still improving

CODE/SM.Rmd

@@ -79,6 +79,7 @@ get_type<-function(mytab){
 }
 ```
 
+
 ## Recalculate everything or not?
 
 If RECALC is set to TRUE, then the ICC tables will be re-generated and the simulation re-ran. Notice that you can only do this if you have access to underlying data. If you are not one of the paper's authors, please email us for access to reproduce this section. You do not need access to reproduce all the rest.
@@ -89,14 +90,49 @@ RECALC=FALSE
 
 if(RECALC) source("regenerate_data.R") # This code cannot be reproduced without access to the underlying datasets
 
+if(RECALC) source("create-sib-subdataset.R") #This code may not be reproducible in the future
+
 if(RECALC) source("create-all-icc.R") 
 
 if(RECALC) source("simulate-r-for-icc.R") #when ran on Aug 2 2023, it gave an error chol(): decomposition failed -- perhaps one of our newly added datasets (lyon, quechua) is too small? -- happened again on Aug 15, need to double check how this affects simulation; I suspect this is bc overlap bet function names across this & previous code
 #aug 18, now it runs for ever...
+
+if(RECALC) source("create-all-rs.R")
+```
+
+
+## Read in all data
+
+```{r readin}
+
+df.icc.simu <- read.csv("../output/df.icc.simu.csv")
+
+mydat_aclew <- read.csv(paste0('../data_output/', 'aclew','_metrics_scaled.csv')) 
+mydat_aclew <- mydat_aclew[is.element(mydat_aclew$experiment, corpora),]
+
+mydat_lena <- read.csv(paste0('../data_output/', 'lena','_metrics_scaled.csv'))
+mydat_lena <- mydat_lena[is.element(mydat_lena$experiment, corpora),]
+
+all_rs <- read.csv("../data_output/all_rs.csv")
+
+df.icc.mixed<-read.csv("../output/df.icc.mixed.csv")
+df.icc.mixed$Type<-get_type(df.icc.mixed)
+
+
+df.icc.age<-read.csv("../output/df.icc.age.csv")
+age_levels=c("(0,6]" , "(6,12]",  "(12,18]" ,"(18,24]" ,"(24,30]", "(30,36]" )
+#not present in data: , "(36,42]", "(42,48]", "(48,54]"
+df.icc.age$age_bin<-factor(df.icc.age$age_bin,levels=age_levels)
+df.icc.age$Type<-get_type(df.icc.age)
+
+df.icc.corpus<-read.csv("../output/df.icc.corpus.csv")
+df.icc.corpus$Type <- get_type(df.icc.corpus)
+
 ```
 
 
-## Simulation to better understand ICC
+
+## SM A: Simulation to better understand ICC
 
 We were uncertain of how to interpret ICC's numeric values. It is described as "proportion of variance explained", but we do not know if it should be considered as a percentage (like R^2) or a correlation (like r). We therefore simulated data controlling the underlying r between paired datapoints to see how ICC recovered that underlying r.
 
@@ -117,14 +153,13 @@ We use simstudy, a package created for such simulations, following the vignette
 
 In the following plot, each point represents the ICC extracted from a mixed model applied to one metric, combining data from all corpora. It appears that ICC values reflect underlying r values, but underestimating r more the larger r is. 
 
-```{r}
-read.csv("../output/df.icc.simu.csv")->df.icc.mixed
+```{r icc-sim-plot}
 
