Post date: May 29, 2015 8:39:21 PM
#Used script splitPops.pl, changed second regular expression to: m/^(E\-BS\-[CW]\-[A-Z0-9]+)/;
perl splitPops.pl sosorum.gl
#It makes a pop*gl file for each population.
perl wrap_qsub_rc_popmod.pl /labs/evolution/projects/sosorum/pop_*gl
cd /local/scratch/
sleep 5
/labs/evolution/projects/sosorum/popmod -i /labs/evolution/projects/sosorum/pop_E-BS-W-UP.gl -n 15000 -b 5000 -t 4 -o popaf_E-BS-W-UP_chain1.hdf5
scp popaf_E-BS-W-UP_chain1.hdf5 /labs/evolution/projects/sosorum/
rm popaf_E-BS-W-UP_chain1.hdf5
#15000 MCMC steps, 5000 burn-in, save every 4th step, 2 chains for each pop
pbs 415182-415199
#popmod cite: Gompert et al. 2015 (moleco)
#30may15
#files from the model are called:
pC_EL<-read.table("p_popaf_E-BS-C-EL.txt",sep=",",header=F)
pC_F1<-read.table("p_popaf_E-BS-C-F1.txt",sep=",",header=F)
pC_F2<-read.table("p_popaf_E-BS-C-F2.txt",sep=",",header=F)
pC_F3<-read.table("p_popaf_E-BS-C-F3.txt",sep=",",header=F)
pW_CS<-read.table("p_popaf_E-BS-W-CS.txt",sep=",",header=F)
pW_EL<-read.table("p_popaf_E-BS-W-EL.txt",sep=",",header=F)
pW_PO<-read.table("p_popaf_E-BS-W-PO.txt",sep=",",header=F)
pW_SG<-read.table("p_popaf_E-BS-W-SG.txt",sep=",",header=F)
pW_UP<-read.table("p_popaf_E-BS-W-UP.txt",sep=",",header=F)
#We compared allele frequencies and gsts between: sample allele freq, sample allelel freq high cov, bayesian allele freq, bayesian allele freq high cov. We decided bayesian allele freq high cov are the best estimates. We do need to drop estimates for W-UP and W-SG -- the allele freq CIs are too giant. We are using the point estimates from the posterior distributions, not using all the MCMC steps.
#See R code to get Gsts: sosorum_gst_30may15.txt