In [7]:
%matplotlib inline
%cd ..
import warnings; warnings.filterwarnings('ignore')
/Users/celefthe/Programming/projects/language_decision
In [8]:
from utils import matparser
import glob
data_dir = '../data/patients/'
matparser.parse_dir(data_dir)
In [21]:
# TODO - Place in utils
import csv
def parse_condition(stim_num):
if stim_num == '1':
return 'SS'
if stim_num == '2':
return 'CP'
if stim_num == '3':
return 'CS'
if stim_num == '4':
return 'US'
csv_dir = '../data/patients_data/'
subjects = []
for csv_file in glob.glob(csv_dir + 'data*.csv'):
subject = []
subj_idx = csv_file[-9:-4] # Use id from filename
with open(csv_file, 'r') as f:
reader = csv.DictReader(f)
for trial in reader:
trial['subj_idx'] = subj_idx
trial['stim'] = parse_condition(trial['stim'])
subject.append(trial)
subjects.append(subject)
keys = subject[0].keys()
with open('../data/patients.csv', 'w') as out:
writer = csv.DictWriter(out, keys)
writer.writeheader()
for subj in subjects:
writer.writerows(subj)
In [34]:
import hddm
data = hddm.load_csv('../data/patients_clean.csv')
model = hddm.HDDM(data, depends_on={'v': 'stim'})
model.find_starting_values()
model.sample(6000, burn=20)
[-----------------100%-----------------] 6000 of 6000 complete in 200.6 sec
Out[34]:
<pymc.MCMC.MCMC at 0x12a0347b8>
In [29]:
model.print_stats()
mean std 2.5q 25q 50q 75q 97.5q mc err
a 2.93757 0.248159 2.46802 2.77635 2.93172 3.08576 3.4536 0.00518446
a_std 0.730792 0.233801 0.401985 0.568739 0.688453 0.8466 1.3287 0.00717605
a_subj.18438 2.22125 0.107598 2.01855 2.14785 2.21827 2.29168 2.44176 0.00171338
a_subj.18695 2.79992 0.158783 2.51062 2.69048 2.79197 2.90375 3.13279 0.00287265
a_subj.18942 2.2051 0.107233 2.00462 2.13093 2.2028 2.2758 2.42095 0.00180295
a_subj.19109 2.67073 0.574107 1.70703 2.27569 2.62205 3.00633 3.95192 0.0120079
a_subj.19719 3.43921 0.194899 3.07504 3.30138 3.43434 3.56504 3.83249 0.00458775
a_subj.20069 3.48925 0.600357 2.64212 3.08201 3.38692 3.75917 4.96801 0.0368087
a_subj.20372 3.23566 0.170471 2.92571 3.11719 3.22818 3.35056 3.58362 0.00419595
a_subj.20541 3.87092 0.38397 3.19622 3.59846 3.83752 4.11586 4.69966 0.0107279
a_subj.20709 3.38635 0.181256 3.05091 3.26081 3.3816 3.50294 3.76176 0.00404821
a_subj.21076 2.50105 0.130434 2.25639 2.41113 2.4959 2.58633 2.76629 0.00282004
v(CP) 0.207 0.325769 -0.431391 -0.0106161 0.204855 0.427603 0.853686 0.004862
v(CS) 0.288241 0.313236 -0.318122 0.081674 0.283334 0.48991 0.917777 0.00452185
v(SS) 1.0509 0.322918 0.435725 0.829838 1.04725 1.26431 1.69338 0.00531726
v(US) 0.65777 0.321864 0.0147717 0.44328 0.657982 0.86952 1.29747 0.00477982
v_std 0.941498 0.134677 0.715894 0.848074 0.928598 1.02032 1.24293 0.00333045
v_subj(CP).18438 -0.0433419 0.220476 -0.460025 -0.196002 -0.0428974 0.108443 0.379986 0.00376868
v_subj(CP).18695 0.533348 0.195618 0.157158 0.399 0.531603 0.662809 0.916696 0.00346753
v_subj(CP).18942 0.525163 0.209827 0.113369 0.385202 0.522291 0.669456 0.935372 0.00369642
v_subj(CP).19719 1.2971 0.218683 0.871122 1.15108 1.29342 1.44041 1.73239 0.00522121
v_subj(CP).20069 -1.55649 0.329213 -2.21813 -1.7774 -1.54625 -1.33531 -0.926757 0.0118736
v_subj(CP).20372 0.376379 0.187846 -0.00248434 0.257275 0.379258 0.500925 0.748596 0.00384472
v_subj(CP).20541 -0.602648 0.241747 -1.0865 -0.761046 -0.598406 -0.442825 -0.132057 0.00479717
v_subj(CP).20709 0.373685 0.160023 0.0539441 0.267116 0.374637 0.478986 0.692653 0.00375295
v_subj(CP).21076 0.732181 0.252232 0.24114 0.559878 0.733142 0.905356 1.21367 0.00487386
v_subj(CS).18438 0.51369 0.204406 0.0981049 0.379557 0.513735 0.652071 0.912753 0.00401461
v_subj(CS).18695 0.731491 0.220695 0.300657 0.579578 0.735081 0.881123 1.15947 0.00344778
v_subj(CS).18942 -0.61787 0.239383 -1.09647 -0.778484 -0.613801 -0.453775 -0.163396 0.00545158
v_subj(CS).19109 -0.249348 0.670498 -1.57447 -0.690308 -0.239782 0.188846 1.09475 0.00872673
v_subj(CS).19719 0.960236 0.205662 0.55939 0.820786 0.959421 1.09728 1.36886 0.0041463
v_subj(CS).20069 -1.2593 0.285139 -1.84603 -1.44397 -1.25606 -1.06672 -0.70428 0.00921854
v_subj(CS).20372 1.22053 0.192317 0.845188 1.08699 1.21861 1.35142 1.59316 0.00402012
v_subj(CS).20541 -0.123441 0.19407 -0.510425 -0.255586 -0.123867 0.00989563 0.259871 0.00255487
