Note
Go to the end to download the full example code.
ASW inference - Two pulses model#
This example implements inference for the ASW population under a two pulses model of admixture, using the tracts package. Inference is performed using autosomal and X chromosome data, allowing for the specification of sex-biased admixture.
To implement this example, we use the following driver file:
samples:
directory: ./ASW_TrioPhased
individual_names: [
"NA19625","NA19700","NA19701","NA19703","NA19704","NA19707","NA19711","NA19712","NA19713","NA19818","NA19819",
"NA19834","NA19835","NA19900","NA19901","NA19904","NA19908","NA19909","NA19913","NA19914","NA19916","NA19917",
"NA19920","NA19921","NA19922","NA19923","NA19982","NA19984","NA20126","NA20127","NA20274","NA20276","NA20278",
"NA20281","NA20282","NA20287","NA20289","NA20291","NA20294","NA20296","NA20298","NA20299","NA20314","NA20317",
"NA20318","NA20320","NA20321","NA20332","NA20334","NA20339","NA20340","NA20342","NA20346","NA20348","NA20351",
"NA20355","NA20356","NA20357","NA20359","NA20362","NA20412"]
male_names : [
"NA19700","NA19703","NA19711","NA19818","NA19834","NA19900","NA19904","NA19908","NA19916","NA19920",
"NA19922","NA19982","NA19984","NA20126","NA20278","NA20281","NA20291","NA20298","NA20318","NA20340",
"NA20342","NA20346","NA20348","NA20351","NA20356","NA20362"] #see Readme_dataprocessing.md for how this was generated
filename_format: "{name}_{label}_final.bed"
labels: [A, B] #If this field is omitted, 'A' and 'B' will be used by default
chromosomes: 1-22 #The chromosomes to use for analysis. Can be specified as a list or a range
allosomes: [X]
models:
model_filename: ../models/ppp_pxx.yaml
ad_model_autosomes: M
ad_model_allosomes: DC
start_params:
t1: 14:16
REUR: 0.1:0.9
REUR_sex_bias: 0.1:0.3
t2: 4:8
REUR2: 0.1:0.8
REUR2_sex_bias: 0.1:0.3
RAMR: 0.1:0.9
RAMR_sex_bias: 0.1:0.3
optim:
repetitions: 3
seed: 100
maximum_iterations: 1000
npts: 50
exclude_tracts_below_cm: 2
unknown_labels_for_smoothing: ["UNK", "centromere","miscall"] # segments with these labels will be smoother over, that is, will be filled with neighbouring ancestries up to their midpoints.
n_reoptimizations: 5
rerun_optimization_on_boundaries: False
output:
output_directory: ./output_two_pulses/
output_filename_format: "ASW_test_output_{label}"
log_filename: 'ASW_two_pulses.log'
verbose_log: 1
verbose_screen: 30
log_scale: True
Complete results from this analysis are saved in the output directory specified in the driver file. Below, we display the optimal parameters estimated from this analysis, as well as the plots illustrating the inferred tract length distributions, compared to the observed histograms, for every source population and chromosome type (autosomes and X chromosome).
Optimal parameters#
parameter |
value |
|---|---|
REUR |
0.12280218019785814 |
REUR_sex_bias |
0.9895834943467057 |
RAMR |
0.030960551215534607 |
RAMR_sex_bias |
0.8641591182247106 |
t1 |
7.762196801885175 |
REUR2 |
0.09158074162784507 |
REUR2_sex_bias |
-0.9898345899409771 |
t2 |
4.989102249481194 |
X_AFR_rate |
0.8462372685866073 |
X_AFR_sex_bias |
-0.964328949341509 |
likelihood -901.672 |
Optimal migration matrices#
Tract length histograms#
Autosomal admixture#
X chromosome admixture#
------------------------------------------------------------------------------------------------
Running tracts 2.0 with driver file: /home/runner/work/tracts/tracts/example/documentation_examples/ASW/ASW_two_pulses.yaml
------------------------------------------------------------------------------------------------
Results will be written to: output_two_pulses.
Using log file: output_two_pulses/ASW_two_pulses.log.
excluding_tracts_below set to 2.0 cM.
Re-optimization will be performed until convergence or maximum 5 times.
Ancestries: EUR, AMR, AFR
Data autosome proportions: [0.19578862 0.03825495 0.76595643]
Data allosome proportions: [0.16839124 0.03818939 0.79341937]
Model parameters and bounds:
--------------------------------------------
Parameter | Lower bound | Upper bound
--------------------------------------------
REUR | 1e-09 | 1
REUR_sex_bias | -1 | 1
RAMR | 1e-09 | 1
RAMR_sex_bias | -1 | 1
t1 | 1 | inf
REUR2 | 1e-09 | 1
REUR2_sex_bias | -1 | 1
t2 | 1 | inf
--------------------------------------------
Multiple starting parameters will be generated and used for multiple optimization runs.
-----------------------------------------------------------------------------------------------
Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RAMR', 't1', 'REUR2', 't2'].
-----------------------------------------------------------------------------------------------
Starting parameters for step 1 optimization
------------------------------------------------------------------------------
Run | REUR | RAMR | t1 | REUR2 | t2
------------------------------------------------------------------------------
1 | 0.3197 | 0.1117 | 14.14 | 0.4669 | 4.909
2 | 0.4537 | 0.3424 | 15.41 | 0.6505 | 6.681
3 | 0.5082 | 0.3643 | 15.33 | 0.2343 | 6.998
------------------------------------------------------------------------------
Optimization run #1
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -10085.8 , array([ 0.310623 , 0 , 0.108834 , 0 , 11.7325 , 0.473689 , 0 , 4.73203 ]), Autosomes
60 , -6657.07 , array([ 0.295726 , 0 , 0.104309 , 0 , 9.16813 , 0.470892 , 0 , 4.46589 ]), Autosomes
90 , -4568.63 , array([ 0.273992 , 0 , 0.0996824 , 0 , 7.40648 , 0.45933 , 0 , 4.06512 ]), Autosomes
120 , -3474.65 , array([ 0.250604 , 0 , 0.0950853 , 0 , 6.26933 , 0.427149 , 0 , 3.7061 ]), Autosomes
150 , -2836.78 , array([ 0.233723 , 0 , 0.0898764 , 0 , 5.6749 , 0.378912 , 0 , 3.55065 ]), Autosomes
180 , -2284.69 , array([ 0.209368 , 0 , 0.0822467 , 0 , 5.7354 , 0.327611 , 0 , 3.42797 ]), Autosomes
210 , -1845.51 , array([ 0.18799 , 0 , 0.075414 , 0 , 5.93517 , 0.279083 , 0 , 3.41415 ]), Autosomes
240 , -1521.54 , array([ 0.170513 , 0 , 0.0686594 , 0 , 6.35888 , 0.244061 , 0 , 3.37309 ]), Autosomes
270 , -1228.13 , array([ 0.155232 , 0 , 0.0601202 , 0 , 6.70305 , 0.207741 , 0 , 3.43287 ]), Autosomes
300 , -1001.21 , array([ 0.142078 , 0 , 0.0527386 , 0 , 6.98488 , 0.174591 , 0 , 3.54602 ]), Autosomes
330 , -960.174 , array([ 0.138119 , 0 , 0.0511888 , 0 , 6.98439 , 0.167882 , 0 , 3.60895 ]), Autosomes
350 , -959.312 , array([ 0.138088 , 0 , 0.0511837 , 0 , 6.9808 , 0.16786 , 0 , 3.60921 ]), Autosomes
Optimization completed.
-----------------------
Optimization run #2
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -24859.5 , array([ 0.438009 , 0 , 0.329853 , 0 , 13.0935 , 0.65459 , 0 , 6.52655 ]), Autosomes
60 , -18148.7 , array([ 0.419447 , 0 , 0.311351 , 0 , 10.3356 , 0.651948 , 0 , 6.36149 ]), Autosomes
90 , -13763.9 , array([ 0.401272 , 0 , 0.295523 , 0 , 8.34593 , 0.645941 , 0 , 5.95576 ]), Autosomes
120 , -10482.2 , array([ 0.377512 , 0 , 0.27383 , 0 , 6.83278 , 0.630943 , 0 , 5.82025 ]), Autosomes
150 , 7.78518e+28 , array([ 0.377482 , 0 , 0.273464 , 0 , 6.82372 , 0.630861 , 0 , 5.8245 ]), OOB (oob=-0.0007785184017068403)
154 , -10453.2 , array([ 0.377498 , 0 , 0.273469 , 0 , 6.82468 , 0.630867 , 0 , 5.82464 ]), Autosomes
Optimization completed.
