by age ana in progress

CODE/SM.Rmd

@@ -537,23 +537,31 @@ ggplot(df.icc.corpus, aes(y = icc_child_id, x = toupper(data_set))) +
 ```{r relBYage, echo=F,fig.width=6, fig.height=10,fig.cap="Distribution of ICC attributed to corpus (a) and children (b), when binning children's age."}
 df.icc.age<-read.csv("../output/df.icc.age.csv")
 
-df.icc.age$age_bin<-factor(df.icc.age$age_bin,levels=c("(0,6]" , "(6,12]",  "(12,18]" ,"(18,24]" ,"(24,30]", "(30,36]", "(36,42]", "(42,48]", "(48,54]" ))
+df.icc.age$age_bin<-factor(df.icc.age$age_bin,levels=c("(0,6]" , "(6,12]",  "(12,18]" ,"(18,24]" ,"(24,30]", "(30,36]" ))
+#not present in data: , "(36,42]", "(42,48]", "(48,54]"
 
-icc_exp <- ggplot(df.icc.age, aes(x = icc_corpus, fill = toupper(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=toupper(data_set)))+ 
-  facet_grid(rows=vars(age_bin)) +
-  scale_fill_colorblind() +scale_colour_manual(values=cbPalette)+ ylim(-0.5,16) + xlim(0, 1) + labs(fill='Pipeline', color="Pipeline")  +  theme(text = element_text(size = 15)) 
 
-icc_chi <- ggplot(df.icc.age, aes(x = icc_child_id, fill = toupper(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,y=0,colour=toupper(data_set)))+
-  facet_grid(rows=vars(age_bin)) +
-  scale_fill_colorblind()+scale_colour_manual(values=cbPalette)+ ylim(-0.5,15) + xlim(0, 1) + labs(fill='Pipeline', color="Pipeline")  +  theme(text = element_text(size = 15)) 
 
+df.icc.age$Type<-get_type(df.icc.age)
 
-ggarrange(icc_exp, icc_chi,
-          labels = c("A", "B"),
-          ncol = 2, nrow = 1, common.legend = TRUE, vjust = 1.05, hjust=0.45,
-          font.label = list(size = 15)) 
+#this complicated section is just to add N of participants in each facet, we first estimate it:
+facet_labels=NULL
+for(thisage in levels(df.icc.age$age_bin)){#thisage="(0,6]"
+  if(min(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T) !=max(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T)){
+    facet_labels = c(facet_labels,paste0("N chi=",paste(range(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T),collapse="-"))) 
+  } else facet_labels = c(facet_labels,paste0("N chi=",min(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T))) 
+}
+
+#and then we structure it so that it goes ont he plot
+f_labels<-data.frame(age_bin=levels(df.icc.age$age_bin),label=facet_labels)
+
+f_labels$age_bin<-factor(f_labels$age_bin,levels=c("(0,6]" , "(6,12]",  "(12,18]" ,"(18,24]" ,"(24,30]", "(30,36]" ))
+
+ggplot(df.icc.age, aes(y = icc_child_id, x = toupper(data_set))) +
+  geom_violin(alpha = 0.5) +
+  geom_quasirandom(aes(colour = Type,shape = Type)) +  
+  theme(legend.position="none") +labs( y = "r",x="Pipeline") + facet_wrap(~age_bin, ncol = 3) +
+  geom_text(x=1.5,y=max(df.icc.age$icc_child_id,na.rm=T),aes(label=label),data=f_labels,size=2)
 
 
 

CODE/create-all-icc.R

@@ -66,15 +66,14 @@ write.csv(df.icc.corpus,"../output/df.icc.corpus.csv",row.names=F)
 #We do this one separately because we want to standardize metrics within each age group
 
 # repeat within age group bins
-df.icc.age.cols = c("data_set","age_bin","metric", "iqr",
-                    "age_b","age_se","age_t", # beta, standard error, T
-                    "icc_adjusted", "icc_conditional", 
-                    "icc_child_id", "icc_corpus",
-                    "child_id_var","corpus_var","residual_var",
-                    "child_id_sd","corpus_sd","residual_sd",
-                    "nobs", "nchi","ncor",
-                    "formula","sw")
-
+df.icc.age.cols =  c("data_set","age_bin","metric", "iqr",
+                       "age_b","age_se","age_t",
+                       "icc_adjusted", "icc_conditional",
+                       "icc_child_id", "icc_corpus",
+                       "child_id_var","corpus_var","residual_var",
+                       "child_id_sd","corpus_sd","residual_sd","r2_cond", "r2_marg",
+                       "nobs","nchi", "ncor",
+                       "formula","sw")
 
 df.icc.age = data.frame(matrix(ncol=length(df.icc.age.cols),nrow=0, dimnames=list(NULL, df.icc.age.cols)),
                         stringsAsFactors=FALSE)
@@ -98,7 +97,7 @@ for (data_set in data_sets){   # data_set = "aclew"