v_subj(CS).20709 0.411012 0.148747 0.122904 0.307135 0.411593 0.51405 0.697484 0.0026673
v_subj(CS).21076 1.11703 0.21865 0.69045 0.972215 1.11744 1.26376 1.54374 0.00471143
v_subj(SS).18438 1.44458 0.342973 0.763528 1.21658 1.44471 1.67674 2.11597 0.00785456
v_subj(SS).18695 1.00496 0.266003 0.490126 0.82297 1.00799 1.1825 1.52893 0.00582832
v_subj(SS).18942 -0.557444 0.304053 -1.15119 -0.768275 -0.558293 -0.34945 0.0276494 0.00659002
v_subj(SS).19109 0.899434 0.605424 -0.228762 0.491974 0.874348 1.28387 2.14442 0.0104837
v_subj(SS).19719 1.02711 0.189211 0.650751 0.89892 1.02495 1.15515 1.39852 0.00372666
v_subj(SS).20069 1.92047 0.317474 1.30235 1.70317 1.9128 2.13521 2.5462 0.0115252
v_subj(SS).20372 1.21957 0.215123 0.801392 1.07014 1.22014 1.36598 1.64501 0.00497393
v_subj(SS).20541 1.24286 0.215928 0.842326 1.09508 1.23871 1.3832 1.68179 0.00498976
v_subj(SS).20709 1.0552 0.205338 0.649255 0.919991 1.0529 1.19044 1.45389 0.00439836
v_subj(SS).21076 1.12135 0.264489 0.611171 0.940863 1.12001 1.29779 1.64672 0.00530148
v_subj(US).18438 0.273191 0.198528 -0.124105 0.143669 0.27312 0.403701 0.668555 0.00345088
v_subj(US).18695 0.737978 0.189145 0.364964 0.608333 0.73385 0.868247 1.10596 0.00320283
v_subj(US).18942 -0.70943 0.220744 -1.15185 -0.859207 -0.707077 -0.557238 -0.283481 0.00431427
v_subj(US).19109 1.09726 0.717247 -0.249949 0.613179 1.08016 1.56476 2.58659 0.0114714
v_subj(US).19719 1.91439 0.238466 1.43858 1.75319 1.91701 2.07119 2.38479 0.00588449
v_subj(US).20069 -1.32717 0.29081 -1.90637 -1.51869 -1.31592 -1.12659 -0.780041 0.00996089
v_subj(US).20372 1.74465 0.219187 1.3165 1.59864 1.7423 1.89364 2.16666 0.00541297
v_subj(US).20541 -0.205768 0.562383 -1.33199 -0.583079 -0.202418 0.171365 0.915876 0.00814454
v_subj(US).20709 1.3249 0.201995 0.932944 1.19203 1.32519 1.46067 1.71672 0.00512755
v_subj(US).21076 1.56339 0.233553 1.10949 1.40806 1.56238 1.71954 2.02199 0.00453696
t 0.490609 0.155361 0.242441 0.379133 0.471763 0.582429 0.846851 0.00535743
t_std 0.939604 0.326444 0.459833 0.700959 0.885986 1.13468 1.71581 0.0128392
t_subj.18438 0.917719 0.0224011 0.866558 0.904387 0.920722 0.933926 0.952074 0.000538377
t_subj.18695 0.726882 0.0399123 0.634944 0.705262 0.732468 0.754897 0.786971 0.000970439
t_subj.18942 0.796809 0.0158325 0.759367 0.787887 0.799517 0.808343 0.819336 0.000347697
t_subj.19109 1.77186 0.397966 0.550129 1.65678 1.89306 2.03767 2.16475 0.00994898
t_subj.19719 0.00352563 0.00579792 5.70085e-10 5.18753e-05 0.000866384 0.00458257 0.0212112 0.00020634
t_subj.20069 0.523871 0.0783996 0.330508 0.482698 0.53555 0.579572 0.641147 0.00435468
t_subj.20372 0.0149708 0.0181962 6.67379e-09 0.000287506 0.00677294 0.025249 0.0605487 0.000766
t_subj.20541 7.21275e-05 0.000132774 4.18998e-11 8.49002e-07 1.37124e-05 8.24522e-05 0.000466367 5.33889e-06
t_subj.20709 0.363641 0.0489458 0.251207 0.334327 0.36894 0.39833 0.444138 0.00125019
t_subj.21076 0.892926 0.0205845 0.843502 0.882116 0.896209 0.907452 0.923951 0.000431217
z(CP) 0.41367 0.0371189 0.343575 0.388408 0.413402 0.438001 0.487732 0.00107318
z(CS) 0.342364 0.0327177 0.281885 0.319291 0.341791 0.363792 0.408135 0.000784862
z(SS) 0.45044 0.0373395 0.379823 0.42491 0.449477 0.475972 0.524174 0.000938754
z(US) 0.370573 0.0366629 0.299447 0.345244 0.370522 0.395286 0.441376 0.000972596
z_std 0.357371 0.0283686 0.303737 0.337988 0.356491 0.375857 0.41483 0.000609971
z_subj(CP).18438 0.389744 0.0504095 0.292204 0.35523 0.388495 0.422906 0.493028 0.00104288
z_subj(CP).18695 0.332028 0.050765 0.24001 0.296661 0.329449 0.365358 0.43661 0.00106664
z_subj(CP).18942 0.516072 0.0544961 0.406209 0.479735 0.517076 0.5529 0.619612 0.00107885
z_subj(CP).19719 0.339895 0.0564281 0.234278 0.300325 0.337902 0.376861 0.455705 0.00118296
z_subj(CP).20069 0.418236 0.0628693 0.301683 0.374437 0.415674 0.459181 0.550891 0.00192459
z_subj(CP).20372 0.455978 0.0613389 0.339014 0.413991 0.455172 0.49758 0.580578 0.00143981
z_subj(CP).20541 0.497129 0.0793674 0.345984 0.443537 0.49523 0.551007 0.65405 0.00174484
z_subj(CP).20709 0.417926 0.0588932 0.307463 0.376978 0.416311 0.456795 0.536617 0.00149317
z_subj(CP).21076 0.333314 0.0493082 0.241158 0.299078 0.331099 0.36566 0.433625 0.00104051
