--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed Dec 07 01:13:18 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/392/siz-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/392/siz-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/392/siz-PA/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/392/siz-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -12215.93 -12233.52
2 -12215.74 -12231.31
--------------------------------------
TOTAL -12215.83 -12232.93
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/392/siz-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/392/siz-PA/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/392/siz-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.998814 0.002019 0.917484 1.088333 0.998345 1386.09 1439.27 1.000
r(A<->C){all} 0.076987 0.000081 0.059959 0.094817 0.076816 1064.57 1095.72 1.000
r(A<->G){all} 0.320435 0.000353 0.285635 0.359721 0.320281 537.00 658.98 1.000
r(A<->T){all} 0.125807 0.000222 0.096568 0.155043 0.125674 786.40 935.38 1.000
r(C<->G){all} 0.037935 0.000026 0.028391 0.048192 0.037693 1282.15 1289.31 1.001
r(C<->T){all} 0.371904 0.000386 0.335464 0.411050 0.371772 728.77 766.72 1.001
r(G<->T){all} 0.066932 0.000082 0.049681 0.084788 0.066557 940.32 957.48 1.000
pi(A){all} 0.220503 0.000036 0.209546 0.233141 0.220378 907.21 916.97 1.000
pi(C){all} 0.320193 0.000046 0.306399 0.332667 0.320108 1045.22 1067.96 1.000
pi(G){all} 0.283606 0.000044 0.271170 0.296875 0.283425 1066.14 1121.15 1.000
pi(T){all} 0.175698 0.000027 0.165009 0.185195 0.175764 1254.45 1280.05 1.000
alpha{1,2} 0.098086 0.000041 0.085151 0.109881 0.097992 1252.30 1310.81 1.001
alpha{3} 6.415497 1.605262 4.168278 9.013655 6.317827 1265.43 1383.21 1.000
pinvar{all} 0.430361 0.000376 0.391258 0.467989 0.430386 1387.93 1444.47 1.001
------------------------------------------------------------------------------------------------------
* 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 -10796.774173
Model 2: PositiveSelection -10796.774182
Model 0: one-ratio -10840.67573
Model 3: discrete -10788.685025
Model 7: beta -10790.382782
Model 8: beta&w>1 -10788.821357
Model 0 vs 1 87.8031140000021
Model 2 vs 1 1.799999881768599E-5
Model 8 vs 7 3.122849999999744
