gene_no_500kb 0.000000e+00res=rbind(res,par_cor(maizeSingle$CmPerMb,maizeSingle$tF,maizeSingle$gene_no_500kb,"maize","single"))ggplot(res,aes(x=recombination,y=diversity,color=taxa,linetype=type))+geom_smooth(size=1)## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.