z_subj(CS).18438 0.380503 0.0532369 0.283835 0.343008 0.378437 0.415656 0.488021 0.00110343
z_subj(CS).18695 0.319596 0.0531237 0.22069 0.281574 0.317107 0.353912 0.430882 0.000839786
z_subj(CS).18942 0.494794 0.0559677 0.387859 0.456044 0.49425 0.533431 0.608182 0.00126965
z_subj(CS).19109 0.356727 0.0840738 0.205539 0.297017 0.35287 0.412255 0.532587 0.0015172
z_subj(CS).19719 0.502225 0.0589059 0.382993 0.462749 0.502711 0.541953 0.616121 0.00135237
z_subj(CS).20069 0.420777 0.0617535 0.306611 0.377939 0.417909 0.460687 0.550159 0.00191428
z_subj(CS).20372 0.260212 0.0420986 0.182159 0.230929 0.258224 0.288501 0.348025 0.000905326
z_subj(CS).20541 0.0773431 0.0127526 0.0546311 0.0683047 0.0766867 0.0854581 0.104157 0.000310201
z_subj(CS).20709 0.383179 0.0565531 0.277982 0.343952 0.380712 0.41983 0.498575 0.00112971
z_subj(CS).21076 0.340303 0.0533337 0.240277 0.302256 0.338461 0.37624 0.450681 0.00102851
z_subj(SS).18438 0.613343 0.0607957 0.485429 0.573667 0.616959 0.655327 0.721492 0.00158071
z_subj(SS).18695 0.595835 0.0607508 0.468357 0.557528 0.599257 0.638955 0.704788 0.00154359
z_subj(SS).18942 0.252321 0.0396721 0.182966 0.2237 0.250469 0.277427 0.336993 0.00094951
z_subj(SS).19109 0.394199 0.0861311 0.236881 0.332869 0.390521 0.45114 0.572159 0.00195648
z_subj(SS).19719 0.371574 0.0565748 0.266945 0.331527 0.368886 0.410028 0.487258 0.00121107
z_subj(SS).20069 0.52021 0.0654955 0.391229 0.476435 0.521761 0.566249 0.642948 0.00226393
z_subj(SS).20372 0.428686 0.0589842 0.312639 0.388111 0.428009 0.468667 0.544228 0.00147496
z_subj(SS).20541 0.332066 0.0574607 0.225682 0.29287 0.330088 0.369122 0.451276 0.00109909
z_subj(SS).20709 0.395262 0.0602039 0.279658 0.354891 0.394795 0.435178 0.519185 0.00125931
z_subj(SS).21076 0.611914 0.0533521 0.499316 0.578337 0.614463 0.648618 0.709501 0.00121745
z_subj(US).18438 0.399252 0.0533676 0.298856 0.362219 0.398573 0.435881 0.506736 0.000930849
z_subj(US).18695 0.350267 0.0547415 0.24752 0.311441 0.348292 0.386688 0.466192 0.000989861
z_subj(US).18942 0.516122 0.059784 0.39856 0.475315 0.516974 0.556361 0.630189 0.00120897
z_subj(US).19109 0.373916 0.0846546 0.219166 0.314244 0.369665 0.430357 0.547929 0.00141849
z_subj(US).19719 0.270714 0.0502444 0.179027 0.23625 0.267895 0.301963 0.378049 0.00113769
z_subj(US).20069 0.41603 0.0604542 0.303719 0.373918 0.413217 0.455181 0.542466 0.00176863
z_subj(US).20372 0.289556 0.0511657 0.196801 0.253323 0.287164 0.321844 0.395589 0.00108684
z_subj(US).20541 0.380156 0.0878547 0.222366 0.317708 0.376709 0.43674 0.566997 0.00136139
z_subj(US).20709 0.370963 0.0630071 0.255699 0.327387 0.368884 0.412148 0.502729 0.00176673
z_subj(US).21076 0.314801 0.0514131 0.220962 0.278906 0.312991 0.347852 0.420969 0.000972482
DIC: 2483.168479
deviance: 2411.276204
pD: 71.892276
In [37]:
model.plot_posteriors()
Plotting a
Plotting a_std
Plotting v(CP)
Plotting v(CS)
Plotting v(SS)
Plotting v(US)
Plotting v_std
Plotting t
Plotting t_std
In [35]:
v_SS, v_CP, v_CS, v_US = model.nodes_db.node[['v(SS)', 'v(CP)', 'v(CS)', 'v(US)']]
hddm.analyze.plot_posterior_nodes([v_SS, v_CP, v_CS, v_US])
In [40]:
print('P(SS > US) = ' + str((v_SS.trace() > v_US.trace()).mean()))
print('P(CP > SS) = ' + str((v_CP.trace() > v_SS.trace()).mean()))
print('P(CS > SS) = ' + str((v_CS.trace() > v_SS.trace()).mean()))
print('P(CP > CS) = ' + str((v_CP.trace() > v_CS.trace()).mean()))
P(SS > US) = 0.862876254181
P(CP > SS) = 0.0282608695652
P(CS > SS) = 0.0277591973244
P(CP > CS) = 0.48143812709
In [32]:
models = []
for i in range(5):
m = hddm.HDDM(data, depends_on={'v': 'stim'})
m.find_starting_values()
m.sample(6000, burn=20)
models.append(m)
hddm.analyze.gelman_rubin(models)
[-----------------100%-----------------] 6000 of 6000 complete in 193.2 sec
Out[32]:
{'a': 1.0004208275167732,
'a_std': 1.0003031423213653,
'a_subj.18438': 1.0001549932498317,
'a_subj.18695': 1.0004439701282759,
'a_subj.18942': 1.0003736081034036,
'a_subj.19109': 1.0025949877970943,
'a_subj.19719': 1.0004406474106762,
'a_subj.20069': 1.0022167566579805,
'a_subj.20372': 1.0002377663768598,
'a_subj.20541': 1.0003131372857155,
'a_subj.20709': 1.0007775713292291,
'a_subj.21076': 1.0001191797941615,
't': 1.0041261451867365,
