  +++++++++++++++++++++++++++++++++++++++++++++
   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 14:14:45 2016
         finished at Wed Jul  6 14:56:49 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
Migration 1 to 2 (M)        Gamma  0.000000  500.000000 5000.00000 500.000000




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)           1700814969

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         * * 
   2 Brissago       * * 



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_xxxx
   Output file (PDF):                       outfile_xxxx.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  0.00573  0.00677  0.00773  0.01193  0.00757  0.00801
    1  Theta_2         0.00140  0.00300  0.00483  0.00673  0.00893  0.00537  0.00566
    1  M_2->1          0.00000  0.00000  1.66667 100.00000 336.66667 101.66667 104.94110
    1  M_1->2          0.00000  0.00000  1.66667 116.66667 376.66667 118.33333 124.06498
    2  Theta_1         0.00193  0.00213  0.00563  0.01020  0.01047  0.00637  0.00675
    2  Theta_2         0.00207  0.00207  0.00590  0.01093  0.01093  0.00670  0.00712
    2  M_2->1          0.00000  0.00000 41.66667 136.66667 410.00000 138.33333 142.67410
    2  M_1->2          0.00000  0.00000 85.00000 156.66667 463.33333 158.33333 168.08519
    3  Theta_1         0.00053  0.00353  0.00543  0.00747  0.01260  0.00603  0.00635
    3  Theta_2         0.00387  0.00833  0.01157  0.01540  0.02653  0.01323  0.01419
    3  M_2->1         13.33333 13.33333 85.00000 136.66667 136.66667 171.66667 196.39028
    3  M_1->2           0.0000  86.6667 185.0000 286.6667 573.3333 228.3333 243.8030
    4  Theta_1         0.00000  0.00247  0.00443  0.00660  0.01260  0.00530  0.00577
    4  Theta_2         0.00600  0.01313  0.01363  0.01407  0.02633  0.01530  0.01616
    4  M_2->1          0.00000  0.00000 78.33333 136.66667 400.00000 138.33333 139.03401
    4  M_1->2          0.00000  0.00000  1.66667 96.66667 286.66667 98.33333 87.34742
    5  Theta_1         0.00267  0.00607  0.00743  0.00893  0.01440  0.00837  0.00887
    5  Theta_2         0.00133  0.00500  0.00743  0.01033  0.01867  0.00863  0.00929
    5  M_2->1           0.0000  70.0000 178.3333 303.3333 673.3333 248.3333 273.1487
    5  M_1->2         100.0000 100.0000 321.6667 650.0000 650.0000 391.6667 422.6792
  All  Theta_1         0.00307  0.00513  0.00643  0.00767  0.01013  0.00657  0.00658
  All  Theta_2         0.00500  0.00753  0.00917  0.01073  0.01420  0.00943  0.00953
  All  M_2->1           0.0000  50.0000 108.3333 156.6667 246.6667 118.3333 113.1683
  All  M_1->2           0.0000  83.3333 145.0000 196.6667 286.6667 151.6667 147.4915
-----------------------------------------------------------------------------------



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              -1996.88                      -1817.61               -1802.05
      2              -1958.61                      -1807.26               -1795.08
      3              -2103.29                      -1913.48               -1897.05
      4              -2657.57                      -2208.55               -2143.87
      5              -2137.53                      -1911.23               -1888.75
---------------------------------------------------------------------------------------
  All               -10864.72                      -9668.98               -9537.64
[Scaling factor = -10.842324]


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




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

Parameter           Accepted changes               Ratio
Theta_1                2201781/6249439           0.35232
Theta_2                2332615/6253867           0.37299
M_2->1                 1815527/6251274           0.29043
M_1->2                 1828565/6246817           0.29272
Genealogies            3537069/24998603           0.14149

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.24193            222038.58
  Theta_2                0.16625            250306.26
  M_2->1                 0.44770            133249.94
  M_1->2                 0.42095            148379.56
  Ln[Prob(D|P)]          0.34319            169692.09
  (*) averaged over loci.