-----------------------
Optimization run #3
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -28604.4 , array([ 0.497709 , 0 , 0.353304 , 0 , 12.7118 , 0.234619 , 0 , 6.94258 ]), Autosomes
60 , -22160.4 , array([ 0.483003 , 0 , 0.343592 , 0 , 10.8519 , 0.237164 , 0 , 6.83502 ]), Autosomes
90 , -18986.4 , array([ 0.464834 , 0 , 0.333967 , 0 , 9.99945 , 0.238343 , 0 , 6.9407 ]), Autosomes
109 , -18717 , array([ 0.464851 , 0 , 0.334548 , 0 , 10.0099 , 0.23892 , 0 , 6.9225 ]), Autosomes
Optimization completed.
-----------------------
In Step 1: Results from multiple optimization runs with different starting parameters:
----------------------------------------------------------------------------------------------------------------------------------------------
Run | LogLik | REUR | REUR_sex_bias | RAMR | RAMR_sex_bias | t1 | REUR2 | REUR2_sex_bias | t2
----------------------------------------------------------------------------------------------------------------------------------------------
1 | -959.312 | 0.1381 | 0 | 0.05118 | 0 | 6.981 | 0.1679 | 0 | 3.609
2 | -10453.2 | 0.3775 | 0 | 0.2735 | 0 | 6.825 | 0.6309 | 0 | 5.825
3 | -18717 | 0.4649 | 0 | 0.3345 | 0 | 10.01 | 0.2389 | 0 | 6.922
----------------------------------------------------------------------------------------------------------------------------------------------
Selecting best parameters from step 1 and proceeding to step 2 optimization.
----------------------------------------------------------------------------------------------------------------
Step 2 : Optimizing allosomal likelihood over parameters : ['REUR_sex_bias', 'RAMR_sex_bias', 'REUR2_sex_bias'].
----------------------------------------------------------------------------------------------------------------
Starting parameters for step 2 optimization (non-sex-bias parameters are fixed to the best step 1 estimates).
-------------------------------------------------------------------------------------------------------------------------------
Run | REUR | REUR_sex_bias | RAMR | RAMR_sex_bias | t1 | REUR2 | REUR2_sex_bias | t2
-------------------------------------------------------------------------------------------------------------------------------
1 | 0.1381 | 0.2193 | 0.05118 | 0.1086 | 6.981 | 0.1679 | 0.2581 | 3.609
2 | 0.1381 | 0.138 | 0.05118 | 0.1569 | 6.981 | 0.1679 | 0.22 | 3.609
3 | 0.1381 | 0.2004 | 0.05118 | 0.1988 | 6.981 | 0.1679 | 0.1787 | 3.609
-------------------------------------------------------------------------------------------------------------------------------
Optimization run #1
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -220.592 , array([ 0.138088 , 0.288138 , 0.0511837 , 0.141831 , 6.9808 , 0.16786 , 0.156171 , 3.60921 ]), Female allosomes
30 , -102.683 , array([ 0.138088 , 0.288138 , 0.0511837 , 0.141831 , 6.9808 , 0.16786 , 0.156171 , 3.60921 ]), Male allosomes
60 , -218.693 , array([ 0.138088 , 0.355795 , 0.0511837 , 0.176316 , 6.9808 , 0.16786 , 0.0337716 , 3.60921 ]), Female allosomes
60 , -102.694 , array([ 0.138088 , 0.355795 , 0.0511837 , 0.176316 , 6.9808 , 0.16786 , 0.0337716 , 3.60921 ]), Male allosomes
90 , -216.869 , array([ 0.138088 , 0.413615 , 0.0511837 , 0.207402 , 6.9808 , 0.16786 , -0.0937512 , 3.60921 ]), Female allosomes
90 , -102.682 , array([ 0.138088 , 0.413615 , 0.0511837 , 0.207402 , 6.9808 , 0.16786 , -0.0937512 , 3.60921 ]), Male allosomes
120 , -215.138 , array([ 0.138088 , 0.463952 , 0.0511837 , 0.234298 , 6.9808 , 0.16786 , -0.222704 , 3.60921 ]), Female allosomes
120 , -102.659 , array([ 0.138088 , 0.463952 , 0.0511837 , 0.234298 , 6.9808 , 0.16786 , -0.222704 , 3.60921 ]), Male allosomes
150 , -213.529 , array([ 0.138088 , 0.509981 , 0.0511837 , 0.260342 , 6.9808 , 0.16786 , -0.34425 , 3.60921 ]), Female allosomes
150 , -102.66 , array([ 0.138088 , 0.509981 , 0.0511837 , 0.260342 , 6.9808 , 0.16786 , -0.34425 , 3.60921 ]), Male allosomes
180 , -212.022 , array([ 0.138088 , 0.557258 , 0.0511837 , 0.286804 , 6.9808 , 0.16786 , -0.453708 , 3.60921 ]), Female allosomes
180 , -102.711 , array([ 0.138088 , 0.557258 , 0.0511837 , 0.286804 , 6.9808 , 0.16786 , -0.453708 , 3.60921 ]), Male allosomes
210 , -210.675 , array([ 0.138088 , 0.602668 , 0.0511837 , 0.31006 , 6.9808 , 0.16786 , -0.549716 , 3.60921 ]), Female allosomes
210 , -102.79 , array([ 0.138088 , 0.602668 , 0.0511837 , 0.31006 , 6.9808 , 0.16786 , -0.549716 , 3.60921 ]), Male allosomes
240 , -209.762 , array([ 0.138088 , 0.635948 , 0.0511837 , 0.32268 , 6.9808 , 0.16786 , -0.61833 , 3.60921 ]), Female allosomes
240 , -102.834 , array([ 0.138088 , 0.635948 , 0.0511837 , 0.32268 , 6.9808 , 0.16786 , -0.61833 , 3.60921 ]), Male allosomes
270 , -208.655 , array([ 0.138088 , 0.677898 , 0.0511837 , 0.3511 , 6.9808 , 0.16786 , -0.688914 , 3.60921 ]), Female allosomes
270 , -102.981 , array([ 0.138088 , 0.677898 , 0.0511837 , 0.3511 , 6.9808 , 0.16786 , -0.688914 , 3.60921 ]), Male allosomes
300 , -207.701 , array([ 0.138088 , 0.71417 , 0.0511837 , 0.379276 , 6.9808 , 0.16786 , -0.749233 , 3.60921 ]), Female allosomes
300 , -103.129 , array([ 0.138088 , 0.71417 , 0.0511837 , 0.379276 , 6.9808 , 0.16786 , -0.749233 , 3.60921 ]), Male allosomes
330 , -206.873 , array([ 0.138088 , 0.747929 , 0.0511837 , 0.406303 , 6.9808 , 0.16786 , -0.798976 , 3.60921 ]), Female allosomes
330 , -103.286 , array([ 0.138088 , 0.747929 , 0.0511837 , 0.406303 , 6.9808 , 0.16786 , -0.798976 , 3.60921 ]), Male allosomes
