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Normativity results

William N. Havard 2 rokov pred
rodič
commit
0c48234dd5
2 zmenil súbory, kde vykonal 99 pridanie a 100 odobranie
  1. 90 90
      OUTPUT/df.icc.normativity.csv
  2. 9 10
      all-analyses.Rmd

+ 90 - 90
OUTPUT/df.icc.normativity.csv

@@ -1,91 +1,91 @@
 "data_set","normative","metric","iqr","age_b","age_se","age_t","icc_adjusted","icc_conditional","child_id_var","residual_var","child_id_sd","residual_sd","nobs","formula","is.sing","sw"
-"aclew","Y","voc_fem_ph",1.48713148488522,0.303313884413507,0.103969338200573,2.9173397625016,0.500926626165868,0.435325463472754,0.49397970584461,0.492152154674702,0.702836898465505,0.701535569073088,252,"full",FALSE,0.00218480392548501
-"aclew","Y","voc_dur_fem_ph",1.37392805828064,0.117549120487211,0.112839183670341,1.04174025957712,0.434410738553378,0.426437774097688,0.513189071094473,0.668156198599902,0.716372159631063,0.817408220291368,247,"full",FALSE,0.105037846550271
-"aclew","Y","avg_voc_dur_fem",1.52041169472307,-0.217760272280775,0.112017415536476,-1.94398586360767,0.426425785294572,0.401197891610174,0.496611263511815,0.667978873008092,0.704706508776395,0.817299744896627,247,"full",FALSE,0.000487018669903755
-"aclew","Y","voc_mal_ph",1.5709249738724,0.0267437495978994,0.119363168071849,0.224053617459294,0.564273231789867,0.563772355252216,0.733632924276964,0.566504815643626,0.856523744140794,0.752665141775296,253,"full",FALSE,0.121843515933417
-"aclew","Y","voc_dur_mal_ph",1.46725739530606,0.0220989898276948,0.12398321658977,0.178241784941061,0.577767963710562,0.577444261875426,0.807561145000337,0.590164578340203,0.898644059124822,0.768221698691337,248,"full",FALSE,0.003214672163622
-"aclew","Y","avg_voc_dur_mal",1.30110411585655,-0.013331868473376,0.104765676623122,-0.127254162843192,0.403862279774911,0.403751385810103,0.419255250159472,0.618859154565673,0.647499227921912,0.786676016264429,246,"full",FALSE,0.0593534088788712
-"aclew","Y","voc_chi_ph",1.59068290376959,0.443473647020069,0.0822632352830027,5.39090938369281,0.5259129480459,0.346875065375768,0.323125489203541,0.291283207909082,0.568441280347883,0.539706594279783,252,"full",FALSE,0.00312108318612166
-"aclew","Y","voc_dur_chi_ph",1.60507689482075,0.416349454039927,0.078297286449005,5.31754640451167,0.533515785761144,0.354740436342598,0.296931419477547,0.259624595175192,0.544914139546357,0.509533703669533,251,"full",FALSE,0.00419582282382142
-"aclew","Y","avg_voc_dur_chi",1.1741080730997,0.148600198649044,0.100968323693827,1.47175067598086,0.490905544121351,0.472735302008502,0.450981942918482,0.467691615200875,0.671551891456261,0.683879825116134,248,"full",FALSE,1.04759644569223e-05
-"aclew","Y","voc_och_ph",1.77172537352388,0.486311463964703,0.0916458113421788,5.30642324883739,0.603429954240895,0.402323522119238,0.463136657547781,0.304370249082995,0.68054144440128,0.55169760655906,251,"full",FALSE,1.77705436574084e-06
