  =============================================
                                               
  =============================================
  MIGRATION RATE AND POPULATION SIZE ESTIMATION
  using Markov Chain Monte Carlo simulation
  =============================================
  Version debug 4.2.7

  Program started at Mon Apr 11 10:10:19 2016
         finished at Mon Apr 11 10:54:49 2016
     


Options in use:
---------------

Analysis strategy is BAYESIAN INFERENCE

Proposal distribution:
Parameter group          Proposal type
-----------------------  -------------------
Population size (Theta)       Slice sampling
Migration rate      (M)       Slice 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 
Population size (Theta_3)      Uniform  0.000000   0.050000   0.100000   0.010000 
Population size (Theta_4)      Uniform  0.000000   0.050000   0.100000   0.010000 
Migration 2 to 1 (M)      Uniform  0.000000  5000.00000 10000.0000 1000.00000
Migration 3 to 1 (M)      Uniform  0.000000  5000.00000 10000.0000 1000.00000



Datatype: DNA sequence data

Inheritance scalers in use for Thetas (specified scalars=1)
1.00 1.00 1.00 1.00 1.00 
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 (from parmfile)            310705631

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 Romanshorn     m a b a 
   2 Arbon_1        a m a b 
   3 Kreuzlinge     b a m a 
   4 Frauenfeld     a b a m 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (inc*samples*rep):                 10000
      Steps recorded (sample*rep):                      1000
   Combining over replicates:                              2
   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):                 5000

Print options:
   Data file:                        infile.xabaaxabbaxaabax
   Haplotyping is turned on:                              NO
   Output file (ASCII text):       outfile-xabaaxabbaxaabax1
   Output file (PDF):          outfile-xabaaxabbaxaabax1.pdf
   Posterior distribution:                         bayesfile
   Print data:                                            No
   Print genealogies:                                     No

Summary of data:
Title:                                                      
Data file:                           infile.xabaaxabbaxaabax
Datatype:                                      Sequence data
Number of loci:                                           10
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.24 0.25 0.25 0.25, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.23 0.26 0.27 0.24, t/t ratio=2.000]
     3         1 Felsenstein 84  [Bf:0.25 0.25 0.24 0.26, t/t ratio=2.000]
     4         1 Felsenstein 84  [Bf:0.25 0.25 0.23 0.28, t/t ratio=2.000]
     5         1 Felsenstein 84  [Bf:0.23 0.27 0.25 0.25, t/t ratio=2.000]
     6         1 Felsenstein 84  [Bf:0.27 0.25 0.25 0.24, t/t ratio=2.000]
     7         1 Felsenstein 84  [Bf:0.27 0.26 0.23 0.24, t/t ratio=2.000]
     8         1 Felsenstein 84  [Bf:0.22 0.24 0.28 0.25, t/t ratio=2.000]
     9         1 Felsenstein 84  [Bf:0.26 0.26 0.24 0.24, t/t ratio=2.000]
    10         1 Felsenstein 84  [Bf:0.25 0.25 0.24 0.25, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     1000
     2     1000
     3     1000
     4     1000
     5     1000
     6     1000
     7     1000
     8     1000
     9     1000
    10     1000

Population                   Locus   Gene copies    
----------------------------------------------------
  1 Romanshorn_0                 1        20
  1                              2        20
  1                              3        20
  1                              4        20
  1                              5        20
  1                              6        20
  1                              7        20
  1                              8        20
  1                              9        20
  1                             10        20
  2 Arbon_1                      1        20
  2                              2        20
  2                              3        20
  2                              4        20
  2                              5        20
  2                              6        20
  2                              7        20
  2                              8        20
  2                              9        20
  2                             10        20
  3 Kreuzlingen_2                1        20
  3                              2        20
  3                              3        20
  3                              4        20
  3                              5        20
  3                              6        20
  3                              7        20
  3                              8        20
  3                              9        20
  3                             10        20
  4 Frauenfeld_3                 1        20
  4                              2        20
  4                              3        20
  4                              4        20
  4                              5        20
  4                              6        20
  4                              7        20
  4                              8        20
  4                              9        20
  4                             10        20
    Total of all populations     1        80
                                 2        80
                                 3        80
                                 4        80
                                 5        80
                                 6        80
                                 7        80
                                 8        80
                                 9        80
                                10        80