360 , -206.13 , array([ 0.138088 , 0.781234 , 0.0511837 , 0.438147 , 6.9808 , 0.16786 , -0.837885 , 3.60921 ]), Female allosomes
360 , -103.476 , array([ 0.138088 , 0.781234 , 0.0511837 , 0.438147 , 6.9808 , 0.16786 , -0.837885 , 3.60921 ]), Male allosomes
390 , -205.496 , array([ 0.138088 , 0.810296 , 0.0511837 , 0.469614 , 6.9808 , 0.16786 , -0.869395 , 3.60921 ]), Female allosomes
390 , -103.66 , array([ 0.138088 , 0.810296 , 0.0511837 , 0.469614 , 6.9808 , 0.16786 , -0.869395 , 3.60921 ]), Male allosomes
420 , -204.955 , array([ 0.138088 , 0.835564 , 0.0511837 , 0.498495 , 6.9808 , 0.16786 , -0.8958 , 3.60921 ]), Female allosomes
420 , -103.83 , array([ 0.138088 , 0.835564 , 0.0511837 , 0.498495 , 6.9808 , 0.16786 , -0.8958 , 3.60921 ]), Male allosomes
450 , -204.472 , array([ 0.138088 , 0.859606 , 0.0511837 , 0.530682 , 6.9808 , 0.16786 , -0.915738 , 3.60921 ]), Female allosomes
450 , -104.014 , array([ 0.138088 , 0.859606 , 0.0511837 , 0.530682 , 6.9808 , 0.16786 , -0.915738 , 3.60921 ]), Male allosomes
480 , -204.082 , array([ 0.138088 , 0.880212 , 0.0511837 , 0.553905 , 6.9808 , 0.16786 , -0.932524 , 3.60921 ]), Female allosomes
480 , -104.163 , array([ 0.138088 , 0.880212 , 0.0511837 , 0.553905 , 6.9808 , 0.16786 , -0.932524 , 3.60921 ]), Male allosomes
510 , -203.745 , array([ 0.138088 , 0.897744 , 0.0511837 , 0.577608 , 6.9808 , 0.16786 , -0.946125 , 3.60921 ]), Female allosomes
510 , -104.303 , array([ 0.138088 , 0.897744 , 0.0511837 , 0.577608 , 6.9808 , 0.16786 , -0.946125 , 3.60921 ]), Male allosomes
540 , -203.458 , array([ 0.138088 , 0.913803 , 0.0511837 , 0.598135 , 6.9808 , 0.16786 , -0.95688 , 3.60921 ]), Female allosomes
540 , -104.432 , array([ 0.138088 , 0.913803 , 0.0511837 , 0.598135 , 6.9808 , 0.16786 , -0.95688 , 3.60921 ]), Male allosomes
570 , -203.212 , array([ 0.138088 , 0.928001 , 0.0511837 , 0.617571 , 6.9808 , 0.16786 , -0.965245 , 3.60921 ]), Female allosomes
570 , -104.552 , array([ 0.138088 , 0.928001 , 0.0511837 , 0.617571 , 6.9808 , 0.16786 , -0.965245 , 3.60921 ]), Male allosomes
600 , -203.017 , array([ 0.138088 , 0.939651 , 0.0511837 , 0.631672 , 6.9808 , 0.16786 , -0.97217 , 3.60921 ]), Female allosomes
600 , -104.645 , array([ 0.138088 , 0.939651 , 0.0511837 , 0.631672 , 6.9808 , 0.16786 , -0.97217 , 3.60921 ]), Male allosomes
630 , -202.853 , array([ 0.138088 , 0.949638 , 0.0511837 , 0.644192 , 6.9808 , 0.16786 , -0.977661 , 3.60921 ]), Female allosomes
630 , -104.728 , array([ 0.138088 , 0.949638 , 0.0511837 , 0.644192 , 6.9808 , 0.16786 , -0.977661 , 3.60921 ]), Male allosomes
660 , -202.72 , array([ 0.138088 , 0.958058 , 0.0511837 , 0.653953 , 6.9808 , 0.16786 , -0.982112 , 3.60921 ]), Female allosomes
660 , -104.795 , array([ 0.138088 , 0.958058 , 0.0511837 , 0.653953 , 6.9808 , 0.16786 , -0.982112 , 3.60921 ]), Male allosomes
690 , -202.563 , array([ 0.138088 , 0.964624 , 0.0511837 , 0.655711 , 6.9808 , 0.16786 , -0.984988 , 3.60921 ]), Female allosomes
690 , -104.808 , array([ 0.138088 , 0.964624 , 0.0511837 , 0.655711 , 6.9808 , 0.16786 , -0.984988 , 3.60921 ]), Male allosomes
720 , -202.521 , array([ 0.138088 , 0.969173 , 0.0511837 , 0.657101 , 6.9808 , 0.16786 , -0.985559 , 3.60921 ]), Female allosomes
720 , -104.835 , array([ 0.138088 , 0.969173 , 0.0511837 , 0.657101 , 6.9808 , 0.16786 , -0.985559 , 3.60921 ]), Male allosomes
750 , -202.476 , array([ 0.138088 , 0.973074 , 0.0511837 , 0.66102 , 6.9808 , 0.16786 , -0.986256 , 3.60921 ]), Female allosomes
750 , -104.867 , array([ 0.138088 , 0.973074 , 0.0511837 , 0.66102 , 6.9808 , 0.16786 , -0.986256 , 3.60921 ]), Male allosomes
780 , -202.445 , array([ 0.138088 , 0.976534 , 0.0511837 , 0.661684 , 6.9808 , 0.16786 , -0.986933 , 3.60921 ]), Female allosomes
780 , -104.887 , array([ 0.138088 , 0.976534 , 0.0511837 , 0.661684 , 6.9808 , 0.16786 , -0.986933 , 3.60921 ]), Male allosomes
810 , -202.423 , array([ 0.138088 , 0.979529 , 0.0511837 , 0.660338 , 6.9808 , 0.16786 , -0.987619 , 3.60921 ]), Female allosomes
810 , -104.898 , array([ 0.138088 , 0.979529 , 0.0511837 , 0.660338 , 6.9808 , 0.16786 , -0.987619 , 3.60921 ]), Male allosomes
840 , -202.403 , array([ 0.138088 , 0.982144 , 0.0511837 , 0.659571 , 6.9808 , 0.16786 , -0.988262 , 3.60921 ]), Female allosomes
840 , -104.909 , array([ 0.138088 , 0.982144 , 0.0511837 , 0.659571 , 6.9808 , 0.16786 , -0.988262 , 3.60921 ]), Male allosomes
870 , -202.388 , array([ 0.138088 , 0.984412 , 0.0511837 , 0.658025 , 6.9808 , 0.16786 , -0.98892 , 3.60921 ]), Female allosomes
870 , -104.916 , array([ 0.138088 , 0.984412 , 0.0511837 , 0.658025 , 6.9808 , 0.16786 , -0.98892 , 3.60921 ]), Male allosomes
900 , -202.378 , array([ 0.138088 , 0.986342 , 0.0511837 , 0.655757 , 6.9808 , 0.16786 , -0.989616 , 3.60921 ]), Female allosomes
900 , -104.919 , array([ 0.138088 , 0.986342 , 0.0511837 , 0.655757 , 6.9808 , 0.16786 , -0.989616 , 3.60921 ]), Male allosomes
930 , -202.37 , array([ 0.138088 , 0.987929 , 0.0511837 , 0.654085 , 6.9808 , 0.16786 , -0.990222 , 3.60921 ]), Female allosomes
930 , -104.922 , array([ 0.138088 , 0.987929 , 0.0511837 , 0.654085 , 6.9808 , 0.16786 , -0.990222 , 3.60921 ]), Male allosomes
947 , -202.295 , array([ 0.138088 , 0.987926 , 0.0511837 , 0.654697 , 6.9808 , 0.16786 , -0.990228 , 3.60921 ]), Female allosomes
947 , -104.898 , array([ 0.138088 , 0.987926 , 0.0511837 , 0.654697 , 6.9808 , 0.16786 , -0.990228 , 3.60921 ]), Male allosomes
Optimization completed.
