  ==========================================================
    Example: sequence data set wit two loci [simulated data]
  ==========================================================
  MIGRATION RATE AND POPULATION SIZE ESTIMATION
  using Markov Chain Monte Carlo simulation
  ==========================================================
  Version 4.1.3a

  Program started at Sun Feb 22 13:55:43 2015
         finished at Sun Feb 22 13:56:24 2015
     


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  10.000000  20.000000   2.000000 
Population size (Theta_2)      Uniform  0.000000  10.000000  20.000000   2.000000 
Population size (Theta_3)      Uniform  0.000000  10.000000  20.000000   2.000000 
Population size (Theta_4)      Uniform  0.000000  10.000000  20.000000   2.000000 
Migration 4 to 2 (M)      Uniform  0.000000  10.000000  20.000000   2.000000 
Ancestor 4 to 2 (D_time)      Uniform  0.000000  50.000000  100.000000 10.000000 
Ancestor 4 to 2 (S_time)      Uniform  0.000000  50.000000  100.000000 10.000000 
Ancestor 4 to 3 (D_time)      Uniform  0.000000  50.000000  100.000000 10.000000 
Ancestor 4 to 3 (S_time)      Uniform  0.000000  50.000000  100.000000 10.000000 
Ancestor 1 to 4 (D_time)      Uniform  0.000000  50.000000  100.000000 10.000000 
Ancestor 1 to 4 (S_time)      Uniform  0.000000  50.000000  100.000000 10.000000 




Inheritance scalers in use for Thetas (specified scalars=1)
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 Africa         * 0 0 0 
   2 Americas       0 * 0 D 
   3 Pacific        0 0 * d 
   4 Asia           d 0 0 * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (inc*samples*rep):                  1000
      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):                   10

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

Summary of data:
Title:  Example: sequence data set wit two loci [simulated d
Data file:                                        infile.seq
Datatype:                                     Haplotype data
Number of loci:                                            2
Mutationmodel:
 Locus  Sublocus  Mutationmodel   Mutationmodel parameter
-----------------------------------------------------------------
     1         1 Felsenstein 84  [Bf:0.31 0.19 0.28 0.22, t/t ratio=2.000]
     2         1 Felsenstein 84  [Bf:0.28 0.22 0.21 0.29, t/t ratio=2.000]


Sites per locus
---------------
Locus    Sites
     1     145
     2     345

Population                   Locus   Gene copies    
----------------------------------------------------
  1 Africa                       1        25
  1                              2        25
  2 Americas                     1        25
  2                              2        25
  3 Pacific                      1        25
  3                              2        25
  4 Asia                         1        25
  4                              2        25
    Total of all populations     1       100
                                 2       100