-plot(df.icc.mixed$icc_child_id~df.icc.mixed$myr,xlab="r used to simulate the data",ylab="Estimated ICC from simulated data")
+plot(df.icc.simu$icc_child_id~df.icc.simu$myr,xlab="r used to simulate the data",ylab="Estimated ICC from simulated data")
 
 ```
 
-## More information for benchmarking our results against previously reported reliability studies
+## SM B: More information for benchmarking our results against previously reported reliability studies
 
 First, we looked for measures of language development used with observational data that can be employed with children aged 0-3 years, and which are available at least in English. All of the instruments we found rely on reports from caregivers, who are basing their judgments on their cumulative experience with the child (e.g., the Child Observation Record Advantage, Schweinhart, McNair, & Larner, 1993; the Desired Results Developmental Profile, REF; the MacArthur-Bates Communicative Development Inventory, REF). Readers are likely most familiar with the MB-CDI,  Fenson et al., 1994 report a correlation of r=.95 in their sample of North American, monolingual infants. We did not find a systematic review or meta-analysis providing more such estimates. However, @frank2021 analyzed data archived in a CDI repository, concentrating on American English and Norwegian, where longitudinal data was available. They found that for both datasets, correlations within 2-4 months were above r=.8, with correlations at 16 months of distance (i.e., a form filled in at 8 and 24 months) at their lowest, r=.5. These correlations are very high when considering that the CDI tends to have ceiling and floor effects at these extreme ages. Another report looked at correlations when parents completed two versions of the form in two media (e.g., short form in paper and long form online, or vice versa) within a month. Here, the correlation was r=.8 for comprehension and r=.6 for production. It is worth bearing in mind that test-retest reliability in parental report measures does not depend only on the consistency in the individual infants' ranking for a given behavior, but also on the consistency of the adult in reporting it. Moreover, they are based on cumulative experience, rather than a one-shot observation, as in the case of long-form recordings. Therefore, they do not constitute an appropriate comparison point, and by and large we can be quite certain that they will yield higher reliability than metrics based on the children themselves.  For example, a meta-analysis of infant laboratory tasks, including test-retest data for sound discrimination, word recognition, and prosodic processing, found that the meta-analytic weighted average was not different from zero, suggesting that performance in these short laboratory tasks may not be captured in a stable way across testing days. Thus, parental report (or short lab studies) may not be the most appropriate comparisons for our own study.
 
@@ -135,34 +170,32 @@ For the other tests, reliability is mainly available from reports by the compani
 
 Many other standardized tests exist for children over three years of age. Given that most children included in the present study were under three, these other tests do not constitute an ideal comparison point. Nonetheless, in the spirit of informativeness, it is useful to consider that a systematic review and meta-analysis has been done looking at all psychometric properties (internal consistency, reliability, measurement error, content and structural validity, convergent and discriminant validity) for standardized assessments targeted at children aged 4-12 years (REF). Out of 76 assessments found in the literature, only 15 could be evaluated for their psychometric properties, and 14 reported on reliability based on independent evidence (i.e., the researchers tested and retested children, rather than relying on the company's report of reliability). Among these, correlations for test-retest reliability averaged r=.67, with a range from .35 to .76. The authors concluded that psychometric quality was limited for all assessments, but based on the available evidence, PLS-5 (whose test-retest reliability was r=.69) was among those recommended for use.
 
-Third, and perhaps most relevant, we looked for references that evaluated the psychometric properties of measures extracted from wearable data. We found no previous work attempting to do so on the basis of completely ecological, unconstrained data like ours. The closest references we could find reported on reliability and/or validity of measurements from wearable data collected in constrained situations, such as having 4.5 year old children wear interior sensors and asking them to complete four tests of balance (e.g., standing with their eyes closed; Liu et al., 2022). It is likely that consistency and test-retest reliability are higher in such cases than in data like ours, making it hard to compare. Nonetheless, to give an idea, a recent meta-analysis of wea?read.tablerable inertial sensors in healthy adults found correlations between these instruments and gold standards above r= .88 for one set of measures (based on means) but much lower for another (based on variability, max weighted mean effect r = .58). Regarding test-retest reliability, the meta-analysts report ICCs above .6 for all measures for which they could find multiple studies reporting them. However, those authors point out that the majority of the included studies were classified as low quality, according to a standardized quality assessment for that work. 
+Third, and perhaps most relevant, we looked for references that evaluated the psychometric properties of measures extracted from wearable data. We found no previous work attempting to do so on the basis of completely ecological, unconstrained data like ours. The closest references we could find reported on reliability and/or validity of measurements from wearable data collected in constrained situations, such as having 4.5 year old children wear interior sensors and asking them to complete four tests of balance (e.g., standing with their eyes closed; Liu et al., 2022). It is likely that consistency and test-retest reliability are higher in such cases than in data like ours, making it hard to compare. Nonetheless, to give an idea, a recent meta-analysis of wearable inertial sensors in healthy adults found correlations between these instruments and gold standards above r= .88 for one set of measures (based on means) but much lower for another (based on variability, max weighted mean effect r = .58). Regarding test-retest reliability, the meta-analysts report ICCs above .6 for all measures for which they could find multiple studies reporting them. However, those authors point out that the majority of the included studies were classified as low quality, according to a standardized quality assessment for that work. 
 
 
 
-## Code to reproduce Table 2
+## SM C: Code to reproduce Table 2
 
 
 
 ```{r tab2}
-mydat <- read.csv(paste0('../data_output/', 'aclew','_metrics.csv'))
-
-child_per_corpus= aggregate(data = mydat, child_id ~ experiment, function(child_id) length(unique(child_id)))[,2]
+child_per_corpus= aggregate(data = mydat_aclew, child_id ~ experiment, function(child_id) length(unique(child_id)))[,2]
 
-rec_per_corpus = aggregate(data = mydat, session_id ~ experiment, function(session_id) length(unique(session_id)))[,2]
+rec_per_corpus = aggregate(data = mydat_aclew, session_id ~ experiment, function(session_id) length(unique(session_id)))[,2]
 
-rec_per_child = setNames(aggregate(data = mydat, session_id ~ experiment*child_id, function(session_id) length(unique(session_id))), c('experiment', 'Chi', 'No_rec'))
+rec_per_child = setNames(aggregate(data = mydat_aclew, session_id ~ experiment*child_id, function(session_id) length(unique(session_id))), c('experiment', 'Chi', 'No_rec'))
 
 min_rec_per_child = aggregate(data = rec_per_child, No_rec ~ experiment, min)[,2]
 max_rec_per_child = aggregate(data = rec_per_child, No_rec ~ experiment, max)[,2]
 rec_r_per_child = paste(min_rec_per_child,max_rec_per_child,sep="-")
 
-dur_per_corpus = aggregate(data = mydat, duration_vtc ~ experiment, function(duration_vtc) round(mean(duration_vtc)/3.6e+6,1))[,2]
+dur_per_corpus = aggregate(data = mydat_aclew, duration_vtc ~ experiment, function(duration_vtc) round(mean(duration_vtc)/3.6e+6,1))[,2]
 
-age_mean_per_corpus = aggregate(data = mydat, age ~ experiment, function(age) round(mean(age),1))[,2]
+age_mean_per_corpus = aggregate(data = mydat_aclew, age ~ experiment, function(age) round(mean(age),1))[,2]
 
-age_min_per_corpus = aggregate(data = mydat, age ~ experiment, function(age) min(age))[,2]
+age_min_per_corpus = aggregate(data = mydat_aclew, age ~ experiment, function(age) min(age))[,2]
 