't_std': 1.0046000145731637,
't_subj.18438': 1.0001972053413895,
't_subj.18695': 1.0001969317573627,
't_subj.18942': 1.0006364023910332,
't_subj.19109': 1.0093439743374819,
't_subj.19719': 1.0034493791915193,
't_subj.20069': 1.0024347139388876,
't_subj.20372': 1.0014593353818224,
't_subj.20541': 1.0014418983240683,
't_subj.20709': 0.99996861453560482,
't_subj.21076': 1.0000609525222015,
'v(CP)': 0.99994152881358211,
'v(CS)': 1.0003337801190839,
'v(SS)': 1.0002182799946662,
'v(US)': 0.99990096467586753,
'v_std': 0.99993474496149604,
'v_subj(CP).18438': 0.999950915931204,
'v_subj(CP).18695': 0.99993975558756465,
'v_subj(CP).18942': 0.99996781009786984,
'v_subj(CP).19719': 1.0001992407681941,
'v_subj(CP).20069': 1.0002167575052987,
'v_subj(CP).20372': 1.0000667401815879,
'v_subj(CP).20541': 0.99986030559487804,
'v_subj(CP).20709': 1.0002884293069054,
'v_subj(CP).21076': 1.000193845818496,
'v_subj(CS).18438': 1.0000291807833479,
'v_subj(CS).18695': 0.99987769106070556,
'v_subj(CS).18942': 0.99996399580675521,
'v_subj(CS).19109': 1.0000440844141385,
'v_subj(CS).19719': 1.0007486726097459,
'v_subj(CS).20069': 1.0010016162749147,
'v_subj(CS).20372': 0.99987461870647032,
'v_subj(CS).20541': 1.0000494641303297,
'v_subj(CS).20709': 0.99995040209987107,
'v_subj(CS).21076': 0.99985079915462904,
'v_subj(SS).18438': 0.9999274419747044,
'v_subj(SS).18695': 1.0004319343514885,
'v_subj(SS).18942': 1.0000145880755873,
'v_subj(SS).19109': 1.0002289707856569,
'v_subj(SS).19719': 0.99994233586129999,
'v_subj(SS).20069': 1.0004096904198938,
'v_subj(SS).20372': 0.99994995988525415,
'v_subj(SS).20541': 1.0001927517259068,
'v_subj(SS).20709': 1.0005958455843258,
'v_subj(SS).21076': 1.0001879292485294,
'v_subj(US).18438': 1.0004620676047287,
'v_subj(US).18695': 1.0000333007582147,
'v_subj(US).18942': 1.0000274120892936,
'v_subj(US).19109': 1.0003188635304581,
'v_subj(US).19719': 1.0000748269885653,
'v_subj(US).20069': 1.000831251820965,
'v_subj(US).20372': 1.000129806662323,
'v_subj(US).20541': 0.99996760076334179,
'v_subj(US).20709': 1.0001924357511625,
'v_subj(US).21076': 0.9998807031014707}
Models converge!
In [48]:
model_bias = hddm.HDDM(data, depends_on={'v': 'stim'}, bias=True)
model_bias.find_starting_values()
model_bias.sample(9000, burn=200)
[-----------------100%-----------------] 9000 of 9000 complete in 377.9 sec
Out[48]:
<pymc.MCMC.MCMC at 0x1272376a0>
In [49]:
model_bias.print_stats()
mean std 2.5q 25q 50q 75q 97.5q mc err
a 2.83853 0.227006 2.42379 2.68967 2.82664 2.97618 3.32349 0.00421897
a_std 0.659414 0.21952 0.365094 0.512054 0.617063 0.758412 1.21575 0.0058521
a_subj.18438 2.19831 0.108048 2.00314 2.12285 2.19451 2.26779 2.42488 0.00195835
a_subj.18695 2.72684 0.156634 2.44703 2.61595 2.71757 2.82804 3.05622 0.00271279
a_subj.18942 2.15589 0.10923 1.95451 2.08035 2.151 2.22659 2.38716 0.00166397
a_subj.19109 2.61206 0.516162 1.7362 2.25141 2.5678 2.91097 3.72607 0.0100356
a_subj.19719 3.24971 0.163918 2.93852 3.13703 3.24291 3.35776 3.5816 0.00288297
a_subj.20069 3.29185 0.498023 2.5463 2.95499 3.21929 3.55607 4.39689 0.0222001
a_subj.20372 3.14318 0.150781 2.85174 3.04061 3.14101 3.24535 3.44045 0.00279137
a_subj.20541 3.56064 0.34321 2.95181 3.31627 3.53837 3.78143 4.29852 0.0101075
a_subj.20709 3.36476 0.175408 3.0383 3.24671 3.35771 3.47398 3.73931 0.0027432
a_subj.21076 2.40627 0.127186 2.1697 2.31767 2.40124 2.48757 2.67649 0.00203994
v(CP) 0.254084 0.322704 -0.379822 0.0394421 0.250579 0.465401 0.882639 0.00368498
v(CS) 0.264718 0.312978 -0.349111 0.0583392 0.264123 0.474212 0.879941 0.00383217
v(SS) 1.2368 0.31804 0.616469 1.02455 1.24064 1.44744 1.86493 0.00424158
v(US) 0.610224 0.322184 -0.0345287 0.397808 0.613889 0.820852 1.23993 0.00371678
v_std 0.950145 0.133051 0.730883 0.855783 0.936641 1.02862 1.256 0.00243401
v_subj(CP).18438 -0.139873 0.192573 -0.517949 -0.270386 -0.138843 -0.0114844 0.238577 0.00250012
v_subj(CP).18695 0.407463 0.169431 0.0692717 0.294941 0.4081 0.518898 0.742686 0.00217087
v_subj(CP).18942 0.655094 0.186051 0.289154 0.530449 0.65728 0.780858 1.01979 0.00250367
v_subj(CP).19719 1.20541 0.177341 0.863695 1.08525 1.20776 1.32491 1.55285 0.00296536
v_subj(CP).20069 -1.59561 0.277181 -2.13899 -1.78246 -1.59169 -1.40636 -1.05505 0.00644834
