--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sat Nov 12 00:08:17 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-PC/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/Abl-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-PC/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-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -15913.10 -15929.96
2 -15913.53 -15929.47
--------------------------------------
TOTAL -15913.29 -15929.74
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/Abl-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-PC/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-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.770011 0.000953 0.707533 0.828221 0.770083 1066.51 1192.69 1.000
r(A<->C){all} 0.079183 0.000059 0.064729 0.094475 0.079094 985.14 1049.48 1.000
r(A<->G){all} 0.236979 0.000208 0.207519 0.264152 0.236600 843.70 859.95 1.000
r(A<->T){all} 0.162665 0.000220 0.131940 0.189568 0.162883 987.80 1050.79 1.000
r(C<->G){all} 0.040559 0.000021 0.032002 0.049149 0.040454 1196.45 1262.56 1.000
r(C<->T){all} 0.380886 0.000295 0.348373 0.414955 0.380532 966.36 970.91 1.000
r(G<->T){all} 0.099728 0.000105 0.078698 0.118478 0.099605 1019.57 1037.15 1.000
pi(A){all} 0.235234 0.000031 0.224172 0.246446 0.235224 895.34 954.20 1.000
pi(C){all} 0.322726 0.000038 0.311028 0.335076 0.322724 1133.55 1162.92 1.000
pi(G){all} 0.281071 0.000034 0.269485 0.292600 0.280997 1039.38 1157.56 1.000
pi(T){all} 0.160969 0.000022 0.152278 0.169986 0.160996 998.60 1001.36 1.001
alpha{1,2} 0.138353 0.000081 0.121559 0.156293 0.138242 1305.13 1329.49 1.000
alpha{3} 6.621457 1.679684 4.393521 9.423974 6.468258 1336.94 1418.97 1.000
pinvar{all} 0.378687 0.000472 0.336223 0.423385 0.379360 1172.41 1336.70 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 -14384.726652
Model 2: PositiveSelection -14384.726652
Model 0: one-ratio -14506.88723
Model 3: discrete -14377.03339
Model 7: beta -14377.783013
Model 8: beta&w>1 -14377.285318
Model 0 vs 1 244.3211560000018
Model 2 vs 1 0.0
Model 8 vs 7 0.9953900000000431