-"aclew","Y","voc_dur_och_ph",1.65459918134701,0.438549874300856,0.0946552760569988,4.63312656799789,0.563446001588472,0.405534758777759,0.454448891494075,0.352103804261845,0.674128245583935,0.593383353542922,247,"full",FALSE,4.06365186272243e-08
-"aclew","Y","avg_voc_dur_och",1.58505121491201,0.4707251166461,0.093311935400641,5.04463994477245,0.439420742665562,0.304210732643964,0.354696359939776,0.452494392568083,0.595563900803076,0.672677034369454,250,"full",FALSE,0.000752543560018937
-"aclew","Y","cry_voc_chi_ph",1.60307648099453,0.225763651896705,0.090010699677853,2.50818683450645,0.489798248927474,0.440384253592122,0.360474042333823,0.375490292212417,0.600394905319676,0.612772626846547,251,"full",FALSE,2.80281408509778e-05
-"aclew","Y","cry_voc_dur_chi_ph",1.49883927163067,0.244964174857594,0.0889509505893706,2.75392419344046,0.472667200831015,0.417097921124856,0.343753423071191,0.383509696660461,0.586304889175582,0.619281597224123,254,"full",FALSE,1.69056103148052e-06
-"aclew","Y","avg_cry_voc_dur_chi",0.799197299248983,-0.300691869179232,0.0964673202296811,-3.11703350381567,0.150556841905463,0.135656805528552,0.196709176969484,1.10983507887378,0.443519082982327,1.05348710427503,248,"full",FALSE,6.64599923595082e-15
-"aclew","Y","can_voc_chi_ph",1.80279885507926,0.483150463904317,0.0848392026568848,5.69489633063058,0.648899722830384,0.416894015936343,0.442843698670337,0.23960951110872,0.665465024377943,0.48949924525858,248,"full",FALSE,0.0375294323388966
-"aclew","Y","can_voc_dur_chi_ph",1.74763314049438,0.439580298012393,0.0873208373209789,5.03408248819877,0.627179072152723,0.435332479017639,0.446347983277046,0.265327522324578,0.668092795408726,0.515099526620417,248,"full",FALSE,2.51142527655703e-06
-"aclew","Y","avg_can_voc_dur_chi",1.08889896453643,-0.066471651014216,0.0980617786666771,-0.677854837205844,0.156546639482443,0.155729633855137,0.207126805460593,1.11597285445782,0.45511186038225,1.05639616359481,247,"full",FALSE,2.83621350382712e-16
-"aclew","Y","non_can_voc_chi_ph",1.37758134504665,0.335321689775139,0.0905823649698246,3.70184295681442,0.52670707072777,0.423576367482466,0.392197813771608,0.35242445459797,0.626256987004224,0.593653480237394,250,"full",FALSE,0.00151936749018711
-"aclew","Y","non_can_voc_dur_chi_ph",1.26979511718498,0.33557981906755,0.0798023346389435,4.20513786452291,0.381980619246853,0.295265574044104,0.234128588788735,0.378804573240176,0.483868358945628,0.61547101738439,252,"full",FALSE,0.00439603185990217
-"aclew","Y","avg_non_can_voc_dur_chi",1.06381778401439,0.00670191592976359,0.10120126727683,0.0662236364237506,0.213158641449594,0.213146209456265,0.261899309824991,0.966759814871685,0.511760988963589,0.983239449407765,247,"full",FALSE,8.19279377742856e-17
-"aclew","Y","lp_n",1.35553497050697,-0.0843758538494529,0.0879497942153817,-0.959363857552906,0.402091269119011,0.39591811610686,0.296443883409316,0.440811327475589,0.544466604494083,0.66393623750748,254,"full",FALSE,0.00140310229624115
-"aclew","Y","cp_n",1.73419191928651,0.372380933654551,0.0948546671374886,3.9258050751978,0.752395156412266,0.612772954899648,0.738676476655968,0.24309017928356,0.85946290010446,0.493041762210423,253,"full",FALSE,4.40060426507414e-05