v_subj(CP).20372 0.613082 0.161966 0.294142 0.505036 0.612017 0.720961 0.934503 0.00208048
v_subj(CP).20541 -0.00986494 0.198955 -0.400407 -0.143739 -0.00679677 0.126459 0.376715 0.00215949
v_subj(CP).20709 0.434287 0.139112 0.157725 0.342229 0.436499 0.527243 0.707529 0.00169762
v_subj(CP).21076 0.544602 0.213266 0.126895 0.400179 0.547581 0.688335 0.965644 0.00259644
v_subj(CS).18438 0.381831 0.181881 0.0245479 0.259533 0.381207 0.504091 0.73433 0.00209324
v_subj(CS).18695 0.56222 0.200218 0.179286 0.426156 0.563662 0.697305 0.960408 0.00212115
v_subj(CS).18942 -0.431783 0.197841 -0.823436 -0.564572 -0.433723 -0.30075 -0.0409706 0.00216352
v_subj(CS).19109 -0.246247 0.672057 -1.60295 -0.685605 -0.240907 0.202577 1.05935 0.00764481
v_subj(CS).19719 1.26211 0.178977 0.911646 1.14082 1.26201 1.38456 1.61262 0.00278233
v_subj(CS).20069 -1.28936 0.244819 -1.7759 -1.45274 -1.28507 -1.12764 -0.810072 0.00594073
v_subj(CS).20372 1.06433 0.173218 0.725039 0.946675 1.06458 1.18004 1.41198 0.0027135
v_subj(CS).20541 -0.179827 0.195096 -0.566495 -0.311849 -0.180701 -0.04689 0.200515 0.00213044
v_subj(CS).20709 0.412114 0.132941 0.146548 0.321653 0.415471 0.502835 0.662109 0.00193215
v_subj(CS).21076 0.944529 0.185719 0.582507 0.81836 0.945686 1.07101 1.3095 0.00251358
v_subj(SS).18438 2.0235 0.280139 1.46984 1.83303 2.0292 2.21645 2.56444 0.00444831
v_subj(SS).18695 1.48914 0.226733 1.03806 1.33483 1.49068 1.64558 1.92309 0.00288281
v_subj(SS).18942 -1.16285 0.269834 -1.69558 -1.34498 -1.16195 -0.982571 -0.634686 0.00384616
v_subj(SS).19109 1.11818 0.608155 -0.0486973 0.704933 1.10868 1.52174 2.36847 0.00928305
v_subj(SS).19719 1.01824 0.156607 0.711668 0.912486 1.01767 1.12285 1.32815 0.00194852
v_subj(SS).20069 2.01575 0.293368 1.44298 1.81715 2.0078 2.21092 2.61791 0.007624
v_subj(SS).20372 1.43489 0.18881 1.06316 1.30919 1.43332 1.56367 1.80399 0.00310555
v_subj(SS).20541 1.52881 0.218556 1.12036 1.37798 1.52395 1.6754 1.96948 0.00578149
v_subj(SS).20709 1.09118 0.17013 0.75433 0.977559 1.09265 1.20321 1.4267 0.00260153
v_subj(SS).21076 1.72613 0.235126 1.26972 1.56802 1.72481 1.88641 2.18788 0.00335211
v_subj(US).18438 0.195389 0.174956 -0.152462 0.0768174 0.197656 0.316249 0.531393 0.00255793
v_subj(US).18695 0.637325 0.170916 0.301126 0.521934 0.639637 0.753255 0.969843 0.00188182
v_subj(US).18942 -0.498602 0.186519 -0.865881 -0.624804 -0.499171 -0.372974 -0.135124 0.00244437
v_subj(US).19109 1.13429 0.739158 -0.279638 0.638947 1.11737 1.6332 2.60317 0.00986388
v_subj(US).19719 1.61554 0.189248 1.24814 1.48507 1.61655 1.74161 1.98984 0.00306306
v_subj(US).20069 -1.37612 0.254872 -1.8741 -1.54883 -1.37204 -1.20352 -0.885262 0.00611191
v_subj(US).20372 1.61095 0.191854 1.23546 1.48336 1.60909 1.73998 1.99215 0.00287472
v_subj(US).20541 0.0828297 0.552516 -1.01498 -0.287193 0.0839226 0.463865 1.14872 0.00594341
v_subj(US).20709 1.29973 0.170628 0.963116 1.1836 1.29786 1.41642 1.63378 0.00246363
v_subj(US).21076 1.29249 0.197236 0.906648 1.16283 1.29187 1.42372 1.68435 0.00274024
t 0.487952 0.152014 0.241246 0.380027 0.470381 0.578993 0.821706 0.00411907
t_std 0.986949 0.339989 0.47525 0.739003 0.934077 1.18441 1.78312 0.0103268
t_subj.18438 0.871342 0.0252429 0.815432 0.855523 0.873501 0.889426 0.914044 0.000471007
t_subj.18695 0.647703 0.0496732 0.532773 0.618748 0.65301 0.682796 0.729275 0.000880623
t_subj.18942 0.743333 0.0253945 0.687588 0.728258 0.745842 0.761239 0.785608 0.000457795
t_subj.19109 1.86999 0.318983 0.989953 1.77829 1.96311 2.0761 2.17633 0.00836867
t_subj.19719 0.00177719 0.00319769 1.02233e-10 1.25015e-05 0.000358483 0.00202417 0.011734 0.000107172
t_subj.20069 0.506239 0.0745084 0.34136 0.46526 0.517173 0.557722 0.619074 0.00314451
t_subj.20372 0.00728487 0.0108263 6.74973e-10 8.77144e-05 0.00207516 0.0101841 0.0387591 0.000351027
t_subj.20541 3.42907e-05 6.64653e-05 1.07443e-10 3.19522e-07 5.71273e-06 3.80898e-05 0.000224071 1.74559e-06
t_subj.20709 0.369264 0.0433502 0.269479 0.343916 0.373714 0.400249 0.441523 0.00059818
t_subj.21076 0.844649 0.0282023 0.780291 0.828001 0.847671 0.864834 0.890764 0.000489442
z 0.353676 0.0233672 0.308719 0.337433 0.353205 0.369307 0.400289 0.000422296
z_std 0.269465 0.0278798 0.218092 0.250082 0.268465 0.287599 0.326938 0.000560351
