  +++++++++++++++++++++++++++++++++++++++++++++
   Two fake Swiss 'towns'                      
   ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +                                                                +
 +   POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY  +
 +   Bayesian inference using the structured coalescent           +
 +                                                                +
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  Compiled for a PARALLEL COMPUTER ARCHITECTURE
  One master and 4 compute nodes are available.
  Using Intel AVX (Advanced Vector Extensions)
  Compiled for a SYMMETRIC multiprocessors (GrandCentral)
  PDF output enabled [Letter-size]
  Version 4.2.7   [April-1-2016]
  Program started at   Wed Jul  6 12:12:30 2016
         finished at Wed Jul  6 12:54:13 2016
     


Options in use:
---------------

Analysis strategy is BAYESIAN INFERENCE

Proposal distribution:
Parameter group          Proposal type
-----------------------  -------------------
Population size (Theta)  Metropolis sampling
Migration rate      (M)  Metropolis sampling


Prior distribution (Proposal-delta will be tuned to acceptance frequence 0.440000):
Parameter group            Prior type   Minimum    Mean(*)    Maximum    Delta
-------------------------  ------------ ---------- ---------- ---------- ----------
Population size (Theta_1)        Gamma  0.000000   0.010000   0.100000   0.010000 
Population size (Theta_2)        Gamma  0.000000   0.010000   0.100000   0.010000 
Migration 2 to 1 (M)        Gamma  0.000000  500.000000 5000.00000 500.000000
Ancestor 2 to 1 (D_time)        Gamma  0.000000   0.100000   1.000000   0.100000 
Ancestor 2 to 1 (S_time)        Gamma  0.000000   1.000000  10.000000   1.000000 




Inheritance scalers in use for Thetas (specified scalars=1)
1.00 1.00 1.00 1.00 1.00 

[Each Theta uses the (true) inheritance scalar of the first locus as a reference]


Pseudo-random number generator: Mersenne-Twister                                
Random number seed (with internal timer)            401562519

Start parameters:
   First genealogy was started using a random tree
   Start parameter values were generated
Connection matrix:
m = average (average over a group of Thetas or M,
s = symmetric migration M, S = symmetric 4Nm,
0 = zero, and not estimated,
* = migration free to vary, Thetas are on diagonal
d = row population split off column population
D = split and then migration
   1 Ascona         * D 
   2 Brissago       0 * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (inc*samples*rep):              10000000
      Steps recorded (sample*rep):                     50000
   Combining over replicates:                             10
   Static heating scheme
      4 chains with  temperatures
       1.00, 1.50, 3.00,1000000.00
      Swapping interval is 1
   Burn-in per replicate (samples*inc):              1000000

Print options:
   Data file:                                         infile
   Haplotyping is turned on:                              NO
   Output file (ASCII text):                    outfile_xB0x
   Output file (PDF):                       outfile_xB0x.pdf
   Posterior distribution:                         bayesfile
   Print data:                                            No
   Print genealogies:                                     No

Summary of data:
Title:                                Two fake Swiss 'towns'
Data file:                                            infile
Datatype:                                     Haplotype data
Number of loci:                                            5
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.24 0.26 0.27 0.22, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.25 0.24 0.26 0.25, t/t ratio=2.000]
     3         1 Felsenstein 84  [Bf:0.25 0.24 0.25 0.26, t/t ratio=2.000]
     4         1 Felsenstein 84  [Bf:0.26 0.24 0.23 0.27, t/t ratio=2.000]
     5         1 Felsenstein 84  [Bf:0.25 0.24 0.27 0.24, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     1000
     2     1000
     3     1000
     4     1000
     5     1000

Population                   Locus   Gene copies    
----------------------------------------------------
  1 Ascona                       1        10
  1                              2        10
  1                              3        10
  1                              4        10
  1                              5        10
  2 Brissago                     1        10
  2                              2        10
  2                              3        10
  2                              4        10
  2                              5        10
    Total of all populations     1        20
                                 2        20
                                 3        20
                                 4        20
                                 5        20