-age_max_per_corpus = aggregate(data = mydat, age ~ experiment, function(age) max(age))[,2]
+age_max_per_corpus = aggregate(data = mydat_aclew, age ~ experiment, function(age) max(age))[,2]
 
 age_r_per_corpus = paste(age_min_per_corpus,age_max_per_corpus,sep="-")
 
@@ -171,33 +204,26 @@ age_r_per_corpus = paste(age_min_per_corpus,age_max_per_corpus,sep="-")
 experiments=c("bergelson", "cougar", "fausey-trio", "lucid","lyon", "quechua",  "warlaumont", "winnipeg")
 locations=c("Northeast US", "Northwest US", "Western US", "Northwest England", "Central France", "Highlands Bolivia", "Western US", "Western Canada")
 
-  corpus_description=cbind(experiments,child_per_corpus, rec_r_per_child, rec_per_corpus, dur_per_corpus, age_mean_per_corpus,age_r_per_corpus)
-
+corpus_description=cbind(experiments,child_per_corpus, rec_r_per_child, rec_per_corpus, dur_per_corpus, age_mean_per_corpus,age_r_per_corpus)
 
 write.table(corpus_description, "../output/corpus_description.csv", sep='\t')
 
-
-nkids=length(levels(factor(paste(mydat$experiment,mydat$child_id))))
-nrecs=length(levels(factor(paste(mydat$experiment,mydat$session_id))))
+nkids=length(levels(mydat_aclew$child_id))
+nrecs=length(levels(mydat_aclew$session_id))
 ```
 
-## Code to reproduce Fig. 2
+## SM D: Code to reproduce Fig. 2
 
 ```{r icc-examples-fig2, echo=F,fig.width=4, fig.height=3,fig.cap="(A) scatterplot for one variable with relatively low ICCs versus (B) one with relatively higher ICCs (see Tables 1-2 for details)"}
 # figure of bad ICC: lena     used to be: avg_voc_dur_chi, now is: peak_wc_adu_ph; good ICC: lena used to be: voc_och_ph, now is: voc_dur_och_ph
-data_set="lena"
-#mydat <- read.csv(paste0('../data_output/', data_set,'_metrics.csv'))
-mydat <- read.csv(paste0('../data_output/', data_set,'_metrics_scaled.csv'))
-mydat <- mydat[is.element(mydat$experiment, corpora),]
-
 
 # remove missing data points altogether
-mydat <- mydat[!is.na(mydat$peak_wc_adu_ph) & !is.na(mydat$voc_dur_och_ph),]
+mydat_lena_nomissing <- mydat_lena[!is.na(mydat_lena$peak_wc_adu_ph) & !is.na(mydat_lena$voc_dur_och_ph),]
 
 #sample down to get 2 recs per child
 mysample = NULL
-for(thischild in levels(as.factor(mydat$child_id))){
-  onechidat <- mydat[mydat$child_id == thischild,c("child_id","experiment","age","child_id","peak_wc_adu_ph","voc_dur_och_ph")]
+for(thischild in levels(as.factor(mydat_lena_nomissing$child_id))){
+  onechidat <- mydat_lena_nomissing[mydat_lena_nomissing$child_id == thischild,c("child_id","experiment","age","child_id","peak_wc_adu_ph","voc_dur_och_ph")]
   
   if(dim(onechidat)[1]>=2){
     selrows=sample(1:dim(onechidat)[1],2)
@@ -258,103 +284,127 @@ ggarrange(bad, good,
 
 ```
 
+## SM E: Code to reproduce text at the beginning of the "Setting the stage" section
 
-## Code to reproduce other text in the section Results, subsection Overall reliability
+```{r gen-table}
 
-```{r readin}
-df.icc.mixed<-read.csv("../output/df.icc.mixed.csv")
+#summary(all_rs)
+rval_tab=cbind(apply(all_rs[,1:20],1,mean),apply(all_rs[,1:20],1,sd),all_rs[,c("data_set","metric")])
+colnames(rval_tab) <-c("m","sd","data_set","metric") 
+rval_tab[,1:2]=round(rval_tab[,1:2],2)
 
-#df.icc.mixed[df.icc.mixed$formula=="no_exp",c("data_set","metric")]
+rval_tab$fin<-paste0(rval_tab$m," ","[",rval_tab$m-2*rval_tab$sd,",",rval_tab$m+2*rval_tab$sd,"]")
 
-df.icc.mixed$Type<-get_type(df.icc.mixed)
+mytab=rbind(cbind(rval_tab[rval_tab$data_set=="aclew" & rval_tab$metric== "wc_adu_ph","fin"],rval_tab[rval_tab$data_set=="lena" & rval_tab$metric== "wc_adu_ph","fin"]),
+            cbind(rval_tab[rval_tab$metric== "can_voc_chi_ph","fin"],rval_tab[rval_tab$metric== "lena_CVC_ph","fin"]),
+            cbind(rval_tab[rval_tab$metric== "simple_CTC_ph","fin"],rval_tab[rval_tab$metric== "lena_CTC_ph","fin"]),
+            cbind(rval_tab[rval_tab$data_set=="aclew" & rval_tab$metric== "voc_chi_ph","fin"],rval_tab[rval_tab$data_set=="lena" & rval_tab$metric== "voc_chi_ph","fin"])
+       )
+mytab=gsub("0.",".",mytab,fixed=T)
+colnames(mytab)<-c("aclew","lena")
+rownames(mytab)<-c("AWC","CVC","CTC","Chi vocs")
+print(mytab)
 
+rval_tab$Type<-get_type(rval_tab)
 