-----------------------
Optimization run #2
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -220.755 , array([ 0.138088 , 0.212298 , 0.0511837 , 0.188605 , 6.9808 , 0.16786 , 0.118252 , 3.60921 ]), Female allosomes
30 , -102.471 , array([ 0.138088 , 0.212298 , 0.0511837 , 0.188605 , 6.9808 , 0.16786 , 0.118252 , 3.60921 ]), Male allosomes
60 , -218.843 , array([ 0.138088 , 0.284294 , 0.0511837 , 0.222204 , 6.9808 , 0.16786 , -0.00309654 , 3.60921 ]), Female allosomes
60 , -102.495 , array([ 0.138088 , 0.284294 , 0.0511837 , 0.222204 , 6.9808 , 0.16786 , -0.00309654 , 3.60921 ]), Male allosomes
90 , -217.023 , array([ 0.138088 , 0.346295 , 0.0511837 , 0.245589 , 6.9808 , 0.16786 , -0.131313 , 3.60921 ]), Female allosomes
90 , -102.477 , array([ 0.138088 , 0.346295 , 0.0511837 , 0.245589 , 6.9808 , 0.16786 , -0.131313 , 3.60921 ]), Male allosomes
120 , -215.271 , array([ 0.138088 , 0.405913 , 0.0511837 , 0.269006 , 6.9808 , 0.16786 , -0.254973 , 3.60921 ]), Female allosomes
120 , -102.487 , array([ 0.138088 , 0.405913 , 0.0511837 , 0.269006 , 6.9808 , 0.16786 , -0.254973 , 3.60921 ]), Male allosomes
150 , -213.598 , array([ 0.138088 , 0.464755 , 0.0511837 , 0.294249 , 6.9808 , 0.16786 , -0.370195 , 3.60921 ]), Female allosomes
150 , -102.542 , array([ 0.138088 , 0.464755 , 0.0511837 , 0.294249 , 6.9808 , 0.16786 , -0.370195 , 3.60921 ]), Male allosomes
180 , -212.06 , array([ 0.138088 , 0.520403 , 0.0511837 , 0.319538 , 6.9808 , 0.16786 , -0.474499 , 3.60921 ]), Female allosomes
180 , -102.629 , array([ 0.138088 , 0.520403 , 0.0511837 , 0.319538 , 6.9808 , 0.16786 , -0.474499 , 3.60921 ]), Male allosomes
210 , -210.671 , array([ 0.138088 , 0.572981 , 0.0511837 , 0.344657 , 6.9808 , 0.16786 , -0.566271 , 3.60921 ]), Female allosomes
210 , -102.746 , array([ 0.138088 , 0.572981 , 0.0511837 , 0.344657 , 6.9808 , 0.16786 , -0.566271 , 3.60921 ]), Male allosomes
240 , -209.438 , array([ 0.138088 , 0.622056 , 0.0511837 , 0.369619 , 6.9808 , 0.16786 , -0.645297 , 3.60921 ]), Female allosomes
240 , -102.886 , array([ 0.138088 , 0.622056 , 0.0511837 , 0.369619 , 6.9808 , 0.16786 , -0.645297 , 3.60921 ]), Male allosomes
270 , -208.436 , array([ 0.138088 , 0.666287 , 0.0511837 , 0.396282 , 6.9808 , 0.16786 , -0.705934 , 3.60921 ]), Female allosomes
270 , -103.036 , array([ 0.138088 , 0.666287 , 0.0511837 , 0.396282 , 6.9808 , 0.16786 , -0.705934 , 3.60921 ]), Male allosomes
300 , -207.484 , array([ 0.138088 , 0.708543 , 0.0511837 , 0.421716 , 6.9808 , 0.16786 , -0.761118 , 3.60921 ]), Female allosomes
300 , -103.21 , array([ 0.138088 , 0.708543 , 0.0511837 , 0.421716 , 6.9808 , 0.16786 , -0.761118 , 3.60921 ]), Male allosomes
330 , -206.67 , array([ 0.138088 , 0.746677 , 0.0511837 , 0.443534 , 6.9808 , 0.16786 , -0.807268 , 3.60921 ]), Female allosomes
330 , -103.377 , array([ 0.138088 , 0.746677 , 0.0511837 , 0.443534 , 6.9808 , 0.16786 , -0.807268 , 3.60921 ]), Male allosomes
360 , -205.958 , array([ 0.138088 , 0.78069 , 0.0511837 , 0.468639 , 6.9808 , 0.16786 , -0.845176 , 3.60921 ]), Female allosomes
360 , -103.553 , array([ 0.138088 , 0.78069 , 0.0511837 , 0.468639 , 6.9808 , 0.16786 , -0.845176 , 3.60921 ]), Male allosomes
390 , -205.362 , array([ 0.138088 , 0.811538 , 0.0511837 , 0.489645 , 6.9808 , 0.16786 , -0.875338 , 3.60921 ]), Female allosomes
390 , -103.717 , array([ 0.138088 , 0.811538 , 0.0511837 , 0.489645 , 6.9808 , 0.16786 , -0.875338 , 3.60921 ]), Male allosomes
420 , -204.826 , array([ 0.138088 , 0.838997 , 0.0511837 , 0.51756 , 6.9808 , 0.16786 , -0.899557 , 3.60921 ]), Female allosomes
420 , -103.898 , array([ 0.138088 , 0.838997 , 0.0511837 , 0.51756 , 6.9808 , 0.16786 , -0.899557 , 3.60921 ]), Male allosomes
450 , -204.376 , array([ 0.138088 , 0.862163 , 0.0511837 , 0.543529 , 6.9808 , 0.16786 , -0.9194 , 3.60921 ]), Female allosomes
450 , -104.061 , array([ 0.138088 , 0.862163 , 0.0511837 , 0.543529 , 6.9808 , 0.16786 , -0.9194 , 3.60921 ]), Male allosomes
480 , -203.984 , array([ 0.138088 , 0.882418 , 0.0511837 , 0.57051 , 6.9808 , 0.16786 , -0.935252 , 3.60921 ]), Female allosomes
480 , -104.22 , array([ 0.138088 , 0.882418 , 0.0511837 , 0.57051 , 6.9808 , 0.16786 , -0.935252 , 3.60921 ]), Male allosomes
510 , -203.661 , array([ 0.138088 , 0.900248 , 0.0511837 , 0.59159 , 6.9808 , 0.16786 , -0.948235 , 3.60921 ]), Female allosomes
510 , -104.356 , array([ 0.138088 , 0.900248 , 0.0511837 , 0.59159 , 6.9808 , 0.16786 , -0.948235 , 3.60921 ]), Male allosomes
540 , -203.382 , array([ 0.138088 , 0.91571 , 0.0511837 , 0.612465 , 6.9808 , 0.16786 , -0.958585 , 3.60921 ]), Female allosomes
540 , -104.483 , array([ 0.138088 , 0.91571 , 0.0511837 , 0.612465 , 6.9808 , 0.16786 , -0.958585 , 3.60921 ]), Male allosomes
570 , -203.158 , array([ 0.138088 , 0.928398 , 0.0511837 , 0.628409 , 6.9808 , 0.16786 , -0.967162 , 3.60921 ]), Female allosomes
570 , -104.585 , array([ 0.138088 , 0.928398 , 0.0511837 , 0.628409 , 6.9808 , 0.16786 , -0.967162 , 3.60921 ]), Male allosomes
600 , -202.974 , array([ 0.138088 , 0.939863 , 0.0511837 , 0.640378 , 6.9808 , 0.16786 , -0.973818 , 3.60921 ]), Female allosomes
600 , -104.671 , array([ 0.138088 , 0.939863 , 0.0511837 , 0.640378 , 6.9808 , 0.16786 , -0.973818 , 3.60921 ]), Male allosomes
630 , -202.818 , array([ 0.138088 , 0.950093 , 0.0511837 , 0.651098 , 6.9808 , 0.16786 , -0.978956 , 3.60921 ]), Female allosomes
630 , -104.75 , array([ 0.138088 , 0.950093 , 0.0511837 , 0.651098 , 6.9808 , 0.16786 , -0.978956 , 3.60921 ]), Male allosomes
660 , -202.696 , array([ 0.138088 , 0.958362 , 0.0511837 , 0.658107 , 6.9808 , 0.16786 , -0.983204 , 3.60921 ]), Female allosomes
660 , -104.809 , array([ 0.138088 , 0.958362 , 0.0511837 , 0.658107 , 6.9808 , 0.16786 , -0.983204 , 3.60921 ]), Male allosomes
