  +++++++++++++++++++++++++++++++++++++++++++++
   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 12:55:45 2016
         finished at Wed Jul  6 13:35:35 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 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)           3731481896

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_xd0x
   Output file (PDF):                       outfile_xd0x.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  0.00553  0.00703  0.00867  0.01287  0.00790  0.00835
    1  Theta_2         0.00027  0.00313  0.00497  0.00687  0.01147  0.00550  0.00577
    1  D_2->1          0.00000  0.00000  0.02767  0.05867  0.22533  0.05900  0.07752
    1  S_2->1          0.00000  0.00000  0.00333  0.26000  1.52000  0.26333  0.40249
    2  Theta_1         0.00033  0.00333  0.00523  0.00733  0.01253  0.00590  0.00625
    2  Theta_2         0.00000  0.00500  0.00710  0.00947  0.02420  0.00790  0.00831
    2  D_2->1          0.00000  0.00000  0.02500  0.05733  0.22400  0.05767  0.07607
    2  S_2->1          0.00000  0.00000  0.00333  0.26000  1.52000  0.26333  0.39838
    3  Theta_1         0.00033  0.00340  0.00537  0.00753  0.01313  0.00610  0.00647
    3  Theta_2         0.00693  0.01147  0.01443  0.01820  0.02773  0.01597  0.01664
    3  D_2->1          0.00000  0.00000  0.00033  0.02467  0.12667  0.02500  0.03174
    3  S_2->1          0.00000  0.00000  0.00333  0.10667  0.34000  0.11000  0.09038
    4  Theta_1         0.00000  0.00207  0.00363  0.00533  0.00907  0.00417  0.00427
    4  Theta_2         0.00713  0.01273  0.01470  0.01693  0.02807  0.01630  0.01704
    4  D_2->1          0.00000  0.00333  0.03433  0.06600  0.23000  0.06233  0.08119
    4  S_2->1          0.00000  0.00000  0.00333  0.27333  1.57333  0.27667  0.40658
    5  Theta_1         0.00100  0.00427  0.00670  0.00967  0.02007  0.00817  0.00920
    5  Theta_2         0.00740  0.01027  0.01317  0.01580  0.01907  0.01357  0.01394
    5  D_2->1          0.00000  0.00000  0.00033  0.01200  0.03333  0.01233  0.00364
    5  S_2->1          0.00000  0.00000  0.00333  0.09333  0.26667  0.09667  0.00401
  All  Theta_1         0.00260  0.00467  0.00590  0.00707  0.00933  0.00603  0.00599
  All  Theta_2         0.00787  0.01053  0.01217  0.01380  0.01720  0.01243  0.01245
  All  D_2->1          0.00000  0.00000  0.00033  0.00867  0.02867  0.00900  0.00946
  All  S_2->1          0.00000  0.00000  0.00333  0.08000  0.24000  0.08333  0.05180
-----------------------------------------------------------------------------------



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              -1926.14                      -1806.87               -1799.16
      2              -1932.26                      -1803.88               -1796.35
      3              -2131.40                      -1925.01               -1903.09
      4              -2508.29                      -2184.73               -2145.34
      5              -2220.36                      -1936.49               -1890.68
---------------------------------------------------------------------------------------
  All               -10735.56                      -9674.08               -9551.73
[Scaling factor = -17.104631]


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




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

Parameter           Accepted changes               Ratio
Theta_1                2153725/6249140           0.34464
Theta_2                1853849/6249944           0.29662
D_2->1                  3576121/6248542           0.57231
S_2->1                  1913609/6252126           0.30607
Genealogies            3640362/25000248           0.14561

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.21025            229038.43
  Theta_2                0.18858            234586.16
  Ln[Prob(D|P)]          0.18107            250190.53
  (*) averaged over loci.