 ```
 
-Out of the `r dim(df.icc.mixed)[1]` fitted models, `r table(df.icc.mixed$formula)["full"]` could be fit with the full model, yielding a measure of Corpus ICC. Of these, `r round(sum(df.icc.mixed$icc_corpus<.2,na.rm=T)/sum(!is.na(df.icc.mixed$icc_corpus))*100)`% had Corpus ICCs smaller than .2, consistent with the idea that LENA and ACLEW metrics are robust to corpus differences. For the `r table(df.icc.mixed$formula)["no_exp"]` for which the full model was singular, we fit the data with the No Corpus model, and none was singular then, allowing us to have Child ICC for all `r dim(df.icc.mixed)[1]` metrics. 
+Out of our `r length(levels(factor(mydat_aclew$experiment)))` corpora and `r length(levels(factor(mydat_aclew$child_id)))` children, `r length(levels(factor(dist_contig_lena$child_id)))` children (belonging to `r length(levels(factor(gsub(" .*","",dist_contig_lena$child_id))))`  corpora) could be included in this analysis, as some children did not have recordings less than two months apart (in particular, no child from the Warlaumont corpus did). 
 
+## SM F: Exploration: Is lower Child ICC than correlations due to the fact that the overall Child ICC includes data more than 1 month away? THIS ONE NEEDS WORK
 
+```{r}
+rval_tab$id=paste(rval_tab$data_set,rval_tab$metric)
+df.icc.mixed$id=paste(df.icc.mixed$data_set,df.icc.mixed$metric)
+r_icc=merge(rval_tab,df.icc.mixed,by="id")
 
-## Code to reproduce Fig. 3
+```
 
-```{r icc-allexp-fig3, echo=F,fig.width=4, fig.height=3,fig.cap="Distribution of ICC attributed to corpus (a) and children (b), when combining data from all corpora."}
 
+## SM G: Code to reproduce Figure 3
 
+```{r r-fig3, echo=F,fig.width=4, fig.height=3,fig.cap="Distribution of correlation coefficients."}
 
-ggplot(df.icc.mixed, aes(y = icc_child_id, x = toupper(data_set))) +
+
+ggplot(rval_tab, aes(y = m, x = toupper(p))) +
   geom_violin(alpha = 0.5) +
   geom_quasirandom(aes(colour = Type,shape = Type)) +  
-  labs( y = "ICC child ID",x="Pipeline") +  theme(text = element_text(size = 20)) + 
-  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
-panel.background = element_blank(), legend.key=element_blank(), axis.line = element_line(colour = "black")) 
+  theme() +labs( y = "r",x="Pipeline")
+
 
 ```
 
+## SM H: Code to reproduce text under Figure 3
+
+```{r reg model cor}
+
+
+lr_cor <- lm(m ~ Type * p, data=rval_tab) 
+#plot(lr_cor)
+#binomial could be used, but diagnostic plots look great
+
+reg_sum_cor=summary(lr_cor)
+
+reg_anova_cor=Anova(lr_icc_chi)
+
+r_msds=aggregate(rval_tab$m,by=list(rval_tab$data_set),get_msd)
+rownames(r_msds)<-r_msds$Group.1
+
+cor_t=t.test(rval_tab$m ~ rval_tab$data_set)
+
+```
+
+To see whether correlations in this analysis differed by talker types and pipelines, we fit a linear model with the formula $lm(cor ~ type * pipeline)$, where type indicates whether the measure pertained to the key child, (female/male) adults, other children; and pipeline LENA or ACLEW. We found an adjusted R-squared of `r round(reg_sum_cor$adj.r.squared*100)`%, suggesting this model did not explain a great deal of variance in correlation coefficients. A Type 3 ANOVA on this model revealed a significant effect of pipeline (F = `r round(reg_anova_cor["data_set","F value"],2)`, p = `r round(reg_anova_cor["data_set","Pr(>F)"],2)`), due to higher correlations for ACLEW (`r r_msds["aclew","x"]`) than for LENA metrics (m = `r r_msds["lena","x"]`). See below for fuller results.
+
+```{r print out anova results rec on cor}
+
+kable(round(reg_anova_cor,2))
+```
+
+
+## SM I: Code to reproduce text at the beginning of the "Overall reliability" section
+
+
+Out of the `r dim(df.icc.mixed)[1]` fitted models, `r table(df.icc.mixed$formula)["full"]` could be fit with the full model, yielding a measure of Corpus ICC. Of these, `r round(sum(df.icc.mixed$icc_corpus<.2,na.rm=T)/sum(!is.na(df.icc.mixed$icc_corpus))*100)`% had Corpus ICCs smaller than .2, consistent with the idea that LENA and ACLEW metrics are robust to corpus differences. For the `r table(df.icc.mixed$formula)["no_exp"]` for which the full model was singular, we fit the data with the No Corpus model, and none was singular then, allowing us to have Child ICC for all `r dim(df.icc.mixed)[1]` metrics. 
 
+```{r best_metric}
 
-## Code to reproduce text under Figure 3
+ismax = aggregate(df.icc.mixed$icc_child_id,by=list(df.icc.mixed$Type),max)
+best_metric<-df.icc.mixed[df.icc.mixed$icc_child_id%in%ismax$x,c("metric","data_set","icc_child_id","Type")]
+best_metric$icc_child_id=round(best_metric$icc_child_id,2)
 
+```
 
 
-The majority of measures had ICCs between .3 and .5. `r sum(df.icc.mixed$icc_child_id > .5)` measures had higher ICCs, and surprisingly, `r sum(df.icc.mixed$icc_child_id[grep("och",df.icc.mixed$metric)] > .5)` of them corresponded to the "other child" category, known to have the worst accuracy according to previous analyses (Cristia et al., 2020). 
+Figure 3 shows the distribution of Child ICC across all 69 metrics, separately for each pipeline. The majority of measures had Child ICCs between .3 and .5. `r sum(df.icc.mixed$icc_child_id > .5)` measures had Child ICCs higher or equal to .5. Surprisingly, the top 6 metrics in terms of Child ICC corresponded to the "other child" category, known to have the worst accuracy according to previous analyses (Cristia et al., 2020). In an analysis fully reported in the SM, we find some evidence that this may be due to the presence versus absence of siblings. The next metric with the highest Child ICC corresponded to an output measure, namely the total vocalization duration per hour extracted from ACLEW annotations (`r best_metric[best_metric$Type=="Output",c("metric","data_set")]`), with a Child ICC of `r best_metric[best_metric$Type=="Output","icc_child_id"]`. Among adult metrics, the average vocalization duration for female vocalizations for ACLEW (`r best_metric[best_metric$Type=="Female",c("metric","data_set")]`) and the ACLEW equivalent of CTC had the highest Child ICC (`r best_metric[best_metric$Type=="Female","icc_child_id"]` and `r best_metric[best_metric$Type=="Adults","icc_child_id"]`, respectively). 
 
-### Checking whether high ICC for other child measures are due to number of siblings
+## SM J: Exploration: Are high Child ICCs for "other child" measures due to number or presence of siblings?
 
