 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
ombined                                                           
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +                                                                +
 +   POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY  +
 +   Bayesian inference using the structured coalescent           +
 +                                                                +
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  Compiled for a SYMMETRIC multiprocessors (GrandCentral)
  PDF output enabled [Letter-size]
  Version 4.2.8   [June-24-2016]
  Program started at   Tue Jul 26 16:44:33 2016
         finished at Tue Jul 26 16:51:40 2016
                          


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

Analysis strategy is BAYESIAN INFERENCE

Proposal distribution:
Parameter group          Proposal type
-----------------------  -------------------
Population size (Theta)       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 




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)           3921600642

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 urchins        * 



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):                     10000
   Combining over replicates:                              2
   Static heating scheme
      4 chains with  temperatures
       1.00, 1.50, 3.00,100000.00
      Swapping interval is 1
   Burn-in per replicate (samples*inc):              1000000

Print options:
   Data file:                                     infile.gap
   Haplotyping is turned on: YES: NO report of haplotype pro
   Output file (ASCII text):                    outfile-gap0
   Output file (PDF):                       outfile-gap0.pdf
   Posterior distribution:                         bayesfile
   All values of Post.Dist:                bayesallfile-gap0
   Print data:                                            No
   Print genealogies:                                     No

Summary of data:
Title:                                               ombined
Data file:                                        infile.gap
Datatype:                                     Haplotype data
Number of loci:                                            5
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.25 0.25 0.32 0.18, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.29 0.17 0.21 0.34, t/t ratio=2.000]
     3         1 Felsenstein 84  [Bf:0.30 0.21 0.19 0.30, t/t ratio=2.000]
     4         1 Felsenstein 84  [Bf:0.29 0.22 0.20 0.29, t/t ratio=2.000]
     5         1 Felsenstein 84  [Bf:0.31 0.18 0.19 0.31, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     252
     2     921
     3     425
     4     459
     5     713

Population                   Locus   Gene copies    
----------------------------------------------------
  1 urchins                      1        24
  1                              2        24
  1                              3        24
  1                              4        24
  1                              5        24
    Total of all populations     1        24
                                 2        24
                                 3        24
                                 4        24
                                 5        24




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

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00520  0.00987  0.01383  0.01780  0.03047  0.01543  0.01685
    2  Theta_1         0.01653  0.02233  0.02550  0.03173  0.04280  0.02850  0.01498
    3  Theta_1         0.03067  0.04107  0.04763  0.04900  0.05127  0.04270  0.01870
    4  Theta_1         0.01087  0.01613  0.01990  0.02540  0.03680  0.02243  0.00592
    5  Theta_1         0.04120  0.04640  0.04810  0.04973  0.05140  0.04750  0.01612
  All  Theta_1         0.01047  0.01907  0.01983  0.02093  0.03047  0.02070  0.02630
-----------------------------------------------------------------------------------



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               -465.39                       -442.69                -420.88
      2              -1684.80                      -1618.02               -1597.70
      3              -1037.09                       -967.00                -941.55
      4               -906.45                       -874.43                -852.55
      5              -3079.07                      -2481.09               -2358.42
---------------------------------------------------------------------------------------
  All                -7180.44                      -6390.86               -6243.98
[Scaling factor = -7.640135]


(1a) Thermodynamic integration: log(Prob(D|Model)): Good approximation with many temperatures
(1b) Bezier-approximated Thermodynamic integration: when using few temperatures USE THIS!
(2)  Harmonic mean approximation: Overestimates the marginal likelihood, poor variance



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




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

Parameter           Accepted changes               Ratio
Theta_1                2498777/2498777           1.00000
Genealogies             346202/2501223            0.13841

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

  Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.24203             30942.42
  Ln[Prob(D|P)]          0.66082             10729.37
  (*) averaged over loci.

