  =============================================
   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 18:25:24 2016
         finished at Fri Apr  1 18:48:47 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)           3873453028

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         0.00373  0.00933  0.01137  0.01340  0.02093  0.01263  0.01348
    1  Theta_2         0.00960  0.01407  0.02330  0.03113  0.04693  0.02643  0.03845
    1  M_1->2          89.3333 160.0000 215.6667 294.6667 452.6667 251.6667 285.8292
    2  Theta_1         0.00673  0.01107  0.01750  0.02813  0.04353  0.02063  0.02244
    2  Theta_2         0.01360  0.03113  0.04750  0.04827  0.05027  0.03303  0.05588
    2  M_1->2         121.3333 215.3333 246.3333 287.3333 449.3333 270.3333 282.8358
    3  Theta_1         0.00933  0.01407  0.01643  0.01847  0.02647  0.01810  0.01936
    3  Theta_2         0.01247  0.03493  0.04763  0.04880  0.05040  0.03290  0.05922
    3  M_1->2         146.0000 188.6667 272.3333 356.6667 428.6667 283.0000 301.7981
  All  Theta_1         0.00880  0.01273  0.01510  0.01767  0.02367  0.01570  0.01596
  All  Theta_2         0.01560  0.02220  0.02937  0.03507  0.05007  0.03210  0.04524
  All  M_1->2         161.3333 241.3333 256.3333 274.0000 361.3333 259.0000 262.9786
-----------------------------------------------------------------------------------



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              -2564.60                      -2285.33               -2254.25
      2               -878.95                       -780.60                -756.71
      3              -2018.93                      -1767.09               -1732.94
---------------------------------------------------------------------------------------
  All                -5442.97                      -4813.50               -4724.39
[Scaling factor = 19.512603]


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




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

Parameter           Accepted changes               Ratio
Theta_1                 183569/416205            0.44105
Theta_2                 278607/416519            0.66889
M_1->2                  170782/416827            0.40972
Genealogies             150970/1750449            0.08625

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

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.55319             18208.45
  Theta_2                0.30543             32018.27
  M_1->2                 0.53224             18477.94
  Ln[Prob(D|P)]          0.71926              9906.34
  (*) averaged over loci.





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

Individual Locus        1     2 
---------- -------- ----- ----- 
?BAG               1 0.121 0.879 
?BAG               2 0.354 0.646 
?BAG               3 1.000 0.000 
?BAG             All 1.000 0.000 
?BAJ               1 0.000 1.000 
?BAJ               2 0.217 0.783 
?BAJ               3 1.000 0.000 
?BAJ             All 0.201 0.799 
?BAH               1 0.773 0.227 
?BAH               2 0.136 0.864 
?BAH               3 1.000 0.000 
?BAH             All 1.000 0.000 
?BAI               1 0.674 0.326 
?BAI               2 0.646 0.354 
?BAI               3 0.923 0.077 
?BAI             All 0.978 0.022 
?BAF               1 0.652 0.348 
?BAF               2 0.783 0.217 
?BAF               3 0.735 0.265 
?BAF             All 0.949 0.051 