 ```{r explo-och-sibn}
 
-#df.icc.mixed[df.icc.mixed$icc_child_id>.5,c("data_set","metric")]
-#df.icc.mixed[df.icc.mixed$icc_child_id>.5,c("data_set","metric","icc_child_id")]
-
-myaclewdat <- read.csv(paste0('../data_output/', "aclew",'_metrics_scaled.csv'))
-myaclewdat <- myaclewdat[is.element(myaclewdat$experiment, corpora),]
-
-read.csv("../input/aclew_md.csv")->x
-x$labname[x$labname=="ROW"]<-"luc"
-x$labname[x$labname=="SOD"]<-"win"
-x$ch_id=paste(tolower(x$labname),as.character(x$child_level_id))
-x$n_of_siblings<-x$number_older_sibs
-
-x$ch_id[x$labname %in% c("BER")] = paste(tolower(x$labname[x$labname %in% c("BER")]),as.numeric(as.character(x$child_level_id[x$labname %in% c("BER")])))
-x=x[!duplicated(x$ch_id),]
-
-myaclewdat$lab=substr(myaclewdat$experiment,1,3)
-myaclewdat$ch_id=paste(myaclewdat$lab,gsub(".* ","",myaclewdat$child_id))
-myaclewdat$ch_id[myaclewdat$experiment=="warlaumont"]=gsub(" 0"," ",myaclewdat$ch_id[myaclewdat$experiment=="warlaumont"])
-myaclewdat$ch_id[myaclewdat$experiment=="winnipeg"]=gsub(" C"," CW",myaclewdat$ch_id[myaclewdat$experiment=="winnipeg"])
-
-#sort(factor(myaclewdat$ch_id[myaclewdat$experiment=="winnipeg"]))
-#sort(x$ch_id[x$lab=="win"])
-#sum(myaclewdat$ch_id %in% x$ch_id)
-#sum(x$ch_id %in% myaclewdat$ch_id)
-
-metadata=x[,c("ch_id","n_of_siblings")]
-read.csv("../input/quechua_md.csv")->x 
-x$ch_id=paste("que",x$child_id)
-metadata=rbind(metadata,x[,c("ch_id","n_of_siblings")])
-mydat2=merge(myaclewdat,metadata,all.x=T,by="ch_id")
-#table(mydat2$n_of_siblings,mydat2$experiment)
-has_n_of_sib=table(mydat2$experiment,!is.na(mydat2$n_of_siblings))
-corp_w_sib=levels(factor(mydat2$experiment[!is.na(mydat2$n_of_siblings)]))
-corp_w_sib_clean=corp_w_sib[1]
-for(i in 2:length(corp_w_sib)) corp_w_sib_clean=paste(corp_w_sib_clean,corp_w_sib[i],sep=", ")
-  
-model<-lmer(voc_dur_och_ph~ age_s +n_of_siblings  + (1|experiment/child_id),data=mydat2)
+
+model<-lmer(voc_dur_och_ph~ age_s + n_of_siblings  + (1|experiment/child_id),data=mydat2)
 
 #is sing
-model<-lmer(voc_dur_och_ph~ age_s +n_of_siblings  + (1|child_id),data=mydat2)
+model<-lmer(voc_dur_och_ph~ age_s + n_of_siblings  + (1|child_id),data=mydat2)
 icc.result.split<- t(as.data.frame(icc(model, by_group=TRUE))$ICC)
 #names(icc.result.split)=c("icc_child_id", "icc_corpus")
 names(icc.result.split)=c("icc_child_id")
 
