--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Thu May 10 01:42:45 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_N2/NS2B_1/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2B_1/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_N2/NS2B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -3451.65         -3496.60
2      -3450.42         -3497.60
--------------------------------------
TOTAL    -3450.86         -3497.22
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2B_1/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_N2/NS2B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         7.692794    0.385136    6.378891    8.815663    7.674388    728.93    870.12    1.000
r(A<->C){all}   0.063142    0.000161    0.039494    0.089352    0.062701    818.70    844.26    1.000
r(A<->G){all}   0.202987    0.000566    0.159866    0.251700    0.201726    634.74    659.61    1.002
r(A<->T){all}   0.059348    0.000150    0.035472    0.083629    0.058885    843.76    878.86    1.001
r(C<->G){all}   0.056813    0.000170    0.033409    0.083628    0.056158    622.28    629.85    1.000
r(C<->T){all}   0.602481    0.000947    0.542096    0.661560    0.602807    584.63    599.68    1.001
r(G<->T){all}   0.015229    0.000061    0.000955    0.030334    0.014028    826.84    831.91    1.000
pi(A){all}      0.326883    0.000254    0.296281    0.358824    0.326672    903.61    973.12    1.000
pi(C){all}      0.218763    0.000175    0.194054    0.245715    0.218259    972.81   1016.68    1.000
pi(G){all}      0.235769    0.000224    0.206766    0.265327    0.235499    749.72    901.39    1.001
pi(T){all}      0.218584    0.000186    0.190875    0.244032    0.218287    699.16    831.67    1.000
alpha{1,2}      0.254998    0.000742    0.205995    0.310829    0.253050   1063.62   1221.53    1.000
alpha{3}        3.372293    0.642241    1.993634    5.006694    3.278331   1191.74   1197.66    1.001
pinvar{all}     0.083321    0.000954    0.027060    0.145304    0.081817   1267.73   1275.58    1.002
------------------------------------------------------------------------------------------------------
* 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	-3196.495324
Model 2: PositiveSelection	-3196.495331
Model 0: one-ratio	-3198.09903
Model 3: discrete	-3168.586784
Model 7: beta	-3170.076891
Model 8: beta&w>1	-3170.078161


Model 0 vs 1	3.2074119999997492

Model 2 vs 1	1.4000000192027073E-5

Model 8 vs 7	0.002539999999498832