  =============================================
   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 17:52:29 2016
         finished at Fri Apr  1 18:14:37 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)            861656255

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         * 0 
   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
   Output file (PDF):              outfile-twoswisstowns.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         0.00393  0.00767  0.01417  0.01793  0.02160  0.01410  0.01476
    1  Theta_2         0.00347  0.00573  0.01150  0.02853  0.04747  0.02090  0.03071
    1  M_1->2          28.6667  57.3333 129.6667 263.3333 441.3333 205.6667 307.2651
    2  Theta_1         0.00953  0.01873  0.01897  0.01907  0.03680  0.02143  0.02289
    2  Theta_2         0.00813  0.02940  0.03383  0.04347  0.04960  0.02963  0.05287
    2  M_1->2         189.3333 378.0000 447.0000 486.0000 510.0000 384.3333 615.8802
    3  Theta_1         0.01020  0.01860  0.01943  0.02067  0.03813  0.02210  0.02342
    3  Theta_2         0.00893  0.02960  0.04117  0.04673  0.04973  0.02990  0.05288
    3  M_1->2         160.6667 346.0000 396.3333 480.0000 507.3333 357.0000 559.3969
  All  Theta_1         0.01227  0.01560  0.01810  0.02080  0.02533  0.01863  0.01880
  All  Theta_2         0.00993  0.01327  0.01803  0.02920  0.04140  0.02557  0.03515
  All  M_1->2         178.0000 327.3333 389.0000 480.0000 510.6667 349.6667 444.6046
-----------------------------------------------------------------------------------



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              -2540.80                      -2280.46               -2254.74
      2               -864.85                       -768.83                -750.69
      3              -2007.81                      -1760.59               -1729.97
---------------------------------------------------------------------------------------
  All                -5410.87                      -4807.29               -4732.82
[Scaling factor = 2.583049]


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




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

Parameter           Accepted changes               Ratio
Theta_1                 192808/415926            0.46356
Theta_2                 298168/416499            0.71589
M_1->2                  245386/417327            0.58799
Genealogies             135750/1750248            0.07756

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

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.49134             21030.27
  Theta_2                0.23062             37667.77
  M_1->2                 0.38790             26672.18
  Ln[Prob(D|P)]          0.66126             12254.29
  (*) averaged over loci.





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

Individual Locus        1     2 
---------- -------- ----- ----- 
?BAG               1 0.703 0.297 
?BAG               2 0.000 1.000 
?BAG               3 0.342 0.658 
?BAG             All 0.000 1.000 
?BAJ               1 0.000 1.000 
?BAJ               2 0.846 0.154 
?BAJ               3 1.000 0.000 
?BAJ             All 0.843 0.157 
?BAH               1 0.000 1.000 
?BAH               2 0.843 0.157 
?BAH               3 0.000 1.000 
?BAH             All 0.000 1.000 
?BAI               1 0.443 0.557 
?BAI               2 0.154 0.846 
?BAI               3 0.762 0.238 
?BAI             All 0.317 0.683 
?BAF               1 0.602 0.398 
?BAF               2 0.311 0.689 
?BAF               3 1.000 0.000 
?BAF             All 1.000 0.000 