+
+has_n_of_sib=table(mydat2$experiment,!is.na(mydat2$n_of_siblings))
+corp_w_sib=levels(factor(mydat2$experiment[!is.na(mydat2$n_of_siblings)]))
+corp_w_sib_clean=corp_w_sib[1]
+for(i in 2:length(corp_w_sib)) corp_w_sib_clean=paste(corp_w_sib_clean,corp_w_sib[i],sep=", ")
+
+
 ```
 
 
-We reasoned this may be because children in our corpora vary in terms of the number of siblings they have, that siblings' presence may be stable across recordings, and that a greater number of siblings would lead to more other child vocalizations. As a result, any measure based on other child vocalizations would result in stable relative ranking of children due to the number of siblings present. To test this hypothesis, we selected the metric with the highest Child ICC, namely ACLEW's total vocalization duration by other children. We fit the full model again to predict this metric, but this time, in addition to controlling for age, we included sibling number as a fixed effect $lmer(metric~ age + sibling_number + (1|corpus/child))$, so that individual variation that was actually due to sibling number was captured by that fixed effect instead of the random effect for child. We had sibling number data for `r sum(has_n_of_sib[,"TRUE"])` recordings from `r length(levels(factor(mydat2$child_id[!is.na(mydat2$n_of_siblings)])))` in `r length(levels(factor(mydat2$experiment[!is.na(mydat2$n_of_siblings)])))` corpora (`r corp_w_sib_clean`). The number of siblings varied from `r min(mydat2$n_of_siblings,na.rm=T)` to `r max(mydat2$n_of_siblings,na.rm=T)`, with a mean of `r round(mean(mydat2$n_of_siblings,na.rm=T),1)` and a median of `r round(median(mydat2$n_of_siblings,na.rm=T),1)`.  Results indicated the full model was singular, so we fitted a No Corpus model to be able to extract a Child ICC. As a sanity check, we verified that the number of siblings predicted the outcome, total vocalization duration by other children -- and found that it did: ß = `r round(summary(model)$coefficients["n_of_siblings","Estimate"],2)`, t = `r round(summary(model)$coefficients["n_of_siblings","t value"],2)`, p < .001. This effect is relatively small: It means that per additional sibling, there is a .2 standard deviation increase in this variable. Turning now to how much variance is allocated to the random factor of Child, there was no difference in Child ICC in our original analysis (`r round(df.icc.mixed[df.icc.mixed$metric=="voc_dur_och_ph" & df.icc.mixed$data_set=="aclew","icc_child_id"],2)`) versus this re-analysis including the number of siblings (`r round(icc.result.split["icc_child_id"],2)`).
+We reasoned the high Child ICC for metrics related to other children may be because children in our corpora vary in terms of the number of siblings they have, that siblings' presence may be stable across recordings, and that a greater number of siblings would lead to more other child vocalizations. As a result, any measure based on other child vocalizations would result in stable relative ranking of children due to the number of siblings present. To test this hypothesis, we selected the metric with the highest Child ICC, namely ACLEW's total vocalization duration by other children. We fit the full model again to predict this metric, but this time, in addition to controlling for age, we included sibling number as a fixed effect $lmer(metric~ age + sibling_number + (1|corpus/child))$, so that individual variation that was actually due to sibling number was captured by that fixed effect instead of the random effect for child. We had sibling number data for `r sum(has_n_of_sib[,"TRUE"])` recordings from `r length(levels(factor(mydat2$child_id[!is.na(mydat2$n_of_siblings)])))` in `r length(levels(factor(mydat2$experiment[!is.na(mydat2$n_of_siblings)])))` corpora (`r corp_w_sib_clean`). The number of siblings varied from `r min(mydat2$n_of_siblings,na.rm=T)` to `r max(mydat2$n_of_siblings,na.rm=T)`, with a mean of `r round(mean(mydat2$n_of_siblings,na.rm=T),1)` and a median of `r round(median(mydat2$n_of_siblings,na.rm=T),1)`.  Results indicated the full model was singular, so we fitted a No Corpus model to be able to extract a Child ICC. As a sanity check, we verified that the number of siblings predicted the outcome, total vocalization duration by other children -- and found that it did: ß = `r round(summary(model)$coefficients["n_of_siblings","Estimate"],2)`, t = `r round(summary(model)$coefficients["n_of_siblings","t value"],2)`, p < .001. This effect is relatively small: It means that per additional sibling, there is a .2 standard deviation increase in this variable. Turning now to how much variance is allocated to the random factor of Child, there was no difference in Child ICC in our original analysis (`r round(df.icc.mixed[df.icc.mixed$metric=="voc_dur_och_ph" & df.icc.mixed$data_set=="aclew","icc_child_id"],2)`) versus this re-analysis including the number of siblings (`r round(icc.result.split["icc_child_id"],2)`).
 
-### Checking whether high ICC for other child measures are due to presence of siblings
 
 ```{r sib-presence}
-mydat2$sib_presence=ifelse(mydat2$n_of_siblings!=0,"present","absent")
 model_sib_presence<-lmer(voc_dur_och_ph~ age_s + sib_presence  + (1|experiment/child_id),data=mydat2)
 
