  +++++++++++++++++++++++++++++++++++++++++++++
   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 10:13:44 2016
         finished at Wed Jul  6 10:57:35 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 
Migration 1 to 2 (M)        Gamma  0.000000  500.000000 5000.00000 500.000000
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)           1035898451

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_x0Bx
   Output file (PDF):                       outfile_x0Bx.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         0.00160  0.00493  0.00703  0.00947  0.01580  0.00790  0.00829
    1  Theta_2         0.00007  0.00293  0.00470  0.00660  0.01120  0.00530  0.00557
    1  M_1->2          0.00000  0.00000  1.66667 80.00000 260.00000 81.66667 76.45285
    1  D_1->2          0.00000  0.00000  0.02767  0.06867  0.25800  0.06900  0.09111
    1  S_1->2          0.00000  0.00000  0.00333  0.46000  2.44667  0.46333  0.73311
    2  Theta_1         0.00107  0.00440  0.00650  0.00893  0.01513  0.00730  0.00774
    2  Theta_2         0.00000  0.00493  0.00543  0.00587  0.02193  0.00623  0.00663
    2  M_1->2          0.00000 16.66667 98.33333 163.33333 400.00000 145.00000 145.79914
    2  D_1->2          0.00000  0.00000  0.02967  0.07600  0.27600  0.07633  0.09930
    2  S_1->2          0.00000  0.00000  0.00333  0.66000  2.82000  0.66333  0.95696
    3  Theta_1         0.00193  0.00500  0.00617  0.00740  0.01193  0.00690  0.00725
    3  Theta_2         0.00407  0.00820  0.01137  0.01533  0.02527  0.01310  0.01408
    3  M_1->2          20.0000  20.0000 141.6667 256.6667 256.6667 178.3333 184.2546
    3  D_1->2          0.00000  0.00000  0.02967  0.06867  0.06867  0.07700  0.09976
    3  S_1->2          0.00000  0.00000  0.00333  0.64667  2.81333  0.65000  0.94045
    4  Theta_1         0.00067  0.00340  0.00583  0.00880  0.01420  0.00717  0.00768
    4  Theta_2         0.00367  0.00840  0.01163  0.01533  0.02573  0.01317  0.01394
    4  M_1->2          0.00000  0.00000  1.66667 76.66667 210.00000 78.33333 51.26253
    4  D_1->2          0.00733  0.00733  0.03233  0.06200  0.06200  0.07700  0.09985
    4  S_1->2          0.00000  0.00000  0.00333  0.63333  2.74667  0.63667  0.91858
    5  Theta_1         0.00400  0.00753  0.00863  0.00967  0.01493  0.00950  0.00996
    5  Theta_2         0.00167  0.00453  0.00697  0.01000  0.01660  0.00830  0.00910
    5  M_1->2          43.3333 110.0000 191.6667 293.3333 416.6667 291.6667 336.8125
    5  D_1->2          0.00400  0.00400  0.02900  0.05400  0.05400  0.07500  0.09787
    5  S_1->2          0.00000  0.00000  0.00333  0.61333  2.73333  0.61667  0.90057
  All  Theta_1         0.00400  0.00627  0.00763  0.00900  0.01167  0.00783  0.00781
  All  Theta_2         0.00440  0.00680  0.00837  0.00993  0.01333  0.00870  0.00877
  All  M_1->2          0.00000 43.33333 95.00000 140.00000 213.33333 105.00000 97.25348
  All  D_1->2          0.00000  0.00000  0.00033  0.06733  0.25067  0.06767  0.09302
  All  S_1->2          0.00000  0.00000  0.11667  0.35333  2.00667  0.35667  0.57276
-----------------------------------------------------------------------------------



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              -1957.74                      -1811.46               -1799.38
      2              -1929.28                      -1802.61               -1797.07
      3              -2085.92                      -1910.80               -1898.20
      4              -2594.63                      -2199.50               -2144.82
      5              -2130.24                      -1910.47               -1887.96
---------------------------------------------------------------------------------------
  All               -10710.71                      -9647.73               -9540.32
[Scaling factor = -12.898369]


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




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

Parameter           Accepted changes               Ratio
Theta_1                1758221/5000501           0.35161
Theta_2                1995556/4996817           0.39937
M_1->2                 1512560/5003172           0.30232
D_1->2                  4793781/4997120           0.95931
S_1->2                  4384938/5001717           0.87669
Genealogies            3903848/25000673           0.15615

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.25338            216387.36
  Theta_2                0.14532            263553.17
  M_1->2                 0.27171            201403.21
  Ln[Prob(D|P)]          0.00466            347406.95
  (*) averaged over loci.

