  +++++++++++++++++++++++++++++++++++++++++++++
   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 11:36:04 2016
         finished at Wed Jul  6 12:12:30 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 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)           3924661789

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       * * 



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_x0xx
   Output file (PDF):                       outfile_x0xx.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         0.00300  0.00493  0.00717  0.00973  0.01280  0.00797  0.00843
    1  Theta_2         0.00007  0.00287  0.00470  0.00653  0.01100  0.00523  0.00549
    1  M_1->2          0.00000  0.00000 51.66667 113.33333 306.66667 115.00000 100.88738
    2  Theta_1         0.00113  0.00440  0.00657  0.00893  0.01533  0.00737  0.00782
    2  Theta_2         0.00020  0.00333  0.00537  0.00760  0.01333  0.00617  0.00655
    2  M_1->2           0.0000  33.3333 111.6667 180.0000 396.6667 151.6667 149.2407
    3  Theta_1         0.00233  0.00493  0.00610  0.00733  0.01093  0.00683  0.00715
    3  Theta_2         0.00547  0.00953  0.01137  0.01347  0.02100  0.01310  0.01410
    3  M_1->2           0.0000  60.0000 141.6667 223.3333 446.6667 178.3333 182.5091
    4  Theta_1         0.00153  0.00233  0.00523  0.00947  0.01107  0.00670  0.00730
    4  Theta_2         0.00380  0.01007  0.01203  0.01413  0.02627  0.01350  0.01429
    4  M_1->2          0.00000  0.00000  1.66667 80.00000 213.33333 81.66667 53.21960
    5  Theta_1         0.00427  0.00667  0.00897  0.01140  0.01520  0.00977  0.01023
    5  Theta_2         0.00000  0.00287  0.00690  0.01273  0.02960  0.00817  0.00893
    5  M_1->2          60.0000  63.3333 231.6667 500.0000 503.3333 325.0000 367.8533
  All  Theta_1         0.00400  0.00627  0.00763  0.00900  0.01167  0.00783  0.00784
  All  Theta_2         0.00433  0.00680  0.00830  0.00987  0.01327  0.00863  0.00874
  All  M_1->2           0.0000  60.0000 115.0000 160.0000 230.0000 121.6667 113.9777
-----------------------------------------------------------------------------------



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              -1959.03                      -1811.48               -1801.41
      2              -1930.93                      -1802.86               -1795.32
      3              -2086.81                      -1910.81               -1895.91
      4              -2598.02                      -2199.97               -2147.00
      5              -2131.00                      -1910.55               -1887.29
---------------------------------------------------------------------------------------
  All               -10718.98                      -9648.88               -9540.14
[Scaling factor = -13.207137]


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




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

Parameter           Accepted changes               Ratio
Theta_1                2913532/8339437           0.34937
Theta_2                3301611/8335693           0.39608
M_1->2                 2647218/8332093           0.31771
Genealogies            3914647/24992777           0.15663

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.24258            222859.62
  Theta_2                0.14504            263598.69
  M_1->2                 0.22707            227602.16
  Ln[Prob(D|P)]          0.32671            177755.38
  (*) averaged over loci.