 #is sing
@@ -367,10 +417,49 @@ names(icc.result.split)=c("icc_child_id")
 ```
 
 
-Perhaps it is not so much the sheer number of siblings that explains variance, but the sheer presence versus absence. After all, we can imagine that the effect of the number of siblings is not monotonic. We therefore repeated the analysis above but rather than adding the actual number of siblings, we had a binary variable that was "present" if the child had any siblings, and "absent" otherwise. As in the sibling number analysis, the full model was singular, so we fitted a No Corpus model to be able to extract a Child ICC. We again verified that sibling presence predicted the outcome, total vocalization duration by other children -- and found that it did: ß = `r round(summary(model_sib_presence)$coefficients["sib_presencepresent","Estimate"],2)`, t = `r round(summary(model_sib_presence)$coefficients["sib_presencepresent","t value"],2)`, p < .001. This effect is, as expected, sizable: It means that there is nearly one whole standard deviation increase in this variable when there are any siblings. In addition to being a better predictor, in this model, the amount of variance allocated to individual children as measured by Child ICC was considerably higher in our original analysis (`r round(df.icc.mixed[df.icc.mixed$metric=="voc_dur_och_ph" & df.icc.mixed$data_set=="aclew","icc_child_id"],2)`) than in this re-analysis including sibling presence (`r round(icc.result.split["icc_child_id"],2)`).
+Perhaps it is not so much the sheer number of siblings that explains variance, but the sheer presence versus absence. After all, we can imagine that the effect of the number of siblings is not monotonic. We therefore repeated the analysis above but rather than adding the actual number of siblings, we had a binary variable that was "present" if the child had any siblings, and "absent" otherwise. 
+
+As in the sibling number analysis, the full model was singular, so we fitted a No Corpus model to be able to extract a Child ICC. We again verified that sibling presence predicted the outcome, total vocalization duration by other children -- and found that it did: ß = `r round(summary(model_sib_presence)$coefficients["sib_presencepresent","Estimate"],2)`, t = `r round(summary(model_sib_presence)$coefficients["sib_presencepresent","t value"],2)`, p < .001. This effect is, as expected, sizable: It means that there is nearly one whole standard deviation increase in this variable when there are any siblings. In addition to being a better predictor, in this model, the amount of variance allocated to individual children as measured by Child ICC was considerably higher in our original analysis (`r round(df.icc.mixed[df.icc.mixed$metric=="voc_dur_och_ph" & df.icc.mixed$data_set=="aclew","icc_child_id"],2)`) than in this re-analysis including sibling presence (`r round(icc.result.split["icc_child_id"],2)`).
+
+
+## SM K: Exploration: are "bad" output measures those coming from VCM?
+
+Among ACLEW measures, a fair number of them come from VCM, a module that classifies child vocalizations in terms of vocal maturity types into cry, canonical, and non-canonical categories. In unpublished analyses, we have found that VCM labels are inaccurate when compared to human labels of the same vocalizations, relatively to other metrics. In this analysis, we checked whether VCM-derived measures had lower Child ICC than other ACLEW measures. As shown in the next Figure, this was not the case: Some output measures from the ACLEW pipeline have lower Child ICC than VCM ones.
+
+```{r}
+vcm_type<-rep("Other ACLEW",dim(df.icc.mixed)[1])
+vcm_type[df.icc.mixed$data_set=="lena"]<-"LENA"
+vcm_type[grep("lp",df.icc.mixed$metric)]<-"ACLEW VCM"
+vcm_type[grep("cp",df.icc.mixed$metric)]<-"ACLEW VCM"
+vcm_type[grep("can",df.icc.mixed$metric)]<-"ACLEW VCM"
+vcm_type[grep("cry",df.icc.mixed$metric)]<-"ACLEW VCM"
+#cbind(df.icc.mixed[,c("metric","data_set","Type")],vcm_type)
+
+ggplot(df.icc.mixed, aes(y = icc_child_id, x = vcm_type)) +
+  geom_violin(alpha = 0.5) +
+  geom_quasirandom(aes(colour = Type,shape = Type)) +  
+  labs( y = "Child ICC",x="Type") +  theme(text = element_text(size = 20)) + 
+  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
+panel.background = element_blank(), legend.key=element_blank(), axis.line = element_line(colour = "black")) 
+
+```
+
+## SM L: Code to reproduce Figure 4
+
+```{r icc-allexp-fig4, echo=F,fig.width=4, fig.height=3,fig.cap="Distribution of ICC attributed to corpus (a) and children (b), when combining data from all corpora."}
 
 
-## Code to reproduce text before Table 3
+ggplot(df.icc.mixed, aes(y = icc_child_id, x = toupper(data_set))) +
+  geom_violin(alpha = 0.5) +
+  geom_quasirandom(aes(colour = Type,shape = Type)) +  
+  labs( y = "Child ICC",x="Pipeline") +  theme(text = element_text(size = 20)) + 
+  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
+panel.background = element_blank(), legend.key=element_blank(), axis.line = element_line(colour = "black")) 
+
+```
+
+
+## SM M: Code to reproduce text below Figure 4
 
 ```{r reg model icc}
 
@@ -386,25 +475,28 @@ msds = aggregate(df.icc.mixed$icc_child_id,by=list(df.icc.mixed$Type),get_msd)
 msds$x=gsub("0.",".",msds$x,fixed=T)
 rownames(msds)<-msds$Group.1
 
-```
+msds_p = aggregate(df.icc.mixed$icc_child_id,by=list(df.icc.mixed$data_set),get_msd)
+msds_p$x=gsub("0.",".",msds_p$x,fixed=T)
+rownames(msds_p)<-msds_p$Group.1
 
+```
 
-Next we explored how similar Child ICCs were across different talker types and pipelines. We fit a linear model with the formula $lm(icc\_child\_id ~ type * pipeline)$, where type indicates whether the measure pertained to the key child, (female/male) adults, other children; and pipeline LENA or ACLEW. We found an adjusted R-squared of `r round(reg_sum$adj.r.squared*100)`%, suggesting much of the variance across Child ICCs was explained by these factors. A Type 3 ANOVA on this model revealed type was a signficant predictor (F(`r reg_anova["Type","Df"]`) = `r round(reg_anova["Type","F value"],1)`, p<.001), whereas as was pipeline (F(`r reg_anova["data_set","Df"]`) = `r round(reg_anova["data_set","F value"],1)`, p = `r round(reg_anova["data_set","Pr(>F)"],3)`); the interaction between type and pipeline was not significant. The main effect of type emerged because output metrics tended to have higher Child ICC (`r msds["Output","x"]`)  than those associated to adults in general (`r msds["Adults","x"]`), females (`r msds["Female","x"]`), and males (`r msds["Male","x"]`); whereas those associated with other children had even higher Child ICCs (`r msds["Other children","x"]`).
 
