  +++++++++++++++++++++++++++++++++++++++++++++
   Two fake Swiss 'towns'                      
   ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +                                                                +
 +   POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY  +
 +   Bayesian inference using the structured coalescent           +
 +                                                                +
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  Compiled for a PARALLEL COMPUTER ARCHITECTURE
  One master and 4 compute nodes are available.
  Using Intel AVX (Advanced Vector Extensions)
  Compiled for a SYMMETRIC multiprocessors (GrandCentral)
  PDF output enabled [Letter-size]
  Version 4.2.7   [April-1-2016]
  Program started at   Wed Jul  6 13:35:36 2016
         finished at Wed Jul  6 14:12:40 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)        Gamma  0.000000   0.010000   0.100000   0.010000 
Population size (Theta_2)        Gamma  0.000000   0.010000   0.100000   0.010000 
Migration 2 to 1 (M)        Gamma  0.000000  500.000000 5000.00000 500.000000




Inheritance scalers in use for Thetas (specified scalars=1)
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 (with internal timer)             81608237

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 Ascona         * * 
   2 Brissago       0 * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (inc*samples*rep):              10000000
      Steps recorded (sample*rep):                     50000
   Combining over replicates:                             10
   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):              1000000

Print options:
   Data file:                                         infile
   Haplotyping is turned on:                              NO
   Output file (ASCII text):                    outfile_xx0x
   Output file (PDF):                       outfile_xx0x.pdf
   Posterior distribution:                         bayesfile
   Print data:                                            No
   Print genealogies:                                     No

Summary of data:
Title:                                Two fake Swiss 'towns'
Data file:                                            infile
Datatype:                                     Haplotype data
Number of loci:                                            5
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.24 0.26 0.27 0.22, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.25 0.24 0.26 0.25, t/t ratio=2.000]
     3         1 Felsenstein 84  [Bf:0.25 0.24 0.25 0.26, t/t ratio=2.000]
     4         1 Felsenstein 84  [Bf:0.26 0.24 0.23 0.27, t/t ratio=2.000]
     5         1 Felsenstein 84  [Bf:0.25 0.24 0.27 0.24, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     1000
     2     1000
     3     1000
     4     1000
     5     1000

Population                   Locus   Gene copies    
----------------------------------------------------
  1 Ascona                       1        10
  1                              2        10
  1                              3        10
  1                              4        10
  1                              5        10
  2 Brissago                     1        10
  2                              2        10
  2                              3        10
  2                              4        10
  2                              5        10
    Total of all populations     1        20
                                 2        20
                                 3        20
                                 4        20
                                 5        20




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

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00273  0.00527  0.00657  0.00800  0.01173  0.00743  0.00785
    1  Theta_2         0.00033  0.00327  0.00510  0.00713  0.01213  0.00577  0.00604
    1  M_2->1          0.00000  0.00000 25.00000 100.00000 280.00000 101.66667 86.05014
    2  Theta_1         0.00013  0.00307  0.00490  0.00687  0.01160  0.00550  0.00577
    2  Theta_2         0.00173  0.00507  0.00717  0.00960  0.01600  0.00803  0.00846
    2  M_2->1          0.00000  0.00000 68.33333 120.00000 326.66667 121.66667 110.33217
    3  Theta_1         0.00000  0.00253  0.00450  0.00660  0.01247  0.00530  0.00576
    3  Theta_2         0.00900  0.01173  0.01470  0.01807  0.02267  0.01590  0.01655
    3  M_2->1         130.0000 130.0000 385.0000 793.3333 793.3333 491.6667 546.5053
    4  Theta_1         0.00000  0.00187  0.00343  0.00507  0.00873  0.00397  0.00405
    4  Theta_2         0.00793  0.01127  0.01497  0.01973  0.02687  0.01663  0.01750
    4  M_2->1          0.00000  0.00000  1.66667 90.00000 250.00000 91.66667 70.59960
    5  Theta_1         0.00000  0.00333  0.00577  0.00887  0.01913  0.00737  0.00850
    5  Theta_2         0.00747  0.01133  0.01250  0.01373  0.01947  0.01350  0.01400
    5  M_2->1         210.0000 453.3333 618.3333 823.3333 1360.0000 765.0000 836.5156
  All  Theta_1         0.00207  0.00400  0.00523  0.00640  0.00867  0.00537  0.00536
  All  Theta_2         0.00807  0.01073  0.01237  0.01400  0.01753  0.01270  0.01273
  All  M_2->1           3.3333  86.6667 141.6667 193.3333 280.0000 148.3333 156.0818
-----------------------------------------------------------------------------------



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              -1959.32                      -1811.90               -1798.74
      2              -1932.91                      -1803.53               -1795.76
      3              -2092.51                      -1912.22               -1897.19
      4              -2609.40                      -2200.70               -2146.01
      5              -2137.69                      -1911.93               -1888.59
---------------------------------------------------------------------------------------
  All               -10749.73                      -9658.18               -9544.19
[Scaling factor = -17.891825]


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




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

Parameter           Accepted changes               Ratio
Theta_1                3239336/8336486           0.38857
Theta_2                2631373/8333110           0.31577
M_2->1                 2807107/8335472           0.33677
Genealogies            3581304/24994932           0.14328

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.14941            259865.67
  Theta_2                0.13199            265657.64
  M_2->1                 0.18905            232750.71
  Ln[Prob(D|P)]          0.34745            168473.48
  (*) averaged over loci.