-"aclew","Y","lp_dur",1.24743926487605,0.0319355261356264,0.0934678712256429,0.341673836333879,0.302630220993878,0.302088399737929,0.277857173037946,0.640283692489052,0.527121592270651,0.800177288161225,254,"full",FALSE,1.41193360204143e-11
-"aclew","Y","cp_dur",1.6922943490313,0.348823554844667,0.0989099325833303,3.5266787241088,0.654479657752795,0.54074904389389,0.611377277506147,0.322765243600402,0.781906182036021,0.568124320550003,254,"full",FALSE,3.0207793054727e-07
-"aclew","N","voc_fem_ph",1.30503468420213,0.0361341552980083,0.0772012437024235,0.46805146607857,0.489630258718789,0.489199537050871,0.46529179899897,0.485000366800785,0.682123008700755,0.696419677206772,502,"full",FALSE,5.12659692075836e-06
-"aclew","N","voc_dur_fem_ph",1.34868161594711,-0.048264156460587,0.0795480397890553,-0.60672967666549,0.496689214045035,0.495968431263203,0.500783290758054,0.507459442519111,0.707660434642247,0.712361876098876,499,"full",FALSE,0.000266509305051753
-"aclew","N","avg_voc_dur_fem",1.2648895569552,-0.210201665145521,0.0729631693867933,-2.88092837677044,0.36474509841619,0.352628236524617,0.28837737791287,0.502249772842747,0.537007800607096,0.708695825331818,498,"full",FALSE,3.81489649609647e-06
-"aclew","N","voc_mal_ph",1.36881038938832,-0.0272435900375334,0.0788741588665235,-0.345405775846522,0.499351377042195,0.499111866662147,0.495142101814448,0.496428412614333,0.703663344088953,0.70457676133572,499,"full",FALSE,3.37869357051573e-05
-"aclew","N","voc_dur_mal_ph",1.38990463528451,-0.0584762968383939,0.0784795020782215,-0.745115543420623,0.521803909670712,0.520633802685759,0.508893596241439,0.46636470828637,0.713367784695551,0.682909004396903,492,"full",FALSE,7.19189325227911e-08
-"aclew","N","avg_voc_dur_mal",1.33596009415767,-0.0595514379865781,0.0811290090344923,-0.734033839378706,0.512117139829597,0.511000267393149,0.528957767389016,0.503926559749543,0.727294828380497,0.709877848470807,496,"full",FALSE,0.000370154272234511
-"aclew","N","voc_chi_ph",1.28676927314273,0.135223401964508,0.0674761779543746,2.00401691476868,0.582253238347232,0.573611372972945,0.456534117854861,0.3275475974324,0.675673085045469,0.572317741671879,502,"full",FALSE,0.0101558682167333
-"aclew","N","voc_dur_chi_ph",1.37668287487854,0.0517404361530582,0.071553136764853,0.72310507257137,0.602819874726948,0.601675597325433,0.54326421746279,0.357940670164489,0.737064595176562,0.598281430569669,499,"full",FALSE,0.0758330970391532
-"aclew","N","avg_voc_dur_chi",1.25715261728522,-0.0886711394780247,0.0753971109512395,-1.17605486946801,0.560349378985502,0.557376661858462,0.534721467262543,0.41954293868834,0.731246516068653,0.647721343394164,498,"full",FALSE,0.000146904688585011
-"aclew","N","voc_och_ph",1.13703394525073,0.179987408654489,0.0639682817345777,2.81369772290128,0.62034281908017,0.603138447688875,0.456850218729212,0.279597765639528,0.675906960113011,0.528770049870005,494,"full",FALSE,1.92163385514671e-05