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

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00740  0.01000  0.01270  0.01660  0.02560  0.01590  0.01626
    1  M_2->1           620.00   840.00  1030.00  1180.00  1400.00  1050.00  1028.97
    1  M_3->1           600.00   840.00  1030.00  1200.00  1440.00  1050.00  1023.69
    2  Theta_1         0.00920  0.01280  0.01550  0.01800  0.02300  0.01610  0.01602
    2  M_2->1         380.0000 620.0000 810.0000 960.0000 1200.0000 830.0000 807.4272
    2  M_3->1         460.0000 720.0000 910.0000 1080.0000 1320.0000 930.0000 909.2718
    3  Theta_1         0.01520  0.01920  0.02190  0.02600  0.03340  0.02370  0.02405
    3  M_2->1           620.00   860.00  1050.00  1220.00  1460.00  1070.00  1049.71
    3  M_3->1         500.0000 740.0000 930.0000 1100.0000 1340.0000 950.0000 922.9666
    4  Theta_1         0.01080  0.01480  0.01790  0.02120  0.02840  0.01910  0.01932
    4  M_2->1         480.0000 760.0000 970.0000 1140.0000 1440.0000 990.0000 962.9758
    4  M_3->1           480.00   740.00  1010.00  1180.00  1620.00  1030.00  1027.30
    5  Theta_1         0.01020  0.01420  0.01690  0.02120  0.02840  0.01890  0.01913
    5  M_2->1           520.00   820.00  1030.00  1220.00  1540.00  1050.00  1038.25
    5  M_3->1           540.00   840.00  1050.00  1240.00  1560.00  1070.00  1054.80
    6  Theta_1         0.00880  0.01140  0.01450  0.01800  0.03000  0.01650  0.01773
    6  M_2->1         460.0000 680.0000 870.0000 1020.0000 1260.0000 890.0000 866.3087
    6  M_3->1         480.0000 760.0000 970.0000 1140.0000 1420.0000 990.0000 964.6701
    7  Theta_1         0.00780  0.01280  0.01570  0.01840  0.02280  0.01590  0.01567
    7  M_2->1         400.0000 640.0000 830.0000 980.0000 1220.0000 850.0000 821.2035
    7  M_3->1         400.0000 700.0000 890.0000 1080.0000 1380.0000 910.0000 894.6038
    8  Theta_1         0.00500  0.00820  0.01050  0.01280  0.01820  0.01110  0.01128
    8  M_2->1         400.0000 720.0000 950.0000 1140.0000 1460.0000 970.0000 943.6122
    8  M_3->1         400.0000 680.0000 870.0000 1040.0000 1300.0000 890.0000 866.0241
    9  Theta_1         0.00840  0.01100  0.01410  0.01840  0.02720  0.01670  0.01729
    9  M_2->1         420.0000 700.0000 890.0000 1060.0000 1340.0000 910.0000 888.8332
    9  M_3->1         440.0000 760.0000 950.0000 1160.0000 1460.0000 990.0000 957.2104
   10  Theta_1         0.01100  0.01640  0.01730  0.01840  0.02420  0.01790  0.01800
   10  M_2->1           540.00   820.00  1010.00  1180.00  1460.00  1030.00  1010.72
   10  M_3->1         460.0000 720.0000 910.0000 1080.0000 1320.0000 930.0000 910.1078
  All  Theta_1         0.01000  0.01300  0.01550  0.01720  0.02120  0.01570  0.01553
  All  M_2->1         520.0000 720.0000 910.0000 1060.0000 1260.0000 930.0000 905.8347
  All  M_3->1         520.0000 740.0000 930.0000 1080.0000 1300.0000 950.0000 921.0925
-----------------------------------------------------------------------------------



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              -4399.32                      -3013.17               -2893.56
      2              -5929.42                      -3745.10               -3496.08
      3              -6722.37                      -4068.67               -3787.70
      4              -5899.07                      -3836.16               -3730.01
      5              -6996.16                      -3901.59               -3453.86
      6              -3991.33                      -3036.29               -3002.89
      7              -4297.26                      -3091.05               -3147.09
      8              -4572.08                      -3212.24               -3135.26
      9              -5911.57                      -4157.84               -4147.74
     10              -6017.21                      -3781.00               -4116.14
---------------------------------------------------------------------------------------
  All               -54667.21                     -35774.55              -34841.74
[Scaling factor = 68.581592]


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




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

Parameter           Accepted changes               Ratio
Theta_1                   3098/3098              1.00000
Theta_2                   3071/3071              1.00000
Theta_3                   3203/3203              1.00000
Theta_4                   3033/3033              1.00000
M_2->1                    3213/3213              1.00000
M_3->1                    3141/3141              1.00000
M_4->1                    3175/3175              1.00000
M_1->2                    3125/3125              1.00000
M_3->2                    3105/3105              1.00000
M_4->2                    3165/3165              1.00000
M_1->3                    3139/3139              1.00000
M_2->3                    3164/3164              1.00000
M_4->3                    3093/3093              1.00000
M_1->4                    3154/3154              1.00000
M_2->4                    3128/3128              1.00000
M_3->4                    2998/2998              1.00000
Genealogies               2677/49995              0.05355

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

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1=1 [m]       0.84878            819.62
  Theta_2=1 [m]       0.84878            819.62
  Theta_3=1 [m]       0.84878            819.62
  Theta_4=1 [m]       0.84878            819.62
  M_(2,1) [a]              0.90624               493.23
  M_(3,1) [b]              0.85034               818.21
  M_(4,1) [a]              0.90624               493.23
  M_(1,2) [a]              0.90624               493.23
  M_(3,2) [a]              0.90624               493.23
  M_(4,2) [b]              0.85034               818.21
  M_(1,3) [b]              0.85034               818.21
  M_(2,3) [a]              0.90624               493.23
  M_(4,3) [a]              0.90624               493.23
  M_(1,4) [a]              0.90624               493.23
  M_(2,4) [b]              0.85034               818.21
  M_(3,4) [a]              0.90624               493.23
  Ln[Prob(D|P)]          0.98822                60.05
  (*) averaged over loci.