690 , -202.542 , array([ 0.138088 , 0.9645 , 0.0511837 , 0.662721 , 6.9808 , 0.16786 , -0.985163 , 3.60921 ]), Female allosomes
690 , -104.829 , array([ 0.138088 , 0.9645 , 0.0511837 , 0.662721 , 6.9808 , 0.16786 , -0.985163 , 3.60921 ]), Male allosomes
720 , -202.498 , array([ 0.138088 , 0.969204 , 0.0511837 , 0.66457 , 6.9808 , 0.16786 , -0.985573 , 3.60921 ]), Female allosomes
720 , -104.859 , array([ 0.138088 , 0.969204 , 0.0511837 , 0.66457 , 6.9808 , 0.16786 , -0.985573 , 3.60921 ]), Male allosomes
750 , -202.465 , array([ 0.138088 , 0.973225 , 0.0511837 , 0.664272 , 6.9808 , 0.16786 , -0.986168 , 3.60921 ]), Female allosomes
750 , -104.878 , array([ 0.138088 , 0.973225 , 0.0511837 , 0.664272 , 6.9808 , 0.16786 , -0.986168 , 3.60921 ]), Male allosomes
780 , -202.438 , array([ 0.138088 , 0.976703 , 0.0511837 , 0.663426 , 6.9808 , 0.16786 , -0.986781 , 3.60921 ]), Female allosomes
780 , -104.893 , array([ 0.138088 , 0.976703 , 0.0511837 , 0.663426 , 6.9808 , 0.16786 , -0.986781 , 3.60921 ]), Male allosomes
810 , -202.421 , array([ 0.138088 , 0.979722 , 0.0511837 , 0.660739 , 6.9808 , 0.16786 , -0.987362 , 3.60921 ]), Female allosomes
810 , -104.9 , array([ 0.138088 , 0.979722 , 0.0511837 , 0.660739 , 6.9808 , 0.16786 , -0.987362 , 3.60921 ]), Male allosomes
840 , -202.402 , array([ 0.138088 , 0.98227 , 0.0511837 , 0.659828 , 6.9808 , 0.16786 , -0.988057 , 3.60921 ]), Female allosomes
840 , -104.91 , array([ 0.138088 , 0.98227 , 0.0511837 , 0.659828 , 6.9808 , 0.16786 , -0.988057 , 3.60921 ]), Male allosomes
870 , -202.388 , array([ 0.138088 , 0.984531 , 0.0511837 , 0.658025 , 6.9808 , 0.16786 , -0.988691 , 3.60921 ]), Female allosomes
870 , -104.916 , array([ 0.138088 , 0.984531 , 0.0511837 , 0.658025 , 6.9808 , 0.16786 , -0.988691 , 3.60921 ]), Male allosomes
900 , -202.374 , array([ 0.138088 , 0.986436 , 0.0511837 , 0.657166 , 6.9808 , 0.16786 , -0.989409 , 3.60921 ]), Female allosomes
900 , -104.924 , array([ 0.138088 , 0.986436 , 0.0511837 , 0.657166 , 6.9808 , 0.16786 , -0.989409 , 3.60921 ]), Male allosomes
930 , -202.363 , array([ 0.138088 , 0.988099 , 0.0511837 , 0.655749 , 6.9808 , 0.16786 , -0.990093 , 3.60921 ]), Female allosomes
930 , -104.928 , array([ 0.138088 , 0.988099 , 0.0511837 , 0.655749 , 6.9808 , 0.16786 , -0.990093 , 3.60921 ]), Male allosomes
960 , -202.286 , array([ 0.138088 , 0.988471 , 0.0511837 , 0.656036 , 6.9808 , 0.16786 , -0.990228 , 3.60921 ]), Female allosomes
960 , -104.905 , array([ 0.138088 , 0.988471 , 0.0511837 , 0.656036 , 6.9808 , 0.16786 , -0.990228 , 3.60921 ]), Male allosomes
Optimization completed.
-----------------------
Optimization run #3
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -219.611 , array([ 0.138088 , 0.26796 , 0.0511837 , 0.229963 , 6.9808 , 0.16786 , 0.0724899 , 3.60921 ]), Female allosomes
30 , -102.633 , array([ 0.138088 , 0.26796 , 0.0511837 , 0.229963 , 6.9808 , 0.16786 , 0.0724899 , 3.60921 ]), Male allosomes
60 , -217.76 , array([ 0.138088 , 0.330158 , 0.0511837 , 0.258073 , 6.9808 , 0.16786 , -0.0563844 , 3.60921 ]), Female allosomes
60 , -102.612 , array([ 0.138088 , 0.330158 , 0.0511837 , 0.258073 , 6.9808 , 0.16786 , -0.0563844 , 3.60921 ]), Male allosomes
90 , -215.93 , array([ 0.138088 , 0.397195 , 0.0511837 , 0.287552 , 6.9808 , 0.16786 , -0.176981 , 3.60921 ]), Female allosomes
90 , -102.657 , array([ 0.138088 , 0.397195 , 0.0511837 , 0.287552 , 6.9808 , 0.16786 , -0.176981 , 3.60921 ]), Male allosomes
120 , -214.223 , array([ 0.138088 , 0.459463 , 0.0511837 , 0.306589 , 6.9808 , 0.16786 , -0.295449 , 3.60921 ]), Female allosomes
120 , -102.69 , array([ 0.138088 , 0.459463 , 0.0511837 , 0.306589 , 6.9808 , 0.16786 , -0.295449 , 3.60921 ]), Male allosomes
150 , -212.645 , array([ 0.138088 , 0.512028 , 0.0511837 , 0.331656 , 6.9808 , 0.16786 , -0.407846 , 3.60921 ]), Female allosomes
150 , -102.741 , array([ 0.138088 , 0.512028 , 0.0511837 , 0.331656 , 6.9808 , 0.16786 , -0.407846 , 3.60921 ]), Male allosomes
180 , -211.216 , array([ 0.138088 , 0.561228 , 0.0511837 , 0.355578 , 6.9808 , 0.16786 , -0.508692 , 3.60921 ]), Female allosomes
180 , -102.819 , array([ 0.138088 , 0.561228 , 0.0511837 , 0.355578 , 6.9808 , 0.16786 , -0.508692 , 3.60921 ]), Male allosomes
210 , -209.94 , array([ 0.138088 , 0.606551 , 0.0511837 , 0.379809 , 6.9808 , 0.16786 , -0.597206 , 3.60921 ]), Female allosomes
210 , -102.922 , array([ 0.138088 , 0.606551 , 0.0511837 , 0.379809 , 6.9808 , 0.16786 , -0.597206 , 3.60921 ]), Male allosomes
240 , -209.21 , array([ 0.138088 , 0.626558 , 0.0511837 , 0.412538 , 6.9808 , 0.16786 , -0.64979 , 3.60921 ]), Female allosomes
240 , -102.998 , array([ 0.138088 , 0.626558 , 0.0511837 , 0.412538 , 6.9808 , 0.16786 , -0.64979 , 3.60921 ]), Male allosomes
270 , -208.182 , array([ 0.138088 , 0.672669 , 0.0511837 , 0.434601 , 6.9808 , 0.16786 , -0.710396 , 3.60921 ]), Female allosomes
270 , -103.162 , array([ 0.138088 , 0.672669 , 0.0511837 , 0.434601 , 6.9808 , 0.16786 , -0.710396 , 3.60921 ]), Male allosomes
300 , -207.267 , array([ 0.138088 , 0.713127 , 0.0511837 , 0.455425 , 6.9808 , 0.16786 , -0.766057 , 3.60921 ]), Female allosomes
300 , -103.318 , array([ 0.138088 , 0.713127 , 0.0511837 , 0.455425 , 6.9808 , 0.16786 , -0.766057 , 3.60921 ]), Male allosomes
330 , -206.46 , array([ 0.138088 , 0.752817 , 0.0511837 , 0.477174 , 6.9808 , 0.16786 , -0.810143 , 3.60921 ]), Female allosomes
330 , -103.496 , array([ 0.138088 , 0.752817 , 0.0511837 , 0.477174 , 6.9808 , 0.16786 , -0.810143 , 3.60921 ]), Male allosomes
360 , -205.78 , array([ 0.138088 , 0.784491 , 0.0511837 , 0.501337 , 6.9808 , 0.16786 , -0.847835 , 3.60921 ]), Female allosomes
360 , -103.661 , array([ 0.138088 , 0.784491 , 0.0511837 , 0.501337 , 6.9808 , 0.16786 , -0.847835 , 3.60921 ]), Male allosomes