       if(dim(thiscordata)[1] > 30 & length(levels(factor(thiscordata$experiment))) > 1)
       {
         icc.row <- new_fit_models(thiscordata, data_set, metric, thisage, TRUE)
-        
+
       }else{
         icc.row <- c(data_set,thisage,metric,iqr,
                      NA,NA,NA,
@@ -113,4 +112,4 @@ for (data_set in data_sets){   # data_set = "aclew"
     } 
   }
 }
-write.csv(df.icc.age,"../output/df.icc.age.csv",row.names=F)
+write.csv(df.icc.age,"../output/df.icc.age.csv",row.names=F)

CODE/functions.R

@@ -51,7 +51,8 @@ extract_full_variables<-function(model){
 }
 
 new_fit_models<-function(dataframe, data_set, metric, age, fit_full = TRUE){
-  #dataframe=mydat ; age = NA ; full = TRUE
+  #dataframe=mydat ; age = NA ; fit_full = TRUE
+  #to check age: dataframe<-thiscordata ; age <- thisage ; fit_full = TRUE
   iqr = quantile(dataframe[,metric],.75,na.rm=T)-quantile(dataframe[,metric],.25,na.rm=T)
   
   if(fit_full){
@@ -67,7 +68,7 @@ new_fit_models<-function(dataframe, data_set, metric, age, fit_full = TRUE){
     sw=shapiro.test(resid(model))$p
     mod_variables <- extract_full_variables(model)
     
-    # Build line
+    
   } else {
     model <- fit_child_model(dataframe, metric)
     if(!isSingular(model)){

OUTPUT/df.icc.age.csv

@@ -1,319 +1,319 @@
-"data_set","age_bin","metric","iqr","age_b","age_se","age_t","icc_adjusted","icc_conditional","icc_child_id","icc_corpus","child_id_var","corpus_var","residual_var","child_id_sd","corpus_sd","residual_sd","nobs","nchi","ncor","formula","sw"
-"aclew","(0,6]","pc_fem_ph","1.42323376997767","-1.77897353907813","0.51606269257708","-3.44720431193041","0.389482279288843","0.353628324877073","0.389482279288842",NA,"0.367831891993892",NA,"0.576580502494292","0.60649146077574",NA,"0.759328981729456","0.44568374387389","0.0920554189968169","128","64",NA
-"aclew","(0,6]","pc_mal_ph","1.49434870019069","-1.08887700092972","0.569129180941589","-1.91323347562014","0.134357120988181","0.129466254448054","0.134357120988181",NA,"0.128810312305948",NA,"0.829905618480328","0.358901535669532",NA,"0.910991557853489","0.165868244191176","0.0364019897431226","126","64",NA
-"aclew","(0,6]","pc_adu_ph","1.45753779565783","-1.60154506179395","0.535497829448367","-2.99075920334496","0.267032025181875","0.245986045277898","0.267032025181875",NA,"0.242212032198403",NA,"0.664840341139337","0.492150416233089",NA,"0.815377422510175","0.324800486690467","0.0788144414125693","128","64",NA
-"aclew","(0,6]","sc_mal_ph","1.50575798463935","-1.06687024697469","0.569720641976028","-1.87261996208236","0.137928774991781","0.13311106236448","0.137928774991781",NA,"0.132499081462963",NA,"0.828134995587738","0.364004232754185",NA,"0.910019228141767","0.168040051094993","0.0349289887305137","126","64",NA
-"aclew","(0,6]","sc_fem_ph","1.42636174607824","-1.78177638233595","0.515902415776048","-3.45370815846191","0.391523557364613","0.355435112130736","0.391523557364614",NA,"0.37039845753674",NA,"0.575645402582255","0.608603694974603",NA,"0.758712990914387","0.447609502547477","0.0921743904167406","128","64",NA
-"aclew","(0,6]","wc_mal_ph","1.48966293972349","-1.05196219969057","0.569652900717008","-1.84667224263493","0.130971219724579","0.126519272567008","0.130971219724579",NA,"0.125818455415801",NA,"0.834838822422686","0.354708972843655",NA,"0.913695147422096","0.160511069901821","0.0339917973348131","126","64",NA
-"aclew","(0,6]","wc_fem_ph","1.41712553203159","-1.78212717457906","0.517240743882324","-3.44545010356822","0.383482430993514","0.347994589224584","0.383482430993514",NA,"0.361350350129861",NA,"0.580936234404627","0.601124238514686",NA,"0.762191730737501","0.44053557389757","0.0925409846729856","128","64",NA
-"aclew","(0,6]","wc_adu_ph","1.45278237963373","-1.59558974104347","0.535982677944232","-2.97694273845448","0.262236229031183","0.241679593411861","0.262236229031184",NA,"0.237484803828985",NA,"0.668129209560235","0.487324126048552",NA,"0.817391711213318","0.320069355555967","0.0783897621441055","128","64",NA
