  +++++++++++++++++++++++++++++++++++++++++++++
   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 10:57:35 2016
         finished at Wed Jul  6 11:36:04 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 
Ancestor 1 to 2 (D_time)        Gamma  0.000000   0.100000   1.000000   0.100000 
Ancestor 1 to 2 (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)           2131202942

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         * 0 
   2 Brissago       d * 



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_x0dx
   Output file (PDF):                       outfile_x0dx.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         0.00147  0.00473  0.00683  0.00920  0.01547  0.00763  0.00808
    1  Theta_2         0.00027  0.00313  0.00503  0.00700  0.01193  0.00563  0.00594
    1  D_1->2          0.00533  0.00533  0.02700  0.04667  0.04667  0.05833  0.07739
    1  S_1->2          0.00000  0.00000  0.00333  0.26000  1.50667  0.26333  0.39478
    2  Theta_1         0.00027  0.00313  0.00503  0.00693  0.01180  0.00563  0.00590
    2  Theta_2         0.00187  0.00480  0.00683  0.00920  0.01453  0.00763  0.00807
    2  D_1->2          0.00000  0.00000  0.02633  0.05667  0.22333  0.05700  0.07593
    2  S_1->2          0.00000  0.00000  0.00333  0.24667  1.46667  0.25000  0.38263
    3  Theta_1         0.00053  0.00340  0.00517  0.00700  0.01140  0.00563  0.00586
    3  Theta_2         0.00467  0.00900  0.01203  0.01560  0.02553  0.01350  0.01428
    3  D_1->2          0.00000  0.00000  0.01900  0.03467  0.13867  0.03500  0.04344
    3  S_1->2          0.00000  0.00000  0.00333  0.14667  0.52000  0.15000  0.10798
    4  Theta_1         0.00000  0.00207  0.00383  0.00567  0.01080  0.00450  0.00485
    4  Theta_2         0.00580  0.01127  0.01383  0.01687  0.02853  0.01543  0.01624
    4  D_1->2          0.00000  0.01467  0.04033  0.06800  0.20133  0.05767  0.07308
    4  S_1->2          0.00000  0.00000  0.00333  0.19333  1.11333  0.19667  0.26516
    5  Theta_1         0.00307  0.00453  0.00723  0.01027  0.01253  0.00783  0.00816
    5  Theta_2         0.00373  0.00647  0.00817  0.01013  0.01507  0.00957  0.01034
    5  D_1->2          0.00000  0.00000  0.00633  0.02000  0.05600  0.02033  0.01783
    5  S_1->2          0.00000  0.00000  0.00333  0.11333  0.32000  0.11667  0.02610
  All  Theta_1         0.00273  0.00473  0.00597  0.00713  0.00933  0.00610  0.00607
  All  Theta_2         0.00593  0.00853  0.01010  0.01173  0.01513  0.01037  0.01046
  All  D_1->2          0.00000  0.00000  0.00033  0.02000  0.05000  0.02033  0.02270
  All  S_1->2          0.00000  0.00000  0.00333  0.10000  0.28000  0.10333  0.07536
-----------------------------------------------------------------------------------



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              -1926.03                      -1806.91               -1800.48
      2              -1932.42                      -1804.27               -1801.11
      3              -2124.71                      -1917.93               -1898.24
      4              -2508.45                      -2185.91               -2146.70
      5              -2213.62                      -1926.90               -1887.95
---------------------------------------------------------------------------------------
  All               -10721.58                      -9658.29               -9550.84
[Scaling factor = -16.358429]


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




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

Parameter           Accepted changes               Ratio
Theta_1                2089036/6250408           0.33422
Theta_2                2389541/6255166           0.38201
D_1->2                  3790404/6248321           0.60663
S_1->2                  1759745/6244215           0.28182
Genealogies            4123414/25001890           0.16492

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.19966            240207.62
  Theta_2                0.10919            279042.30
  Ln[Prob(D|P)]          0.08952            291212.11
  (*) averaged over loci.