Bayesian estimates
==================

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00280  0.00587  0.00677  0.00760  0.01207  0.00757  0.00802
    1  Theta_2         0.00027  0.00313  0.00497  0.00693  0.01160  0.00557  0.00583
    1  M_2->1          0.00000  0.00000  1.66667 63.33333 193.33333 65.00000 48.03696
    1  D_2->1          0.00000  0.00000  0.02833  0.06333  0.24067  0.06367  0.08383
    1  S_2->1          0.00000  0.00000  0.00333  0.32000  1.98667  0.32333  0.53776
    2  Theta_1         0.00013  0.00307  0.00490  0.00687  0.01167  0.00550  0.00581
    2  Theta_2         0.00273  0.00507  0.00717  0.00960  0.01340  0.00803  0.00844
    2  M_2->1          0.00000  0.00000 55.00000 116.66667 323.33333 118.33333 109.35155
    2  D_2->1          0.00000  0.00000  0.03033  0.06933  0.06933  0.07767  0.10029
    2  S_2->1          0.00000  0.00000  0.00333  0.69333  2.89333  0.69667  0.99385
    3  Theta_1         0.00000  0.00253  0.00450  0.00660  0.01227  0.00530  0.00573
    3  Theta_2         0.00840  0.01127  0.01470  0.01873  0.02400  0.01590  0.01654
    3  M_2->1         126.6667 223.3333 381.6667 590.0000 786.6667 488.3333 541.8216
    3  D_2->1          0.00733  0.00733  0.03033  0.05467  0.05467  0.07767  0.10063
    3  S_2->1          0.00000  0.00000  0.00333  0.70000  2.87333  0.70333  1.00120
    4  Theta_1         0.00000  0.00193  0.00350  0.00513  0.00880  0.00397  0.00409
    4  Theta_2         0.00707  0.01360  0.01490  0.01633  0.02967  0.01670  0.01755
    4  M_2->1          0.00000  0.00000  1.66667 86.66667 246.66667 88.33333 69.02154
    4  D_2->1          0.00733  0.00733  0.03033  0.05467  0.05467  0.07833  0.10114
    4  S_2->1          0.00000  0.00000  0.00333  0.69333  2.85333  0.69667  0.98368
    5  Theta_1         0.00000  0.00340  0.00583  0.00893  0.01900  0.00743  0.00854
    5  Theta_2         0.00733  0.00993  0.01257  0.01553  0.01987  0.01357  0.01403
    5  M_2->1         196.6667 416.6667 628.3333 903.3333 1406.6667 765.0000 836.9091
    5  D_2->1          0.00000  0.00000  0.02900  0.07600  0.27533  0.07633  0.09931
    5  S_2->1          0.00000  0.00000  0.00333  0.69333  2.83333  0.69667  0.98597
  All  Theta_1         0.00207  0.00407  0.00530  0.00647  0.00867  0.00543  0.00542
  All  Theta_2         0.00793  0.01060  0.01217  0.01387  0.01747  0.01250  0.01259
  All  M_2->1           0.0000  63.3333 118.3333 173.3333 330.0000 131.6667 210.9592
  All  D_2->1          0.00000  0.00000  0.00033  0.06533  0.24200  0.06567  0.09015
  All  S_2->1          0.00000  0.00000  0.00333  0.34667  1.96000  0.35000  0.56142
-----------------------------------------------------------------------------------



Log-Probability of the data given the model (marginal likelihood = log(P(D|thisModel))
--------------------------------------------------------------------
[Use this value for Bayes factor calculations:
BF = Exp[log(P(D|thisModel) - log(P(D|otherModel)]
shows the support for thisModel]



Locus      Raw Thermodynamic score(1a)  Bezier approximated score(1b)      Harmonic mean(2)
------------------------------------------------------------------------------------------
      1              -1958.75                      -1811.96               -1800.88
      2              -1932.04                      -1803.41               -1796.87
      3              -2091.79                      -1912.06               -1897.39
      4              -2607.11                      -2200.38               -2143.42
      5              -2137.10                      -1911.95               -1888.00
---------------------------------------------------------------------------------------
  All               -10745.60                      -9658.59               -9545.38
[Scaling factor = -18.826367]


MCMC run characteristics
========================




Acceptance ratios for all parameters and the genealogies
---------------------------------------------------------------------

Parameter           Accepted changes               Ratio
Theta_1                1949217/4997000           0.39008
Theta_2                1576576/4998047           0.31544
M_2->1                 1587949/4999228           0.31764
D_2->1                  4818588/4998212           0.96406
S_2->1                  4484484/5001129           0.89669
Genealogies            3577216/25006384           0.14305

Autocorrelation and Effective sample size
-------------------------------------------------------------------

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.15192            259268.91
  Theta_2                0.13923            261710.10
  M_2->1                 0.19948            228042.09
  Ln[Prob(D|P)]          0.00313            346716.42
  (*) averaged over loci.