-"aclew","N","voc_dur_och_ph",1.15834985427806,0.179623107903573,0.0686080444327553,2.61810563744644,0.634223870326275,0.619151639227808,0.543869620652981,0.313666095202663,0.737475166126278,0.560059010464668,490,"full",FALSE,1.28479186784172e-11
-"aclew","N","avg_voc_dur_och",1.29627536474679,0.124663110942211,0.069647944485848,1.78990366280718,0.656830065171452,0.649810788275061,0.608033338664787,0.317675411445597,0.779764925259393,0.563627014474641,499,"full",FALSE,5.71175594812184e-06
-"aclew","N","cry_voc_chi_ph",1.2555558798254,-0.201956672642943,0.0757356676922818,-2.66659922328149,0.496433473291527,0.482348175569133,0.452988799319959,0.459497613645883,0.67304442596307,0.677862533000522,492,"full",FALSE,4.94900114978183e-06
-"aclew","N","cry_voc_dur_chi_ph",1.28904805912153,-0.253240532054945,0.0775970008836544,-3.26353504866306,0.452585960619209,0.433160434260857,0.422471801390169,0.510990210582458,0.649978308399726,0.714835792740163,486,"full",FALSE,6.11338775933548e-07
-"aclew","N","avg_cry_voc_dur_chi",0.652327214141891,-0.069618353041213,0.0621273449525305,-1.12057505586962,0.450682486057202,0.448370437372613,0.273118235267906,0.332892079570997,0.522607151948675,0.576968005673623,503,"full",FALSE,4.60260377200801e-23
-"aclew","N","can_voc_chi_ph",1.385728792088,0.352222589946729,0.0757194034462258,4.65168205131027,0.541987157542536,0.499602149363222,0.508009688209591,0.429299768555025,0.712747983658734,0.655209713416266,493,"full",FALSE,2.23197623925212e-06
-"aclew","N","can_voc_dur_chi_ph",1.3445569473013,0.301371422666478,0.0752821284899097,4.0032266450446,0.578119842374261,0.545015130371577,0.556707056753417,0.406254280928915,0.746128043135638,0.637380797427185,493,"full",FALSE,1.74237617734741e-06
-"aclew","N","avg_can_voc_dur_chi",0.89597829217643,0.067869809908324,0.0695519672559157,0.975814381476771,0.429834688439732,0.428149235738347,0.32350844373119,0.429126121211673,0.568778026765442,0.655077187216646,502,"full",FALSE,2.57111893445047e-16
-"aclew","N","non_can_voc_chi_ph",1.28037761476488,0.0368406539604657,0.0715035133212626,0.51522858457237,0.546464793742953,0.545902567296946,0.464792070406608,0.385751415154584,0.681756606426816,0.621088894728109,500,"full",FALSE,0.00139297372926305
-"aclew","N","non_can_voc_dur_chi_ph",1.36330828001172,-0.0125800793586458,0.074889820908532,-0.167981164943773,0.552198799454997,0.552138158848852,0.515900835513616,0.41836565695756,0.718262372335915,0.646811917760921,496,"full",FALSE,0.00477018287633292
-"aclew","N","avg_non_can_voc_dur_chi",0.909603336397192,0.0404713648879007,0.0737777858477031,0.54855759661105,0.453069422924252,0.452509481276563,0.38648047937889,0.466546584077353,0.621675541885709,0.683042153953438,501,"full",FALSE,3.4618260631421e-17
-"aclew","N","lp_n",1.25713197771756,0.342368576549406,0.0793134288584822,4.31665332689487,0.470399413389622,0.43721177483095,0.468341448415426,0.527283620588453,0.684354767949655,0.726142975307517,503,"full",FALSE,6.11307112666474e-08
-"aclew","N","cp_n",1.38675353337353,0.357932863719906,0.0737042075696273,4.85634233814632,0.560782755729032,0.514456362735113,0.514469381334837,0.402943602711031,0.717265209901356,0.634778388660981,501,"full",FALSE,3.42463234750206e-08
