--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue May 01 12:19:02 WEST 2018
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=MUSCLE
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/ADOPS1/DNG_N1/E_3/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_3/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/ADOPS1/DNG_N1/E_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -12117.99        -12163.23
2     -12120.14        -12164.05
--------------------------------------
TOTAL   -12118.57        -12163.72
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_3/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/ADOPS1/DNG_N1/E_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         8.883683    0.303010    7.784483    9.935343    8.870359    476.73    572.78    1.000
r(A<->C){all}   0.046526    0.000030    0.035833    0.057214    0.046335    917.78    940.50    1.000
r(A<->G){all}   0.193069    0.000151    0.169862    0.217268    0.192885    578.52    675.40    1.000
r(A<->T){all}   0.045094    0.000031    0.035173    0.056808    0.045002    997.44   1060.23    1.000
r(C<->G){all}   0.018233    0.000021    0.009446    0.027433    0.018057    614.75    767.36    1.000
r(C<->T){all}   0.664697    0.000239    0.631770    0.692145    0.665073    552.96    555.92    1.000
r(G<->T){all}   0.032381    0.000033    0.021609    0.043769    0.032138    775.72    856.26    1.000
pi(A){all}      0.343073    0.000069    0.326207    0.358382    0.343094    683.82    980.92    1.000
pi(C){all}      0.214483    0.000048    0.200844    0.227913    0.214231    818.70    829.95    1.000
pi(G){all}      0.244550    0.000059    0.228715    0.258513    0.244632    646.25    751.17    1.000
pi(T){all}      0.197895    0.000043    0.184897    0.210508    0.197931    711.05    866.14    1.000
alpha{1,2}      0.196855    0.000106    0.177159    0.216743    0.196524   1230.72   1259.27    1.000
alpha{3}        4.179512    0.547246    2.772413    5.593344    4.091450   1346.03   1367.73    1.001
pinvar{all}     0.087943    0.000315    0.052177    0.121557    0.087222   1084.47   1087.20    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	-11404.48168
Model 2: PositiveSelection	-11404.48168
Model 0: one-ratio	-11439.671203
Model 3: discrete	-11263.455063
Model 7: beta	-11265.175861
Model 8: beta&w>1	-11265.180711


Model 0 vs 1	70.3790459999982

Model 2 vs 1	0.0

Model 8 vs 7	0.009700000002339948