--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Dec 09 15:39:34 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/442/Zasp52-PO/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/442/Zasp52-PO/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/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -4429.58         -4446.88
2      -4429.19         -4449.46
--------------------------------------
TOTAL    -4429.36         -4448.84
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/442/Zasp52-PO/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/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.490928    0.001681    0.409289    0.569859    0.488647   1397.38   1449.19    1.003
r(A<->C){all}   0.072325    0.000206    0.046225    0.101196    0.071326   1086.98   1141.22    1.000
r(A<->G){all}   0.191314    0.000709    0.139169    0.243381    0.189736    757.98    862.99    1.000
r(A<->T){all}   0.121191    0.000679    0.073249    0.173290    0.120338    863.77    932.14    1.000
r(C<->G){all}   0.081162    0.000180    0.055855    0.106875    0.080233   1149.07   1164.61    1.002
r(C<->T){all}   0.402499    0.001215    0.339444    0.470477    0.401971    688.38    823.29    1.000
r(G<->T){all}   0.131508    0.000509    0.088987    0.176486    0.130156   1093.12   1186.95    1.000
pi(A){all}      0.228705    0.000102    0.209213    0.248199    0.228809    965.54    987.69    1.000
pi(C){all}      0.330878    0.000128    0.309456    0.353419    0.330651   1018.58   1057.53    1.000
pi(G){all}      0.281176    0.000115    0.259907    0.301322    0.281029   1228.47   1288.53    1.000
pi(T){all}      0.159241    0.000075    0.142489    0.175641    0.159172   1153.99   1179.67    1.000
alpha{1,2}      0.172634    0.001204    0.105866    0.240656    0.169998    922.04   1051.61    1.000
alpha{3}        2.342114    0.643136    1.048182    3.976436    2.218016   1229.01   1299.43    1.000
pinvar{all}     0.533031    0.002406    0.439992    0.626908    0.535956   1147.23   1177.07    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	-3834.780134
Model 2: PositiveSelection	-3834.780134
Model 0: one-ratio	-3904.716809
Model 3: discrete	-3833.849026
Model 7: beta	-3835.214203
Model 8: beta&w>1	-3833.846982


Model 0 vs 1	139.87334999999985

Model 2 vs 1	0.0

Model 8 vs 7	2.7344419999999445