390 , -205.197 , array([ 0.138088 , 0.813564 , 0.0511837 , 0.523369 , 6.9808 , 0.16786 , -0.87825 , 3.60921 ]), Female allosomes
390 , -103.822 , array([ 0.138088 , 0.813564 , 0.0511837 , 0.523369 , 6.9808 , 0.16786 , -0.87825 , 3.60921 ]), Male allosomes
420 , -204.693 , array([ 0.138088 , 0.839826 , 0.0511837 , 0.545707 , 6.9808 , 0.16786 , -0.902721 , 3.60921 ]), Female allosomes
420 , -103.981 , array([ 0.138088 , 0.839826 , 0.0511837 , 0.545707 , 6.9808 , 0.16786 , -0.902721 , 3.60921 ]), Male allosomes
450 , -204.27 , array([ 0.138088 , 0.863384 , 0.0511837 , 0.564749 , 6.9808 , 0.16786 , -0.922038 , 3.60921 ]), Female allosomes
450 , -104.127 , array([ 0.138088 , 0.863384 , 0.0511837 , 0.564749 , 6.9808 , 0.16786 , -0.922038 , 3.60921 ]), Male allosomes
480 , -203.921 , array([ 0.138088 , 0.882615 , 0.0511837 , 0.581851 , 6.9808 , 0.16786 , -0.93807 , 3.60921 ]), Female allosomes
480 , -104.253 , array([ 0.138088 , 0.882615 , 0.0511837 , 0.581851 , 6.9808 , 0.16786 , -0.93807 , 3.60921 ]), Male allosomes
510 , -203.621 , array([ 0.138088 , 0.901095 , 0.0511837 , 0.59639 , 6.9808 , 0.16786 , -0.950385 , 3.60921 ]), Female allosomes
510 , -104.372 , array([ 0.138088 , 0.901095 , 0.0511837 , 0.59639 , 6.9808 , 0.16786 , -0.950385 , 3.60921 ]), Male allosomes
540 , -203.366 , array([ 0.138088 , 0.916614 , 0.0511837 , 0.610609 , 6.9808 , 0.16786 , -0.960398 , 3.60921 ]), Female allosomes
540 , -104.48 , array([ 0.138088 , 0.916614 , 0.0511837 , 0.610609 , 6.9808 , 0.16786 , -0.960398 , 3.60921 ]), Male allosomes
570 , -203.137 , array([ 0.138088 , 0.931089 , 0.0511837 , 0.626605 , 6.9808 , 0.16786 , -0.967759 , 3.60921 ]), Female allosomes
570 , -104.592 , array([ 0.138088 , 0.931089 , 0.0511837 , 0.626605 , 6.9808 , 0.16786 , -0.967759 , 3.60921 ]), Male allosomes
600 , -202.952 , array([ 0.138088 , 0.942643 , 0.0511837 , 0.639563 , 6.9808 , 0.16786 , -0.974158 , 3.60921 ]), Female allosomes
600 , -104.682 , array([ 0.138088 , 0.942643 , 0.0511837 , 0.639563 , 6.9808 , 0.16786 , -0.974158 , 3.60921 ]), Male allosomes
630 , -202.799 , array([ 0.138088 , 0.952253 , 0.0511837 , 0.650831 , 6.9808 , 0.16786 , -0.979292 , 3.60921 ]), Female allosomes
630 , -104.759 , array([ 0.138088 , 0.952253 , 0.0511837 , 0.650831 , 6.9808 , 0.16786 , -0.979292 , 3.60921 ]), Male allosomes
660 , -202.676 , array([ 0.138088 , 0.960786 , 0.0511837 , 0.658265 , 6.9808 , 0.16786 , -0.983276 , 3.60921 ]), Female allosomes
660 , -104.821 , array([ 0.138088 , 0.960786 , 0.0511837 , 0.658265 , 6.9808 , 0.16786 , -0.983276 , 3.60921 ]), Male allosomes
690 , -202.555 , array([ 0.138088 , 0.964439 , 0.0511837 , 0.65905 , 6.9808 , 0.16786 , -0.984877 , 3.60921 ]), Female allosomes
690 , -104.818 , array([ 0.138088 , 0.964439 , 0.0511837 , 0.65905 , 6.9808 , 0.16786 , -0.984877 , 3.60921 ]), Male allosomes
720 , -202.529 , array([ 0.138088 , 0.966687 , 0.0511837 , 0.661263 , 6.9808 , 0.16786 , -0.985183 , 3.60921 ]), Female allosomes
720 , -104.836 , array([ 0.138088 , 0.966687 , 0.0511837 , 0.661263 , 6.9808 , 0.16786 , -0.985183 , 3.60921 ]), Male allosomes
750 , -202.507 , array([ 0.138088 , 0.968917 , 0.0511837 , 0.662334 , 6.9808 , 0.16786 , -0.985493 , 3.60921 ]), Female allosomes
750 , -104.85 , array([ 0.138088 , 0.968917 , 0.0511837 , 0.662334 , 6.9808 , 0.16786 , -0.985493 , 3.60921 ]), Male allosomes
780 , -202.486 , array([ 0.138088 , 0.971003 , 0.0511837 , 0.663624 , 6.9808 , 0.16786 , -0.985822 , 3.60921 ]), Female allosomes
780 , -104.865 , array([ 0.138088 , 0.971003 , 0.0511837 , 0.663624 , 6.9808 , 0.16786 , -0.985822 , 3.60921 ]), Male allosomes
810 , -202.469 , array([ 0.138088 , 0.972951 , 0.0511837 , 0.663807 , 6.9808 , 0.16786 , -0.986159 , 3.60921 ]), Female allosomes
810 , -104.875 , array([ 0.138088 , 0.972951 , 0.0511837 , 0.663807 , 6.9808 , 0.16786 , -0.986159 , 3.60921 ]), Male allosomes
840 , -202.453 , array([ 0.138088 , 0.974769 , 0.0511837 , 0.66387 , 6.9808 , 0.16786 , -0.986485 , 3.60921 ]), Female allosomes
840 , -104.885 , array([ 0.138088 , 0.974769 , 0.0511837 , 0.66387 , 6.9808 , 0.16786 , -0.986485 , 3.60921 ]), Male allosomes
870 , -202.44 , array([ 0.138088 , 0.976457 , 0.0511837 , 0.66346 , 6.9808 , 0.16786 , -0.986819 , 3.60921 ]), Female allosomes
870 , -104.892 , array([ 0.138088 , 0.976457 , 0.0511837 , 0.66346 , 6.9808 , 0.16786 , -0.986819 , 3.60921 ]), Male allosomes
900 , -202.429 , array([ 0.138088 , 0.978044 , 0.0511837 , 0.662675 , 6.9808 , 0.16786 , -0.987123 , 3.60921 ]), Female allosomes
900 , -104.898 , array([ 0.138088 , 0.978044 , 0.0511837 , 0.662675 , 6.9808 , 0.16786 , -0.987123 , 3.60921 ]), Male allosomes
930 , -202.417 , array([ 0.138088 , 0.979498 , 0.0511837 , 0.662614 , 6.9808 , 0.16786 , -0.987473 , 3.60921 ]), Female allosomes
930 , -104.905 , array([ 0.138088 , 0.979498 , 0.0511837 , 0.662614 , 6.9808 , 0.16786 , -0.987473 , 3.60921 ]), Male allosomes
960 , -202.41 , array([ 0.138088 , 0.980857 , 0.0511837 , 0.661197 , 6.9808 , 0.16786 , -0.987798 , 3.60921 ]), Female allosomes
960 , -104.907 , array([ 0.138088 , 0.980857 , 0.0511837 , 0.661197 , 6.9808 , 0.16786 , -0.987798 , 3.60921 ]), Male allosomes
990 , -202.4 , array([ 0.138088 , 0.982109 , 0.0511837 , 0.660681 , 6.9808 , 0.16786 , -0.98814 , 3.60921 ]), Female allosomes
990 , -104.912 , array([ 0.138088 , 0.982109 , 0.0511837 , 0.660681 , 6.9808 , 0.16786 , -0.98814 , 3.60921 ]), Male allosomes
1000 , -202.399 , array([ 0.138088 , 0.982505 , 0.0511837 , 0.660086 , 6.9808 , 0.16786 , -0.988257 , 3.60921 ]), Female allosomes
1000 , -104.912 , array([ 0.138088 , 0.982505 , 0.0511837 , 0.660086 , 6.9808 , 0.16786 , -0.988257 , 3.60921 ]), Male allosomes
Optimization completed.