z_subj.18438 0.43671 0.0319162 0.376273 0.41485 0.436563 0.45867 0.49883 0.000647968
z_subj.18695 0.383703 0.0331586 0.319267 0.361252 0.383371 0.406186 0.448823 0.000474516
z_subj.18942 0.43323 0.0311604 0.372919 0.411656 0.432767 0.453715 0.495891 0.000574239
z_subj.19109 0.333918 0.0585987 0.22606 0.29292 0.331849 0.372161 0.454195 0.000865926
z_subj.19719 0.348349 0.0321246 0.286989 0.326428 0.347983 0.36953 0.412979 0.000551756
z_subj.20069 0.460242 0.0326012 0.399576 0.437527 0.459436 0.48207 0.526998 0.000665276
z_subj.20372 0.319957 0.029758 0.262172 0.299527 0.319878 0.339946 0.379096 0.000520492
z_subj.20541 0.119658 0.016524 0.0900467 0.10797 0.118956 0.130482 0.153951 0.000461946
z_subj.20709 0.380506 0.0336484 0.316358 0.357412 0.380096 0.403198 0.447538 0.000564318
z_subj.21076 0.380338 0.0326174 0.317109 0.358022 0.380226 0.401925 0.445895 0.000563583
DIC: 2618.886470
deviance: 2561.267869
pD: 57.618601
In [50]:
v_bSS, v_bCP, v_bCS, v_bUS = model_bias.nodes_db.node[['v(SS)', 'v(CP)', 'v(CS)', 'v(US)']]
hddm.analyze.plot_posterior_nodes([v_bSS, v_bCP, v_bCS, v_bUS])
In [55]:
model_bias = hddm.HDDM(data, depends_on={'v': 'stim', 'z': 'stim'}, bias=True)
model_bias.find_starting_values()
model_bias.sample(10000, burn=200)
[-----------------100%-----------------] 10000 of 10000 complete in 448.8 sec
Out[55]:
<pymc.MCMC.MCMC at 0x116d0ada0>
In [56]:
model_bias.print_stats()
mean std 2.5q 25q 50q 75q 97.5q mc err
a 2.9456 0.257963 2.45679 2.7804 2.93363 3.10038 3.49243 0.00471275
a_std 0.744006 0.245184 0.410632 0.573196 0.695083 0.861974 1.36678 0.00770908
a_subj.18438 2.22336 0.108157 2.01787 2.14965 2.22082 2.29395 2.44574 0.0015609
a_subj.18695 2.79477 0.155762 2.50554 2.68671 2.78882 2.89797 3.11331 0.00212321
a_subj.18942 2.19967 0.105692 1.99944 2.12658 2.19571 2.26828 2.41692 0.00141666
a_subj.19109 2.68192 0.583764 1.72183 2.27619 2.62334 3.02968 3.97272 0.0119671
a_subj.19719 3.44027 0.196063 3.0816 3.30428 3.43015 3.56599 3.85111 0.00357664
a_subj.20069 3.55616 0.657067 2.61808 3.1045 3.45411 3.87989 5.11695 0.0381119
a_subj.20372 3.23824 0.168214 2.92817 3.12397 3.23173 3.34727 3.5879 0.00270076
a_subj.20541 3.89027 0.404657 3.19072 3.5964 3.8579 4.15118 4.76828 0.00909072
a_subj.20709 3.38941 0.182557 3.05092 3.26514 3.38365 3.50789 3.77331 0.00314505
a_subj.21076 2.50918 0.133226 2.25869 2.41931 2.50525 2.59613 2.78678 0.0022826
v(CP) 0.203141 0.326794 -0.433149 -0.0183928 0.205004 0.420721 0.836524 0.00386083
v(CS) 0.287438 0.312135 -0.318454 0.0792852 0.281665 0.493113 0.913788 0.00390824
v(SS) 1.05057 0.319783 0.419698 0.837863 1.05021 1.26218 1.69393 0.00413703
v(US) 0.657653 0.32981 0.0118969 0.43579 0.657471 0.873756 1.31404 0.00442096
v_std 0.943831 0.132866 0.718053 0.850776 0.93144 1.02155 1.24549 0.0026487
v_subj(CP).18438 -0.0402273 0.218921 -0.46417 -0.189882 -0.041175 0.106444 0.387211 0.00318535
v_subj(CP).18695 0.53476 0.191541 0.161151 0.403745 0.535583 0.664166 0.911086 0.00251307
v_subj(CP).18942 0.531798 0.211813 0.114692 0.388352 0.533996 0.675085 0.944934 0.00301455
v_subj(CP).19719 1.2979 0.220538 0.865668 1.15226 1.29621 1.4474 1.72874 0.00373736
v_subj(CP).20069 -1.57561 0.333253 -2.24779 -1.79345 -1.5687 -1.34092 -0.945708 0.00983717
v_subj(CP).20372 0.376234 0.192405 0.00323187 0.246857 0.376018 0.505764 0.753685 0.00302055
v_subj(CP).20541 -0.606948 0.241577 -1.08175 -0.768123 -0.603057 -0.446745 -0.135517 0.00360805
v_subj(CP).20709 0.373805 0.155708 0.0759795 0.267273 0.372931 0.480296 0.683065 0.00211792
v_subj(CP).21076 0.733281 0.254666 0.22744 0.565645 0.733555 0.902759 1.23772 0.00352938
v_subj(CS).18438 0.515089 0.206169 0.112467 0.377064 0.513074 0.654525 0.928566 0.00276589
v_subj(CS).18695 0.736665 0.232563 0.286527 0.58098 0.736825 0.893371 1.19635 0.00309388
v_subj(CS).18942 -0.623133 0.24112 -1.09508 -0.788091 -0.620748 -0.457674 -0.150413 0.00341801
v_subj(CS).19109 -0.25503 0.65857 -1.55439 -0.691218 -0.253969 0.191785 1.03018 0.00727414
v_subj(CS).19719 0.964319 0.203288 0.563947 0.828609 0.96393 1.1031 1.35579 0.00354901
v_subj(CS).20069 -1.27254 0.28269 -1.83632 -1.45925 -1.26746 -1.07811 -0.728315 0.00877904