-"aclew","N","lp_dur",1.36957300347191,0.430160804291549,0.0770635219996996,5.58189910257703,0.404501175420044,0.358183273020652,0.373190351646072,0.54940363404146,0.610893077097844,0.741217669811952,503,"full",FALSE,6.37739076963555e-05
-"aclew","N","cp_dur",1.36440405022002,0.350351350184619,0.0735847876442602,4.76119265137197,0.563534195419996,0.518841276074322,0.516779998346163,0.400253967908556,0.718874118567474,0.632656279435015,502,"full",FALSE,1.32076671300683e-11
-"lena","Y","voc_fem_ph",1.23926171895615,0.0428654216317557,0.106694377684064,0.401758954522288,0.415600317873389,0.414442105307507,0.44436426435014,0.624846333524127,0.666606528883524,0.790472221854839,251,"full",FALSE,0.0551482831630693
-"lena","Y","voc_dur_fem_ph",1.3390098270162,-0.0404768300270164,0.106755034123604,-0.37915617150336,0.382230900251207,0.381304881126528,0.415727395132617,0.671906793676811,0.644769257279391,0.819699209269358,246,"full",FALSE,0.00830883388835006
-"lena","Y","avg_voc_dur_fem",1.37703635971145,-0.214326418172131,0.103874063835331,-2.06332948051303,0.345570315841863,0.324436319057655,0.36641542147009,0.693905458746273,0.605322576375679,0.833009879140862,241,"full",FALSE,2.84046093882812e-05
-"lena","Y","wc_fem_ph",1.27710234509043,-0.0299972426468165,0.104989930679536,-0.285715424828483,0.370472412804179,0.369965368177234,0.394386862384586,0.670164366678315,0.628002278964484,0.818635673959005,247,"full",FALSE,0.0240941621738891
-"lena","Y","voc_mal_ph",1.31570056260613,0.10637377740615,0.113743053091122,0.935211202049692,0.529932757274355,0.521596661243803,0.615526953689748,0.545992022520811,0.784555258531703,0.738912729976153,247,"full",FALSE,2.07300872630073e-08
-"lena","Y","voc_dur_mal_ph",1.30353750697435,0.116390145251842,0.113650458366658,1.02410625460344,0.524690587491213,0.514832772752818,0.60807995023142,0.550850598034995,0.779794812903638,0.742193100234026,245,"full",FALSE,1.45976915977732e-08
-"lena","Y","avg_voc_dur_mal",1.2500856115958,-0.00495808683237913,0.112682208056872,-0.044000618357396,0.351506011678438,0.351494873311914,0.436876418336017,0.80599398450556,0.660966276247145,0.897771677268536,249,"full",FALSE,1.18187376897557e-07
-"lena","Y","wc_mal_ph",1.28330572677433,0.075600619372294,0.116839339611298,0.647047643574524,0.573680378710142,0.569385336995285,0.706801524308632,0.525246059221765,0.840714888834872,0.724738614413338,247,"full",FALSE,1.89310308048363e-08
-"lena","Y","voc_chi_ph",1.71799989427854,0.390157791407905,0.0860112042427494,4.53612752946422,0.548042855626718,0.400964370937788,0.36755439453549,0.303112854862681,0.606262644845854,0.550556858882605,251,"full",FALSE,6.25797113667041e-06
-"lena","Y","voc_dur_chi_ph",1.68150674911455,0.377246031010054,0.0880923245748744,4.28239387291242,0.565056364338745,0.426224368216396,0.398341506194662,0.30661738168694,0.63114301564278,0.553730423299046,250,"full",FALSE,0.00157968176224617
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+ 9 - 10
all-analyses.Rmd