-----------------------
In Step 2: Results from multiple optimization runs with different starting parameters:
----------------------------------------------------------------------------------------------------------------------------------------------
Run | LogLik | REUR | REUR_sex_bias | RAMR | RAMR_sex_bias | t1 | REUR2 | REUR2_sex_bias | t2
----------------------------------------------------------------------------------------------------------------------------------------------
1 | -307.192 | 0.1381 | 0.9879 | 0.05118 | 0.6547 | 6.981 | 0.1679 | -0.9902 | 3.609
2 | -307.191 | 0.1381 | 0.9885 | 0.05118 | 0.6561 | 6.981 | 0.1679 | -0.9902 | 3.609
3 | -307.311 | 0.1381 | 0.9825 | 0.05118 | 0.6601 | 6.981 | 0.1679 | -0.9883 | 3.609
----------------------------------------------------------------------------------------------------------------------------------------------
Selecting best parameters from step 2.
Step 2 used allosomal data only. Final likelihood is evaluated on autosomal + allosomal data at the selected optimal parameters.
--------------------------------------------------------------------------------------------------
Launching re-optimization until convergence is achieved or 5 re-optimizations have been performed.
--------------------------------------------------------------------------------------------------
Re-optimization 1/5: re-optimizing starting from the current optimal parameters (likelihood = -1290.919871).
-----------------------------------------------------------------------------------------------
Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RAMR', 't1', 'REUR2', 't2'].
-----------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -924.808 , array([ 0.134308 , 0.988471 , 0.050005 , 0.656054 , 6.97568 , 0.16314 , -0.990228 , 3.97437 ]), Autosomes
52 , -919.755 , array([ 0.134304 , 0.988471 , 0.0499284 , 0.656054 , 6.98012 , 0.163214 , -0.990228 , 3.97619 ]), Autosomes
Optimization completed.
-----------------------
----------------------------------------------------------------------------------------------------------------
Step 2 : Optimizing allosomal likelihood over parameters : ['REUR_sex_bias', 'RAMR_sex_bias', 'REUR2_sex_bias'].
----------------------------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -199.082 , array([ 0.134304 , 0.989256 , 0.0499284 , 0.695886 , 6.98012 , 0.163214 , -0.992033 , 3.97619 ]), Female allosomes
30 , -104.617 , array([ 0.134304 , 0.989256 , 0.0499284 , 0.695886 , 6.98012 , 0.163214 , -0.992033 , 3.97619 ]), Male allosomes
60 , -198.895 , array([ 0.134304 , 0.990269 , 0.0499284 , 0.72277 , 6.98012 , 0.163214 , -0.993742 , 3.97619 ]), Female allosomes
60 , -104.673 , array([ 0.134304 , 0.990269 , 0.0499284 , 0.72277 , 6.98012 , 0.163214 , -0.993742 , 3.97619 ]), Male allosomes
84 , -198.894 , array([ 0.134304 , 0.990292 , 0.0499284 , 0.723101 , 6.98012 , 0.163214 , -0.993724 , 3.97619 ]), Female allosomes
84 , -104.674 , array([ 0.134304 , 0.990292 , 0.0499284 , 0.723101 , 6.98012 , 0.163214 , -0.993724 , 3.97619 ]), Male allosomes
Optimization completed.
-----------------------
Change in likelihood from -1290.919871 to -1223.337001 after re-optimizing.
Re-optimization 2/5: re-optimizing starting from the current optimal parameters (likelihood = -1223.337001).
-----------------------------------------------------------------------------------------------
Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RAMR', 't1', 'REUR2', 't2'].
-----------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -909.112 , array([ 0.133727 , 0.990292 , 0.0496017 , 0.723101 , 6.98268 , 0.161335 , -0.993724 , 4.02735 ]), Autosomes
41 , -908.554 , array([ 0.133706 , 0.990292 , 0.0496013 , 0.723101 , 6.98 , 0.161342 , -0.993724 , 4.02774 ]), Autosomes
Optimization completed.
-----------------------
----------------------------------------------------------------------------------------------------------------
Step 2 : Optimizing allosomal likelihood over parameters : ['REUR_sex_bias', 'RAMR_sex_bias', 'REUR2_sex_bias'].
----------------------------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -198.986 , array([ 0.133706 , 0.989234 , 0.0496013 , 0.700091 , 6.98 , 0.161342 , -0.991923 , 4.02774 ]), Female allosomes
30 , -104.504 , array([ 0.133706 , 0.989234 , 0.0496013 , 0.700091 , 6.98 , 0.161342 , -0.991923 , 4.02774 ]), Male allosomes
60 , -198.777 , array([ 0.133706 , 0.990186 , 0.0496013 , 0.7341 , 6.98 , 0.161342 , -0.993654 , 4.02774 ]), Female allosomes
60 , -104.58 , array([ 0.133706 , 0.990186 , 0.0496013 , 0.7341 , 6.98 , 0.161342 , -0.993654 , 4.02774 ]), Male allosomes
83 , -198.773 , array([ 0.133706 , 0.990287 , 0.0496013 , 0.735228 , 6.98 , 0.161342 , -0.993626 , 4.02774 ]), Female allosomes
83 , -104.584 , array([ 0.133706 , 0.990287 , 0.0496013 , 0.735228 , 6.98 , 0.161342 , -0.993626 , 4.02774 ]), Male allosomes
Optimization completed.
-----------------------
Change in likelihood from -1223.337001 to -1211.910260 after re-optimizing.
Re-optimization 3/5: re-optimizing starting from the current optimal parameters (likelihood = -1211.910260).
-----------------------------------------------------------------------------------------------
Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RAMR', 't1', 'REUR2', 't2'].
-----------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -899.661 , array([ 0.132656 , 0.990287 , 0.0491074 , 0.735228 , 6.9886 , 0.159202 , -0.993626 , 3.99282 ]), Autosomes
44 , -896.426 , array([ 0.132667 , 0.990287 , 0.0491052 , 0.735228 , 6.98661 , 0.159189 , -0.993626 , 3.9938 ]), Autosomes
Optimization completed.
-----------------------
----------------------------------------------------------------------------------------------------------------
Step 2 : Optimizing allosomal likelihood over parameters : ['REUR_sex_bias', 'RAMR_sex_bias', 'REUR2_sex_bias'].
----------------------------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -199.094 , array([ 0.132667 , 0.989407 , 0.0491052 , 0.706317 , 6.98661 , 0.159189 , -0.99167 , 3.9938 ]), Female allosomes
30 , -104.147 , array([ 0.132667 , 0.989407 , 0.0491052 , 0.706317 , 6.98661 , 0.159189 , -0.99167 , 3.9938 ]), Male allosomes
60 , -198.919 , array([ 0.132667 , 0.99013 , 0.0491052 , 0.75347 , 6.98661 , 0.159189 , -0.993139 , 3.9938 ]), Female allosomes
60 , -104.285 , array([ 0.132667 , 0.99013 , 0.0491052 , 0.75347 , 6.98661 , 0.159189 , -0.993139 , 3.9938 ]), Male allosomes
90 , -198.846 , array([ 0.132667 , 0.990721 , 0.0491052 , 0.773166 , 6.98661 , 0.159189 , -0.993678 , 3.9938 ]), Female allosomes
90 , -104.345 , array([ 0.132667 , 0.990721 , 0.0491052 , 0.773166 , 6.98661 , 0.159189 , -0.993678 , 3.9938 ]), Male allosomes
101 , -198.771 , array([ 0.132667 , 0.990718 , 0.0491052 , 0.773073 , 6.98661 , 0.159189 , -0.993679 , 3.9938 ]), Female allosomes
101 , -104.317 , array([ 0.132667 , 0.990718 , 0.0491052 , 0.773073 , 6.98661 , 0.159189 , -0.993679 , 3.9938 ]), Male allosomes
Optimization completed.
-----------------------
Change in likelihood from -1211.910260 to -1199.514509 after re-optimizing.
Re-optimization 4/5: re-optimizing starting from the current optimal parameters (likelihood = -1199.514509).
-----------------------------------------------------------------------------------------------
Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RAMR', 't1', 'REUR2', 't2'].