v_subj(CS).20372 1.22353 0.192408 0.854367 1.09294 1.22415 1.35208 1.59857 0.00297411
v_subj(CS).20541 -0.122623 0.192674 -0.49715 -0.253157 -0.123757 0.00754577 0.260239 0.00177618
v_subj(CS).20709 0.413201 0.151284 0.119621 0.31121 0.412079 0.516517 0.712328 0.00228519
v_subj(CS).21076 1.12329 0.22131 0.686626 0.977753 1.12253 1.27195 1.55003 0.00349679
v_subj(SS).18438 1.43628 0.335963 0.785355 1.20686 1.43344 1.65949 2.10958 0.00641438
v_subj(SS).18695 1.01308 0.264847 0.507216 0.831874 1.01409 1.18851 1.54266 0.00463494
v_subj(SS).18942 -0.553823 0.299636 -1.14715 -0.753697 -0.552586 -0.353163 0.0320158 0.00442466
v_subj(SS).19109 0.895721 0.603648 -0.205005 0.478459 0.864281 1.27681 2.16235 0.00938268
v_subj(SS).19719 1.02425 0.190878 0.645698 0.895275 1.0232 1.15382 1.39225 0.00335278
v_subj(SS).20069 1.94498 0.334413 1.31323 1.71444 1.9329 2.16655 2.62672 0.0109291
v_subj(SS).20372 1.22523 0.215126 0.80647 1.07936 1.22353 1.37067 1.6442 0.00343983
v_subj(SS).20541 1.25274 0.22918 0.824115 1.095 1.24372 1.40502 1.72806 0.00482715
v_subj(SS).20709 1.06175 0.200431 0.670981 0.92718 1.06408 1.19606 1.44779 0.0030887
v_subj(SS).21076 1.13055 0.270202 0.604819 0.948998 1.12873 1.31516 1.66264 0.00447288
v_subj(US).18438 0.278406 0.201969 -0.113827 0.143055 0.275657 0.413333 0.684345 0.00296581
v_subj(US).18695 0.739244 0.188896 0.377719 0.613264 0.737135 0.866108 1.12034 0.00248048
v_subj(US).18942 -0.700056 0.224114 -1.14838 -0.848122 -0.696779 -0.551584 -0.264286 0.00354377
v_subj(US).19109 1.09313 0.724133 -0.329827 0.610141 1.09257 1.56825 2.51843 0.00937875
v_subj(US).19719 1.9095 0.238523 1.44529 1.74873 1.90787 2.07161 2.3709 0.00449416
v_subj(US).20069 -1.34447 0.305336 -1.94427 -1.54531 -1.33596 -1.13571 -0.765038 0.00914626
v_subj(US).20372 1.74663 0.225604 1.31258 1.59381 1.74461 1.89724 2.19168 0.00406782
v_subj(US).20541 -0.204235 0.562925 -1.34826 -0.58051 -0.203661 0.177822 0.903839 0.00636837
v_subj(US).20709 1.31976 0.199622 0.935544 1.18536 1.31878 1.45647 1.7109 0.00374298
v_subj(US).21076 1.56863 0.234569 1.10595 1.41135 1.57103 1.72583 2.0288 0.00371908
t 0.490764 0.149551 0.246591 0.384669 0.472176 0.580037 0.824895 0.00430459
t_std 0.93655 0.316264 0.464237 0.705968 0.886797 1.11222 1.67344 0.0108057
t_subj.18438 0.917897 0.0221592 0.865969 0.904755 0.921049 0.934108 0.95228 0.00052273
t_subj.18695 0.728382 0.0368724 0.644941 0.706237 0.733006 0.755029 0.786613 0.000624117
t_subj.18942 0.797387 0.0154477 0.760303 0.78921 0.799904 0.80857 0.819646 0.000308023
t_subj.19109 1.76592 0.414686 0.412931 1.65366 1.89517 2.03802 2.16285 0.011479
t_subj.19719 0.00357981 0.00593045 5.86591e-10 4.85758e-05 0.000834141 0.00451453 0.0218465 0.000171078
t_subj.20069 0.514619 0.0895435 0.30155 0.469946 0.528065 0.576118 0.643187 0.00490587
t_subj.20372 0.0153781 0.0182105 9.50826e-11 0.000497332 0.00735082 0.0259886 0.0616383 0.000639708
t_subj.20541 8.06095e-05 0.000133922 2.0513e-13 1.29163e-06 1.96371e-05 0.000100173 0.000478036 4.06826e-06
t_subj.20709 0.364182 0.0490333 0.25357 0.335335 0.369507 0.398992 0.444268 0.00081774
t_subj.21076 0.891713 0.0212567 0.84217 0.879109 0.894651 0.907443 0.9243 0.000371863
z(CP) 0.413545 0.0374767 0.341629 0.388131 0.41335 0.438941 0.487632 0.000699426
z(CS) 0.341062 0.0326994 0.278777 0.318737 0.340201 0.362895 0.408049 0.000636906
z(SS) 0.450163 0.0373826 0.377535 0.424633 0.450023 0.475271 0.525005 0.000720763
z(US) 0.370584 0.0372689 0.301427 0.344817 0.368758 0.395343 0.445843 0.000854073
z_std 0.357337 0.0283129 0.304712 0.337847 0.356706 0.37619 0.414082 0.000477449
z_subj(CP).18438 0.388649 0.0510766 0.292241 0.353444 0.387737 0.423048 0.492822 0.000766289
z_subj(CP).18695 0.330662 0.0494887 0.239193 0.295831 0.328822 0.363225 0.431731 0.000627433
z_subj(CP).18942 0.514666 0.0545005 0.407309 0.477422 0.515434 0.552439 0.618406 0.000887813
z_subj(CP).19719 0.339821 0.0582787 0.231577 0.298935 0.33743 0.377984 0.459281 0.000930733
z_subj(CP).20069 0.418861 0.0619296 0.302173 0.376551 0.416511 0.458604 0.548113 0.00153608
z_subj(CP).20372 0.456003 0.0634343 0.331942 0.41325 0.455152 0.499591 0.581785 0.00100719
z_subj(CP).20541 0.496807 0.076226 0.34712 0.444399 0.497006 0.549272 0.644668 0.0011122