-"aclew","(0,6]","sc_adu_ph","1.42834636794385","-1.59848165081874","0.535399288116884","-2.98558792717292","0.269515873260672","0.248376717965905","0.269515873260673",NA,"0.2448136524541",NA,"0.663532299464357","0.494786471575467",NA,"0.814574919491361","0.32681052239835","0.0784338044324443","128","64",NA
-"aclew","(0,6]","can_voc_dur_chi_ph","1.19438910061092","0.435322428898399","0.600878078815767","0.724477134789721","0.305468927472918","0.303803079633062","0.138479527545917","0.166989399927001","0.144116897860623","0.173787380124521","0.722804774209136","0.379627314429064","0.416878135819715","0.850179260044102","0.309256491388022","0.0054534117549597","126","64","5"
-"aclew","(0,6]","cry_voc_dur_chi_ph","1.41068893553029","0.460614957492714","0.482328123275101","0.95498258398256","0.514532024197686","0.510859008322042","0.327503711149998","0.187028313047688","0.29298486433085","0.167315554171561","0.434299716873343","0.541280762941794","0.409042240082318","0.659014200813111","0.517997564724574","0.00713855640253283","127","64","5"
-"aclew","(0,6]","cry_voc_chi_ph","1.58338205382471","0.650386756303605","0.387859507947233","1.67686170630652","0.643649122667105","0.633064159413399","0.543499714761653","0.10014940790545","0.417311954309687","0.0768970875252615","0.273614644130912","0.645996868653159","0.277303241101256","0.523081871346075","0.649509397790862","0.0164452383774627","127","64","5"
-"aclew","(0,6]","avg_can_voc_dur_chi","1.40332222167747",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA
-"aclew","(0,6]","non_can_voc_chi_ph","1.43585370868167","-1.05227610707936","0.545987875850158","-1.92728841357669","0.436015183178292","0.421929214398674","0.262582576172564","0.173432607005729","0.266035578972428","0.175713273477616","0.571401307207141","0.515786369510118","0.419181671209055","0.755910912215944","0.454235357219069","0.0323061428203954","126","63","5"
-"aclew","(0,6]","non_can_voc_dur_chi_ph","1.37050386929717","-0.997854176804282","0.546877799471942","-1.82463829719875","0.43219089250715","0.41935696104028","0.294990060989056","0.137200831518094","0.29413245645038","0.136801956873214","0.566158354697806","0.542339797959157","0.369867485558293","0.75243495047599","0.449052014058219","0.0296950530179392","125","63","5"
-"aclew","(0,6]","avg_non_can_voc_dur_chi","1.24781546236844","0.616708961452544","0.600732087328896","1.02659567294747","0.20571490048509","0.203381173433519","0.164158805636787","0.0415560948483041","0.16513728685829","0.0418037931565998","0.799019497108633","0.40637087353585","0.20445975925986","0.8938789051704","0.214725646031608","0.0113444725980895","127","63","5"
-"aclew","(0,6]","lp_n","1.51677713488976","-1.32416849401178","0.458401380825022","-2.88866602371173","0.319727378901047","0.296173119769912","0.305465960152637","0.0142614187484099","0.203769055932173","0.00951345227849531","0.453793639424086","0.45140785984758","0.0975369277683858","0.673642070705272","0.369842941891792","0.0736698221218797","128","64","5"
-"aclew","(0,6]","lp_dur","1.49000284096167","-0.446715187085226","0.507755587546195","-0.879783892175454","0.307435034441641","0.305079353004819","0.270664048335507","0.036770986106133","0.212062023513393","0.0288096249509414","0.54261631315267","0.460501925634837","0.16973398289954","0.736624947413994","0.312741724474942","0.0076623714701228","127","64","5"
-"aclew","(0,6]","can_voc_chi_ph","1.51216571990988","0.701885400189947","0.581148826362209","1.20775499897936","0.365434722074518","0.360157918663828","0.226853748000647","0.138580974073872","0.233466010597373","0.14262028926951","0.653061389580624","0.48318320603822","0.377651015184005","0.808122137786501","0.374597716230777","0.0144397975669497","124","62","5"
-"aclew","(0,6]","avg_cry_voc_dur_chi","1.19415672856441",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA
-"aclew","(0,6]","cp_dur","1.39672270801406","1.05138295639058","0.613587451470516","1.71350139881585","0.19132232375278","0.185274237577506","0.116577808367402","0.074744515385378","0.118490680753554","0.0759709642395748","0.821946901478453","0.344224753255128","0.275628308124501","0.90661287299401","0.216886263067482","0.0316120254899759","127","64","5"