-----------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -808.674 , array([ 0.126245 , 0.990718 , 0.0449965 , 0.773073 , 7.40338 , 0.145327 , -0.993679 , 4.03689 ]), Autosomes
60 , -703.24 , array([ 0.121505 , 0.990718 , 0.038452 , 0.773073 , 7.76688 , 0.122747 , -0.993679 , 4.12683 ]), Autosomes
90 , -639.27 , array([ 0.120142 , 0.990718 , 0.0335568 , 0.773073 , 7.81638 , 0.10455 , -0.993679 , 4.51475 ]), Autosomes
120 , -614.041 , array([ 0.120385 , 0.990718 , 0.0311943 , 0.773073 , 7.71029 , 0.092628 , -0.993679 , 4.93337 ]), Autosomes
150 , -610.871 , array([ 0.12174 , 0.990718 , 0.0309615 , 0.773073 , 7.76199 , 0.0915898 , -0.993679 , 4.98973 ]), Autosomes
157 , -610.828 , array([ 0.121745 , 0.990718 , 0.0309619 , 0.773073 , 7.7622 , 0.0915806 , -0.993679 , 4.98911 ]), Autosomes
Optimization completed.
-----------------------
----------------------------------------------------------------------------------------------------------------
Step 2 : Optimizing allosomal likelihood over parameters : ['REUR_sex_bias', 'RAMR_sex_bias', 'REUR2_sex_bias'].
----------------------------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -193.055 , array([ 0.121745 , 0.988794 , 0.0309619 , 0.724372 , 7.7622 , 0.0915806 , -0.989974 , 4.98911 ]), Female allosomes
30 , -98.4708 , array([ 0.121745 , 0.988794 , 0.0309619 , 0.724372 , 7.7622 , 0.0915806 , -0.989974 , 4.98911 ]), Male allosomes
60 , -192.755 , array([ 0.121745 , 0.989076 , 0.0309619 , 0.787259 , 7.7622 , 0.0915806 , -0.989938 , 4.98911 ]), Female allosomes
60 , -98.5778 , array([ 0.121745 , 0.989076 , 0.0309619 , 0.787259 , 7.7622 , 0.0915806 , -0.989938 , 4.98911 ]), Male allosomes
90 , -192.522 , array([ 0.121745 , 0.989477 , 0.0309619 , 0.836811 , 7.7622 , 0.0915806 , -0.98987 , 4.98911 ]), Female allosomes
90 , -98.6637 , array([ 0.121745 , 0.989477 , 0.0309619 , 0.836811 , 7.7622 , 0.0915806 , -0.98987 , 4.98911 ]), Male allosomes
120 , -192.397 , array([ 0.121745 , 0.989736 , 0.0309619 , 0.864014 , 7.7622 , 0.0915806 , -0.9898 , 4.98911 ]), Female allosomes
120 , -98.7113 , array([ 0.121745 , 0.989736 , 0.0309619 , 0.864014 , 7.7622 , 0.0915806 , -0.9898 , 4.98911 ]), Male allosomes
138 , -192.396 , array([ 0.121745 , 0.989752 , 0.0309619 , 0.86417 , 7.7622 , 0.0915806 , -0.989803 , 4.98911 ]), Female allosomes
138 , -98.7117 , array([ 0.121745 , 0.989752 , 0.0309619 , 0.86417 , 7.7622 , 0.0915806 , -0.989803 , 4.98911 ]), Male allosomes
Optimization completed.
-----------------------
Change in likelihood from -1199.514509 to -901.935180 after re-optimizing.
Re-optimization 5/5: re-optimizing starting from the current optimal parameters (likelihood = -901.935180).
-----------------------------------------------------------------------------------------------
Step 1 : Optimizing autosomal likelihood over parameters ['REUR', 'RAMR', 't1', 'REUR2', 't2'].
-----------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
27 , -610.715 , array([ 0.122802 , 0.989752 , 0.0309606 , 0.86417 , 7.7622 , 0.0915807 , -0.989803 , 4.9891 ]), Autosomes
Optimization completed.
-----------------------
----------------------------------------------------------------------------------------------------------------
Step 2 : Optimizing allosomal likelihood over parameters : ['REUR_sex_bias', 'RAMR_sex_bias', 'REUR2_sex_bias'].
----------------------------------------------------------------------------------------------------------------
Iter. Log-likelihood Model parameters Transmission
-------------------------------------------------------------
30 , -192.765 , array([ 0.122802 , 0.988785 , 0.0309606 , 0.724371 , 7.7622 , 0.0915807 , -0.989968 , 4.9891 ]), Female allosomes
30 , -98.6022 , array([ 0.122802 , 0.988785 , 0.0309606 , 0.724371 , 7.7622 , 0.0915807 , -0.989968 , 4.9891 ]), Male allosomes
60 , -192.467 , array([ 0.122802 , 0.98908 , 0.0309606 , 0.787328 , 7.7622 , 0.0915807 , -0.98992 , 4.9891 ]), Female allosomes
60 , -98.71 , array([ 0.122802 , 0.98908 , 0.0309606 , 0.787328 , 7.7622 , 0.0915807 , -0.98992 , 4.9891 ]), Male allosomes
90 , -192.237 , array([ 0.122802 , 0.989435 , 0.0309606 , 0.836823 , 7.7622 , 0.0915807 , -0.98985 , 4.9891 ]), Female allosomes
90 , -98.7961 , array([ 0.122802 , 0.989435 , 0.0309606 , 0.836823 , 7.7622 , 0.0915807 , -0.98985 , 4.9891 ]), Male allosomes
120 , -192.113 , array([ 0.122802 , 0.989593 , 0.0309606 , 0.864064 , 7.7622 , 0.0915807 , -0.989843 , 4.9891 ]), Female allosomes
120 , -98.8437 , array([ 0.122802 , 0.989593 , 0.0309606 , 0.864064 , 7.7622 , 0.0915807 , -0.989843 , 4.9891 ]), Male allosomes
134 , -192.113 , array([ 0.122802 , 0.989583 , 0.0309606 , 0.864159 , 7.7622 , 0.0915807 , -0.989835 , 4.9891 ]), Female allosomes
134 , -98.8438 , array([ 0.122802 , 0.989583 , 0.0309606 , 0.864159 , 7.7622 , 0.0915807 , -0.989835 , 4.9891 ]), Male allosomes
Optimization completed.
-----------------------
Change in likelihood from -901.935180 to -901.671747 after re-optimizing.
Convergence not achieved after 5 repetitions. Stopping re-optimization.
Final parameters and corresponding likelihood computed on autosomal + allosomal data:
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
LogLik | REUR | REUR_sex_bias | RAMR | RAMR_sex_bias | t1 | REUR2 | REUR2_sex_bias | t2 | X_AFR_rate | X_AFR_sex_bias
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-901.672 | 0.1228 | 0.9896 | 0.03096 | 0.8642 | 7.762 | 0.09158 | -0.9898 | 4.989 | 0.8462 | -0.9643
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Parameters X_AFR_rate, X_AFR_sex_bias correspond to the dependent ancestry and were not free in the optimization.
The optimal solution has sex-bias parameter(s) REUR_sex_bias, REUR2_sex_bias, X_AFR_sex_bias near their ±1 boundary. Re-running the optimization fixing these parameters near their boundary values may yield a better solution. Consider setting driver_spec.optim.rerun_optimization_on_boundaries to TRUE.
Predicted autosome proportions: [0.20321446 0.02812241 0.76866313]
Predicted allosome proportions: [0.21716446 0.0379582 0.74487734]
Results saved to : output_two_pulses
{'destination_dir': PosixPath('/home/runner/work/tracts/tracts/docs/source/auto_examples/ASW/output_two_pulses'), 'table_file': PosixPath('/home/runner/work/tracts/tracts/docs/source/auto_examples/ASW/output_two_pulses/ASW_test_output_optimal_parameters.txt')}
import sys
from pathlib import Path
from tracts.driver import run_tracts
# Read files automatically for online documentation
sys.path.append('.')
script_dir = Path.cwd()
driver_filename = script_dir / "ASW_two_pulses.yaml"
run_tracts(
driver_filename=str(driver_filename),
script_dir=str(script_dir),
)
Total running time of the script: (26 minutes 58.377 seconds)