  =============================================
   two (fake) Swiss towns                      
  =============================================
  MIGRATION RATE AND POPULATION SIZE ESTIMATION
  using Markov Chain Monte Carlo simulation
  =============================================
  Version debug 4.2.7

  Program started at Fri Apr  1 17:21:10 2016
         finished at Fri Apr  1 17:43:10 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)      Uniform  0.000000   0.050000   0.100000   0.010000 
Population size (Theta_2)      Uniform  0.000000   0.050000   0.100000   0.010000 
Migration 1 to 2 (M)      Uniform  0.000000  500.000000 1000.00000 100.000000




Inheritance scalers in use for Thetas (specified scalars=1)
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)            930706661

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 Aadorf         * c 
   2 Bern           * * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (inc*samples*rep):               1000000
      Steps recorded (sample*rep):                     20000
   Combining over replicates:                              4
   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):               250000

Print options:
   Data file:                                  twoswisstowns
   Haplotyping is turned on:                              NO
   Output file (ASCII text):         outfile-twoswisstowns_c
   Output file (PDF):            outfile-twoswisstowns_c.pdf
   Posterior distribution:                         bayesfile
   Print data:                                            No
   Print genealogies:                                     No

Summary of data:
Title:                                two (fake) Swiss towns
Data file:                                     twoswisstowns
Datatype:                                     Haplotype data
Number of loci:                                            3
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.26 0.27 0.22 0.25, t/t ratio=2.000]
     1         2 Felsenstein 84  [Bf:0.24 0.27 0.22 0.27, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.26 0.23 0.25 0.25, t/t ratio=2.000]
     3         1 Felsenstein 84  [Bf:0.26 0.24 0.24 0.26, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     500 500
     2     300
     3     700

Population                   Locus   Gene copies    
----------------------------------------------------
  1 Aadorf                       1        10
  1 Aadorf                       1        10
  1                              2        10
  1                              3        10
  2 Bern                         1        10
  2 Bern                         1        10
  2                              2        10
  2                              3        10
    Total of all populations     1        40
                                 2        20
                                 3        20




Bayesian estimates
==================

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00000  0.01133  0.01730  0.02400  0.03980  0.01723  0.01742
    1  Theta_2         0.00127  0.00267  0.00697  0.02007  0.04460  0.01450  0.02418
    1  M_1->2          34.0000  67.3333 123.0000 214.0000 458.0000 204.3333 331.5924
    2  Theta_1         0.00940  0.01127  0.02143  0.03833  0.04527  0.02363  0.02515
    2  Theta_2         0.00613  0.00973  0.02130  0.03113  0.04873  0.02590  0.04757
    2  M_1->2         144.0000 348.6667 448.3333 489.3333 508.0000 361.6667 604.8222
    3  Theta_1         0.00907  0.00987  0.01657  0.02727  0.02933  0.01883  0.01998
    3  Theta_2         0.01307  0.03660  0.04697  0.04880  0.05073  0.03457  0.06084
    3  M_1->2          95.3333 281.3333 374.3333 438.0000 513.3333 335.0000 487.6906
  All  Theta_1         0.01253  0.01540  0.01783  0.02093  0.02540  0.01883  0.01907
  All  Theta_2         0.00273  0.01427  0.02030  0.02880  0.04813  0.02323  0.03623
  All  M_1->2         132.6667 275.3333 377.6667 420.0000 509.3333 331.6667 417.4700
-----------------------------------------------------------------------------------



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              -2533.33                      -2279.80               -2253.47
      2               -867.66                       -770.11                -750.36
      3              -2006.54                      -1760.03               -1730.04
---------------------------------------------------------------------------------------
  All                -5390.50                      -4792.91               -4716.83
[Scaling factor = 17.035171]


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




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

Parameter           Accepted changes               Ratio
Theta_1                 208930/417564            0.50035
Theta_2                 289295/417626            0.69271
M_1->2                  240912/415627            0.57964
Genealogies             139952/1749183            0.08001

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

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.50701             20592.52
  Theta_2                0.25041             36791.94
  M_1->2                 0.44129             23399.50
  Ln[Prob(D|P)]          0.65237             12636.04
  (*) averaged over loci.





Assignment of Individuals to populations
========================================

Individual Locus        1     2 
---------- -------- ----- ----- 
?BAG               1 0.000 1.000 
?BAG               2 0.125 0.875 
?BAG               3 0.734 0.266 
?BAG             All 0.000 1.000 
?BAJ               1 0.239 0.761 
?BAJ               2 1.000 0.000 
?BAJ               3 0.330 0.670 
?BAJ             All 1.000 0.000 
?BAH               1 0.668 0.332 
?BAH               2 0.829 0.171 
?BAH               3 0.330 0.670 
?BAH             All 0.827 0.173 
?BAI               1 0.668 0.332 
?BAI               2 0.703 0.297 
?BAI               3 0.266 0.734 
?BAI             All 0.633 0.367 
?BAF               1 0.779 0.221 
?BAF               2 0.875 0.125 
?BAF               3 0.064 0.936 
?BAF             All 0.627 0.373 
