  =============================================
   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 19:39:42 2016
         finished at Fri Apr  1 20:04:48 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)           3502792550

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         0.00280  0.01113  0.01510  0.01933  0.02640  0.01543  0.01588
    1  Theta_2         0.00253  0.00600  0.01143  0.02220  0.04847  0.02090  0.03203
    1  M_1->2          48.6667  98.0000 125.0000 204.0000 478.6667 227.6667 362.5230
    2  Theta_1         0.01293  0.01793  0.02230  0.02740  0.03687  0.02483  0.02663
    2  Theta_2         0.00620  0.01933  0.03423  0.03633  0.04960  0.02777  0.05144
    2  M_1->2         204.0000 394.6667 478.3333 493.3333 514.6667 397.0000 678.4120
    3  Theta_1         0.00907  0.01487  0.01883  0.02380  0.03660  0.02083  0.02188
    3  Theta_2         0.01140  0.03287  0.04770  0.04907  0.05060  0.03357  0.06056
    3  M_1->2         170.0000 346.6667 448.3333 486.0000 507.3333 362.3333 563.4210
  All  Theta_1         0.01287  0.01780  0.01863  0.01933  0.02587  0.01923  0.01948
  All  Theta_2         0.00700  0.01827  0.02417  0.03620  0.05000  0.02797  0.04163
  All  M_1->2         196.6667 351.3333 431.0000 492.0000 510.6667 364.3333 507.8432
-----------------------------------------------------------------------------------



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              -2552.10                      -2282.42               -2254.09
      2               -866.03                       -769.99                -751.09
      3              -2017.90                      -1762.00               -1728.85
---------------------------------------------------------------------------------------
  All                -5419.13                      -4797.51               -4717.13
[Scaling factor = 16.904579]


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




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

Parameter           Accepted changes               Ratio
Theta_1                 180717/415635            0.43480
Theta_2                 304274/415691            0.73197
M_1->2                  258582/417529            0.61932
Genealogies             133696/1751145            0.07635

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

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.46396             22702.93
  Theta_2                0.23107             37778.56
  M_1->2                 0.37685             27579.40
  Ln[Prob(D|P)]          0.65299             12622.87
  (*) averaged over loci.





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

Individual Locus        1     2 
---------- -------- ----- ----- 
?BAG               1 0.169 0.831 
?BAG               2 0.295 0.705 
?BAG               3 0.079 0.921 
?BAG             All 0.007 0.993 
?BAJ               1 0.169 0.831 
?BAJ               2 0.295 0.705 
?BAJ               3 0.855 0.145 
?BAJ             All 0.334 0.666 
?BAH               1 0.721 0.279 
?BAH               2 0.157 0.843 
?BAH               3 0.776 0.224 
?BAH             All 0.625 0.375 
?BAI               1 0.169 0.831 
?BAI               2 0.843 0.157 
?BAI               3 1.000 0.000 
?BAI             All 1.000 0.000 
?BAF               1 0.439 0.561 
?BAF               2 0.843 0.157 
?BAF               3 0.855 0.145 
?BAF             All 0.961 0.039 