-"aclew","(0,6]","cp_n","1.37567000106379","1.54234026158304","0.594020603983867","2.59644236452264","0.22064186425151","0.205347137152915","0.161228245624857","0.0594136186266537","0.157411382040005","0.0580070804825372","0.760907871798972","0.396751032815297","0.240846591178985","0.872300333485533","0.274666380581537","0.0693192434286227","126","63","5"
-"aclew","(0,6]","avg_voc_dur_chi","1.29647960763633","0.947015457503535","0.595480894868359","1.59033726466218","0.187838310353634","0.182648012689938","0.08362583901738","0.104212471336254","0.0796269350626349","0.0992291352208016","0.773324930209557","0.282182449955051","0.315006563774156","0.879388952744778","0.21027974369265","0.0276317310027123","125","63","5"
-"aclew","(0,6]","avg_voc_dur_och","1.35902497633022","0.824947132332873","0.443875137707712","1.85851169000615","0.637371853739345","0.625149331774073","0.637371853739343",NA,"0.675396463215902",NA,"0.384261975187794","0.821825080668571",NA,"0.619888679673854","0.644325769976614","0.0191764382025414","126","62",NA
-"aclew","(0,6]","avg_voc_dur_mal","1.31370308178976","0.246679469259637","0.611947302460369","0.403105738464486","0.573696812820256","0.573065596511048","0.0636923020333783","0.510004510786876","0.107932624672565","0.864250838590003","0.722411036022479","0.328531010214508","0.929650922975932","0.849947666637469","0.574165857650909","0.00110026113986006","125","64","5"
-"aclew","(0,6]","avg_voc_dur_fem","1.45961084832672","-1.33270562712153","0.595029896589009","-2.23972885187992","0.315549570872605","0.300121459463172","0.237661937661292","0.0778876332113121","0.247265077300638","0.0810348170863015","0.712106826747479","0.497257556303208","0.284666150229179","0.843864222933689","0.349014289096149","0.0488928296329772","126","64","5"
-"aclew","(0,6]","voc_dur_chi_ph","1.39634224955025","-0.17653950838588","0.541142556859203","-0.326234753020566","0.43884363761044","0.438421666000404","0.205897684897654","0.232945952712788","0.202776280128015","0.229414496746067","0.552649243200985","0.450306873285335","0.47897233400904","0.743403822428285","0.439383219441931","0.000961553441527367","126","64","5"
-"aclew","(0,6]","voc_dur_och_ph","1.02011755297746","0.187860306463376","0.297848364252067","0.630724653919506","0.869654257919224","0.868868192584599","0.869654257919225",NA,"1.04475979899947",NA,"0.156590955608702","1.0221349221113",NA,"0.39571575102427","0.869772075164908","0.000903882580309372","122","59",NA
-"aclew","(0,6]","voc_dur_mal_ph","1.59200322392544","-1.22244281574427","0.623482690057453","-1.96066841187143","0.188506867892254","0.180428842593153","0.150555657632614","0.0379512102596396","0.154004072761649","0.0388204670493372","0.830080046992718","0.392433526551503","0.19702910203657","0.911087288349869","0.223281526898101","0.0428526843049484","125","64","5"
-"aclew","(0,6]","voc_dur_fem_ph","1.52743673029468","-1.71795555528096","0.514241047715902","-3.34075928576994","0.42801817951299","0.392497302517532","0.42801817951299",NA,"0.418975317898997",NA,"0.559897398151797","0.647283027661777",NA,"0.748262920471005","0.475486480771302","0.0829891782537701","127","64",NA
-"aclew","(0,6]","voc_chi_ph","1.39984969328611","-0.489176647297947","0.488429805262361","-1.00152906728365","0.548929929308774","0.544956585037553","0.333897082863009","0.215032846445766","0.331560270543541","0.213527917440829","0.447913211430593","0.575812704395745","0.46209081081626","0.669263185473841","0.552194930150607","0.00723834511305362","128","64","5"
-"aclew","(0,6]","voc_och_ph","1.14117093377138","0.0433071018329536","0.337119297622477","0.128462245081713","0.83248025673889","0.832440177982808","0.766059292986223","0.0664209637526694","0.910491075202944","0.0789438823559462","0.199103688912853","0.954196560045646","0.280969539907703","0.446210363968446","0.832488321774517","4.81437917089332e-05","125","62","5"
-"aclew","(0,6]","voc_mal_ph","1.54872975385887","-1.67544441660942","0.546532913162253","-3.06558740792985","0.0927974886561106","0.0847457009113733","0.0927974886561106",NA,"0.0837496794206819",NA,"0.818749737681454","0.289395368692524",NA,"0.904847908590971","0.171513003144903","0.0867673022335301","126","64",NA
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