@@ -424,16 +424,14 @@ for (data_set in data_sets){ #data_set="aclew"
     metrics = all_cols[!is.element(all_cols, no.scale.columns.norm)]
 
     #remove outliers
-    for(metric in metrics) corpus_data[, metric] <- ifelse(scale(corpus_data[, metric])>2.5,NA,corpus_data[, metric])
-    colnames(corpus_data)<-gsub(".V1","", colnames(corpus_data),fixed=T)
+    # for(metric in metrics) corpus_data[, metric] <- ifelse(scale(corpus_data[, metric])>2.5,NA,corpus_data[, metric])
+    # colnames(corpus_data)<-gsub(".V1","", colnames(corpus_data),fixed=T)
 
     # Scale thiscordata
     # print(corpus_data)
     corpus_data[, metrics] <- scale(corpus_data[, metrics])
     metrics = metrics[!(metrics=="age")] #remove age, since a regression with age twice doesn't make sense
-    scaled_data <- corpus_data
-    scaled_data[, metrics] <- scale(corpus_data[, metrics])
-    print(scaled_data)
+
     #print(summary(thiscordata)) #to look at distribution of thiscordata
     # Fit LMER
     for (normativity in normativity_items)
@@ -442,7 +440,8 @@ for (data_set in data_sets){ #data_set="aclew"
 
       # Not sure why we get NAs for LENA data
       # TODO: check why we have NAs
-      for (metric in metrics) if(sum(!is.na(thiscordata[,metric]>30))){#metric="lp_n"
+      for (metric in metrics)if(sum(!is.na(thiscordata[,metric]>0))){#metric="lp_n"
+        print(metric)
         formula <- as.formula(paste0(metric, "~ age + (1|child_id)"))
 
         model <- lmer(formula, data=thiscordata)
@@ -492,10 +491,10 @@ write.csv(df.icc.normativity,"OUTPUT/df.icc.normativity.csv",row.names=F)
 df.icc.mixed<-read.csv("OUTPUT/df.icc.mixed.csv")
 
 icc_exp <- ggplot(df.icc.mixed, aes(x = icc_corpus, fill = data_set)) + geom_density(alpha = 0.5) + theme(legend.position = "top", axis.title.y=element_blank() ) +labs( x = "ICC corpus")+
-  geom_jitter( aes(x = icc_corpus,y=0,colour=data_set))+ scale_fill_colorblind() +scale_colour_manual(values=cbPalette)
+  geom_jitter( aes(x = icc_corpus,y=0,colour=data_set))+ scale_fill_colorblind() +scale_colour_manual(values=cbPalette) + xlim(0,1)
 
 icc_chi <- ggplot(df.icc.mixed, aes(x = icc_child_id_corpus, fill = data_set)) + geom_density(alpha = 0.5) + theme(legend.position = "top", axis.title.y=element_blank() ) +labs( x = "ICC child ID")+
-  geom_jitter(aes(x = icc_child_id_corpus,y=0,colour=data_set))+ scale_fill_colorblind()+scale_colour_manual(values=cbPalette)
+  geom_jitter(aes(x = icc_child_id_corpus,y=0,colour=data_set))+ scale_fill_colorblind()+scale_colour_manual(values=cbPalette) + xlim(0,1)
 
 
 ggarrange(icc_exp, icc_chi,
@@ -585,10 +584,10 @@ ggarrange(bad, good,
 df.icc.normativity<-read.csv("OUTPUT/df.icc.normativity.csv")
 
 icc_normative <- ggplot(df.icc.normativity[df.icc.normativity$data_set == 'aclew',], aes(x = icc_adjusted, fill = normative)) + geom_density(alpha = 0.5) + theme(legend.position = "top", axis.title.y=element_blank() ) +labs( x = "ICC ACLEW")+
-  geom_jitter( aes(x = icc_adjusted,y=0,colour=normative))+ scale_fill_colorblind() +scale_colour_manual(values=cbPalette)
+  geom_jitter( aes(x = icc_adjusted,y=0,colour=normative))+ scale_fill_colorblind() +scale_colour_manual(values=cbPalette) + xlim(0,1)
 
 icc_non_normative <- ggplot(df.icc.normativity[df.icc.normativity$data_set == 'lena',], aes(x = icc_adjusted, fill = normative)) + geom_density(alpha = 0.5) + theme(legend.position = "top", axis.title.y=element_blank() ) +labs( x = "ICC LENA")+
-  geom_jitter(aes(x = icc_adjusted,y=0,colour=normative))+ scale_fill_colorblind()+scale_colour_manual(values=cbPalette)
+  geom_jitter(aes(x = icc_adjusted,y=0,colour=normative))+ scale_fill_colorblind()+scale_colour_manual(values=cbPalette) + xlim(0,1)
 
 
 ggarrange(icc_normative, icc_non_normative,