z_subj(CP).20709 0.418886 0.0576722 0.310306 0.378517 0.417742 0.457687 0.533985 0.000793058
z_subj(CP).21076 0.332876 0.0496103 0.239987 0.299295 0.331076 0.364589 0.436534 0.000718614
z_subj(CS).18438 0.379625 0.0525763 0.282039 0.343007 0.378377 0.414697 0.485679 0.000738947
z_subj(CS).18695 0.318535 0.0542329 0.222611 0.280484 0.316325 0.35442 0.432663 0.000784172
z_subj(CS).18942 0.495257 0.0570579 0.384198 0.455428 0.495207 0.534879 0.6058 0.000792508
z_subj(CS).19109 0.354528 0.0832716 0.20738 0.295074 0.348762 0.407721 0.531628 0.000983487
z_subj(CS).19719 0.50137 0.0594186 0.385433 0.46009 0.501544 0.543155 0.615432 0.00123296
z_subj(CS).20069 0.41971 0.0597864 0.308482 0.37828 0.417901 0.458643 0.541322 0.00138439
z_subj(CS).20372 0.259567 0.0427619 0.182211 0.229362 0.257758 0.28721 0.347783 0.000675812
z_subj(CS).20541 0.0767861 0.0128827 0.0533593 0.0677349 0.0761696 0.085004 0.104344 0.000245877
z_subj(CS).20709 0.380821 0.0566422 0.277657 0.341364 0.37877 0.417846 0.495396 0.000850829
z_subj(CS).21076 0.338195 0.0527828 0.239969 0.3012 0.33642 0.373645 0.445972 0.000806415
z_subj(SS).18438 0.614423 0.0592853 0.489962 0.576239 0.617663 0.65659 0.71965 0.00143561
z_subj(SS).18695 0.594942 0.058566 0.47269 0.557141 0.597484 0.634926 0.70484 0.00113046
z_subj(SS).18942 0.251955 0.0381631 0.182549 0.22552 0.249964 0.27569 0.334357 0.000767641
z_subj(SS).19109 0.395775 0.0856889 0.237458 0.336189 0.392847 0.452174 0.572355 0.00144701
z_subj(SS).19719 0.372929 0.0570859 0.267119 0.332514 0.371344 0.411511 0.488393 0.000969869
z_subj(SS).20069 0.517779 0.0678675 0.379663 0.472949 0.520473 0.564451 0.646644 0.00176412
z_subj(SS).20372 0.427075 0.0591298 0.31581 0.385598 0.425674 0.466884 0.547226 0.00102551
z_subj(SS).20541 0.330617 0.0567666 0.224776 0.29093 0.328206 0.369272 0.446899 0.000961219
z_subj(SS).20709 0.39446 0.0599122 0.280894 0.35313 0.393125 0.434114 0.515441 0.000978053
z_subj(SS).21076 0.610455 0.0544299 0.497766 0.574097 0.612799 0.648318 0.711081 0.000991126
z_subj(US).18438 0.39867 0.0542776 0.295585 0.360879 0.398092 0.434259 0.508844 0.000835703
z_subj(US).18695 0.34948 0.0551292 0.248075 0.310668 0.347974 0.385946 0.461434 0.000759569
z_subj(US).18942 0.514446 0.0592696 0.400243 0.474549 0.513903 0.554552 0.631702 0.00100168
z_subj(US).19109 0.374593 0.0874883 0.216111 0.312206 0.371309 0.432114 0.553723 0.00132043
z_subj(US).19719 0.270974 0.0503493 0.181247 0.234865 0.268083 0.303941 0.377093 0.000870165
z_subj(US).20069 0.416662 0.0613329 0.303372 0.374317 0.414394 0.45723 0.542325 0.00142453
z_subj(US).20372 0.288746 0.0517395 0.194351 0.252609 0.285916 0.322476 0.398287 0.000905872
z_subj(US).20541 0.381091 0.0862904 0.223433 0.31886 0.377422 0.438932 0.560248 0.00129931
z_subj(US).20709 0.372747 0.0630036 0.254358 0.32834 0.370575 0.414895 0.50217 0.00126415
z_subj(US).21076 0.313366 0.0518628 0.219204 0.276742 0.311396 0.347377 0.421222 0.000827263
DIC: 2483.416100
deviance: 2411.206602
pD: 72.209498
In [57]:
v_bSS, v_bCP, v_bCS, v_bUS = model_bias.nodes_db.node[['v(SS)', 'v(CP)', 'v(CS)', 'v(US)']]
hddm.analyze.plot_posterior_nodes([v_bSS, v_bCP, v_bCS, v_bUS])
In [59]:
print('P(SS > US) = ' + str((v_bSS.trace() > v_bUS.trace()).mean()))
print('P(CP > SS) = ' + str((v_bCP.trace() > v_bSS.trace()).mean()))
print('P(CS > SS) = ' + str((v_bCS.trace() > v_bSS.trace()).mean()))
print('P(CP > CS) = ' + str((v_bCP.trace() > v_bCS.trace()).mean()))
P(SS > US) = 0.802755102041
P(CP > SS) = 0.0326530612245
P(CS > SS) = 0.0437755102041
P(CP > CS) = 0.424081632653
In [58]:
z_bSS, z_bCP, z_bCS, z_bUS = model_bias.nodes_db.node[['z(SS)', 'z(CP)', 'z(CS)', 'z(US)']]
hddm.analyze.plot_posterior_nodes([z_bSS, z_bCP, z_bCS, z_bUS])
In [63]:
print("Bias Probabilities")
print('P(zUS > zSS) = ' + str((z_bSS.trace() < z_bUS.trace()).mean()))
print('P(zCP > zSS) = ' + str((z_bCP.trace() > z_bSS.trace()).mean()))
print('P(zCS > zSS) = ' + str((z_bCS.trace() > z_bSS.trace()).mean()))
print('P(zCS > zCP) = ' + str((z_bCP.trace() < z_bCS.trace()).mean()))
Bias Probabilities
P(zUS > zSS) = 0.0691836734694
P(zCP > zSS) = 0.246632653061
P(zCS > zSS) = 0.015
P(zCS > zCP) = 0.0731632653061
Content source: CPernet/LanguageDecision
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