--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sat Nov 12 01:59:30 WET 2016
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=CLUSTALW2
tcoffee.params=
tcoffee.maxSeqs=0
codeml.bin=codeml
mrbayes.tburnin=2500
codeml.dir=
input.sequences=
mrbayes.pburnin=2500
mrbayes.bin=mb_adops
tcoffee.bin=t_coffee_ADOPS
mrbayes.dir=/usr/bin/
tcoffee.dir=
tcoffee.minScore=3
input.fasta=/opt/ADOPS/2/Abl-PF/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/Abl-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p":
(Use the harmonic mean for Bayes factor comparisons of models)
(Values are saved to the file /opt/ADOPS/2/Abl-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -16006.35 -16023.21
2 -16006.70 -16022.03
--------------------------------------
TOTAL -16006.51 -16022.79
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/Abl-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p":
Summaries are based on a total of 3002 samples from 2 runs.
Each run produced 2001 samples of which 1501 samples were included.
Parameter summaries saved to file "/opt/ADOPS/2/Abl-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.759278 0.000949 0.699319 0.820956 0.758721 1452.19 1476.60 1.001
r(A<->C){all} 0.079685 0.000058 0.065024 0.094261 0.079534 1057.59 1073.19 1.000
r(A<->G){all} 0.237207 0.000210 0.210218 0.265446 0.236852 774.75 884.73 1.000
r(A<->T){all} 0.161568 0.000240 0.131281 0.191564 0.161092 909.76 1023.88 1.000
r(C<->G){all} 0.041153 0.000022 0.032017 0.049777 0.040898 1002.44 1018.22 1.001
r(C<->T){all} 0.380654 0.000319 0.346335 0.414703 0.380276 794.30 922.71 1.000
r(G<->T){all} 0.099733 0.000101 0.079975 0.119570 0.099582 1076.19 1081.15 1.000
pi(A){all} 0.236055 0.000031 0.225413 0.247055 0.236116 782.88 975.57 1.002
pi(C){all} 0.322015 0.000036 0.309558 0.332872 0.322031 724.73 809.82 1.000
pi(G){all} 0.280280 0.000035 0.268877 0.292018 0.280279 877.14 924.88 1.001
pi(T){all} 0.161649 0.000020 0.152979 0.170390 0.161517 1043.48 1052.41 1.000
alpha{1,2} 0.139083 0.000083 0.120920 0.156043 0.138737 1276.74 1283.02 1.000
alpha{3} 6.524392 1.522409 4.459491 9.187657 6.419408 1354.40 1397.88 1.000
pinvar{all} 0.387064 0.000425 0.346930 0.425734 0.387377 1214.50 1272.34 1.000
------------------------------------------------------------------------------------------------------
* Convergence diagnostic (ESS = Estimated Sample Size); min and avg values
correspond to minimal and average ESS among runs.
ESS value below 100 may indicate that the parameter is undersampled.
+ Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman
and Rubin, 1992) should approach 1.0 as runs converge.
Setting sumt conformat to Simple
--- CODEML SUMMARY
Model 1: NearlyNeutral -14473.408666
Model 2: PositiveSelection -14473.408666
Model 0: one-ratio -14596.637491
Model 3: discrete -14465.913145
Model 7: beta -14466.662107
Model 8: beta&w>1 -14466.160768
Model 0 vs 1 246.45765000000029
Model 2 vs 1 0.0
Model 8 vs 7 1.002678000000742