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

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         4.88000  5.48000  6.14000  6.96000 11.56000  7.98000  8.73234
    1  Theta_2         1.20000  2.72000  3.58000  4.12000  5.60000  3.54000  3.90509
    1  Theta_3         1.24000  5.00000  5.98000  6.96000  8.72000  5.82000  5.45310
    1  Theta_4         5.08000  5.36000  6.18000  6.84000 13.84000 10.22000 10.30796
    1  M_4->2          0.36000  2.92000  3.62000  3.88000  6.08000  3.46000  3.50335
    1  D_4->2          4.26667  5.86667  7.90000  9.66667 21.86667 12.76667 12.77253
    1  S_4->2          6.53333 14.93333 16.76667 18.53333 22.00000 15.50000 16.07695
    1  D_4->3          3.73333  9.20000 12.90000 15.20000 20.46667 12.83333 14.43618
    1  S_4->3          6.73333 11.40000 13.36667 15.66667 20.13333 13.43333 13.27158
    1  D_1->4          2.00000  8.26667 10.56667 14.40000 17.60000 10.50000 10.27715
    1  S_1->4         10.93333 16.33333 18.36667 20.80000 27.66667 18.63333 18.67829
    2  Theta_1         2.24000  3.08000  4.18000  5.80000  8.32000  5.42000  6.52924
    2  Theta_2         0.16000  0.96000  1.58000  2.32000  3.92000  2.06000  3.22124
    2  Theta_3         1.20000  2.80000  3.86000  4.64000  4.96000  5.98000  6.21836
    2  Theta_4         3.40000  5.52000  6.70000  7.44000  7.88000  7.02000  8.46014
    2  M_4->2          0.12000  1.80000  2.62000  3.48000  6.28000  3.02000  3.49782
    2  D_4->2          0.00000  0.93333  2.30000  3.86667  6.40000  9.23333  8.59434
    2  S_4->2          7.66667  8.73333 10.10000 11.53333 25.73333 14.56667 14.10327
    2  D_4->3          4.26667  5.80000  8.36667 11.20000 14.60000  8.43333  8.05747
    2  S_4->3          1.40000  7.60000 10.03333 13.60000 17.00000 10.03333  9.92163
    2  D_1->4          8.13333 17.46667 22.56667 23.86667 25.46667 19.10000 18.18640
    2  S_1->4          3.13333 11.20000 13.76667 15.80000 24.00000 13.70000 14.47894
  All  Theta_1         2.48000  3.68000  4.34000  5.16000 12.64000  6.66000  7.48258
  All  Theta_2         0.68000  1.40000  2.06000  2.76000  4.72000  3.18000  4.96637
  All  Theta_3         1.40000  2.96000  3.70000  4.24000  8.72000  5.06000  5.13345
  All  Theta_4         8.28000 12.28000 13.06000 13.80000 14.04000 12.62000 10.78857
  All  M_4->2          0.44000  1.24000  2.06000  2.80000  6.04000  2.90000  3.21429
  All  D_4->2          1.60000  8.53333 10.96667 12.26667 19.20000 10.90000 10.84742
  All  S_4->2          7.00000 13.53333 16.83333 19.06667 26.13333 15.30000 14.98051
  All  D_4->3          4.40000 12.53333 13.83333 15.20000 16.46667 12.03333 11.78865
  All  S_4->3          7.20000 10.06667 11.23333 12.60000 16.06667 11.50000 11.38349
  All  D_1->4          6.60000 16.53333 18.16667 19.86667 24.33333 16.30000 14.57228
  All  S_1->4         14.86667 16.33333 17.50000 18.53333 23.80000 15.70000 14.35950
-----------------------------------------------------------------------------------



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              -7974.50                      -7674.00              -10133.41
      2             -16546.93                     -15783.51              -21657.35
---------------------------------------------------------------------------------------
  All               -24514.61                     -23450.68              -31783.93
[Scaling factor = 6.828643]


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




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

Parameter           Accepted changes               Ratio
Theta_1                    105/105               1.00000
Theta_2                     85/85                1.00000
Theta_3                     66/66                1.00000
Theta_4                     80/80                1.00000
M_4->2                      90/90                1.00000
D_4->2                       94/95                0.98947
S_4->2                       85/91                0.93407
D_4->3                       76/93                0.81720
S_4->3                       72/86                0.83721
D_1->4                       94/111               0.84685
S_1->4                       77/99                0.77778
Genealogies                247/999                0.24725

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

[  0]   Parameter         Autocorrelation(*)   Effective Sample size
  ---------         ---------------      ---------------------
  Theta_1                0.97066                27.08
  Theta_2                0.94557                39.08
  Theta_3                0.97335                23.24
  Theta_4                0.97331                19.62
  M_4->2                 0.94994                43.74
  Ln[Prob(D|P)]          0.98454                14.16
  (*) averaged over loci.


POTENTIAL PROBLEMS
------------------------------------------------------------------------------------------
This section reports potential problems with your run, but such reporting is often not 
very accurate. Whith many parameters in a multilocus analysis, it is very common that 
some parameters for some loci will not be very informative, triggering suggestions (for 
example to increase the prior range) that are not sensible. This suggestion tool will 
improve with time, therefore do not blindly follow its suggestions. If some parameters 
are flagged, inspect the tables carefully and judge wether an action is required. For 
example, if you run a Bayesian inference with sequence data, for macroscopic species 
there is rarely the need to increase the prior for Theta beyond 0.1; but if you use 
microsatellites it is rather common that your prior distribution for Theta should have a 
range from 0.0 to 100 or more. With many populations (>3) it is also very common that 
some migration routes are estimated poorly because the data contains little or no 
information for that route. Increasing the range will not help in such situations, 
reducing number of parameters may help in such situations.
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Param 4: Effective sample size of run seems too short! 
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