--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Nov 22 08:45:27 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/3/acj6-PI/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PI/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/3/acj6-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -2266.17 -2284.86
2 -2265.95 -2284.74
--------------------------------------
TOTAL -2266.06 -2284.80
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PI/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/3/acj6-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.388850 0.003261 0.282135 0.497652 0.384365 1312.29 1397.93 1.000
r(A<->C){all} 0.118751 0.001152 0.060245 0.190239 0.115789 848.97 932.21 1.000
r(A<->G){all} 0.247701 0.002851 0.148755 0.355528 0.245181 471.46 516.19 1.000
r(A<->T){all} 0.111127 0.001423 0.041648 0.184680 0.107305 777.09 799.93 1.000
r(C<->G){all} 0.065652 0.000415 0.029946 0.107426 0.063244 844.44 944.36 1.000
r(C<->T){all} 0.445810 0.003740 0.322958 0.563454 0.446044 672.17 746.40 1.000
r(G<->T){all} 0.010958 0.000105 0.000022 0.031156 0.007902 782.15 813.64 1.000
pi(A){all} 0.241819 0.000149 0.217424 0.265722 0.241292 1266.11 1281.69 1.000
pi(C){all} 0.306077 0.000175 0.280298 0.331841 0.306253 1162.05 1280.55 1.000
pi(G){all} 0.270149 0.000173 0.244127 0.295796 0.270271 1262.34 1311.24 1.000
pi(T){all} 0.181955 0.000121 0.160300 0.202828 0.181911 1185.48 1279.24 1.000
alpha{1,2} 0.045390 0.000683 0.000112 0.086819 0.047038 1048.01 1140.40 1.000
alpha{3} 2.455511 0.675468 1.067352 4.100930 2.337238 1389.62 1445.31 1.000
pinvar{all} 0.746716 0.000690 0.694248 0.797080 0.747571 1285.60 1323.85 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 -2168.428845
Model 2: PositiveSelection -2168.426242
Model 0: one-ratio -2168.474962
Model 3: discrete -2168.426242
Model 7: beta -2168.42576
Model 8: beta&w>1 -2168.428362
Model 0 vs 1 0.0922339999997348
Model 2 vs 1 0.005205999999816413
Model 8 vs 7 0.005204000000048836