+Next, we explored how similar Child ICCs were across different talker types and pipelines. We fit a linear model with the formula $lm(icc\_child\_id ~ type * pipeline)$, where type indicates whether the measure pertained to the key child, (female/male) adults, other children; and pipeline LENA or ACLEW. We found an adjusted R-squared of `r round(reg_sum$adj.r.squared*100)`%, suggesting much of the variance across Child ICCs was explained by these factors. A Type 3 ANOVA on this model revealed type was a signficant predictor (F(`r reg_anova["Type","Df"]`) = `r round(reg_anova["Type","F value"],1)`, p<.001), whereas as was pipeline (F(`r reg_anova["data_set","Df"]`) = `r round(reg_anova["data_set","F value"],1)`, p = `r round(reg_anova["data_set","Pr(>F)"],3)`); the interaction between type and pipeline was not significant. The main effect of type emerged because output metrics tended to have higher Child ICC (`r msds["Output","x"]`)  than those associated to adults in general (`r msds["Adults","x"]`), females (`r msds["Female","x"]`), and males (`r msds["Male","x"]`); whereas those associated with other children had even higher Child ICCs (`r msds["Other children","x"]`). The main effect of pipeline arose because of slightly higher Child ICCs for the ACLEW metrics (`r msds_p["aclew","x"]`) than for LENA metrics (`r msds_p["lena","x"]`). 
 
 
-## Code to reproduce Table 3
+## SM N: Code to reproduce Table 4
 
 
 ```{r tab3, results="as.is"}
-key_metrics = c("wc_adu_ph", "lena_CVC", "lena_CTC",  "voc_fem_ph",  "voc_chi_ph")
+key_metrics = c("wc_adu_ph", "lena_CVC_ph", "lena_CTC_ph",  "voc_fem_ph",  "voc_chi_ph")
 x <- merge(df.icc.mixed[df.icc.mixed$metric %in% key_metrics & df.icc.mixed$data_set=="lena",c("metric","icc_child_id")] ,
            df.icc.mixed[df.icc.mixed$metric %in% key_metrics & df.icc.mixed$data_set=="aclew",c("metric","icc_child_id")],
            by='metric', all=TRUE)
 colnames(x) <- c("metric", "LENA ICC", "ACLEW ICC")
 rownames(x)<-x$metric
-x["lena_CTC","ACLEW ICC"]<-df.icc.mixed[df.icc.mixed$metric=="simple_CTC","icc_child_id"]
-x["lena_CVC","ACLEW ICC"]<-df.icc.mixed[df.icc.mixed$metric=="can_voc_chi_ph","icc_child_id"]
+x["lena_CTC_ph","ACLEW ICC"]<-df.icc.mixed[df.icc.mixed$metric=="simple_CTC_ph","icc_child_id"]
+x["lena_CVC_ph","ACLEW ICC"]<-df.icc.mixed[df.icc.mixed$metric=="can_voc_chi_ph","icc_child_id"]
 x[,2:3]=round(x[,2:3],2)
 x=x[key_metrics,]
 
@@ -412,217 +504,123 @@ kable(x,row.names = F,digits=2,caption="Most commonly used metrics.")
 #x
 ```
 
-## Code to reproduce "Exploratory analyses: Correlations based on paired analyses of recordings done within two months"
-
-
-```{r compute correlation analysis for close recs, warning = F, echo=FALSE}
-#to compare with Gilkerson, we just need to focus on AWC, CVC, CTC (in LENA, but for comparison purposes, we also include aclew)
-
 
-mydat_lena <- read.csv(paste0('../data_output/', "lena",'_metrics_scaled.csv'))
-#table(mydat_lena$session_id)[table(mydat_lena$session_id)>1]
-mydat_lena=mydat_lena[order(mydat_lena$experiment,mydat_lena$child_id,mydat_lena$age),]
-# dim(mydat_lena) #1253 obs 
-key=mydat_lena[,c("experiment","child_id","age")]
 
-dist_contig_lena <- define_contiguous(mydat_lena)
-# dim(dist_contig_lena) #684
-# table(dist_contig_lena$session_id)[table(dist_contig_lena$session_id)>1] #0=no repeats
-mydat_lena = merge(mydat_lena,dist_contig_lena[,c("session_id","next_session")],by="session_id", all.x=T) #note that we need to do all.x=T, bc we need to keep others that are next session
+## SM O: Code to reproduce text at the beginning of the "Reliability across age groups" section
 
+Out of `r dim(df.icc.age)[1]` fitted models (`r dim(df.icc.mixed)[1]` metrics times `r length(levels(factor(df.icc.age$age_bin)))` age bins), `r sum(df.icc.age$formula=="no_chi_effect")` were singular when including a random intercept per child, and therefore they could not be included in these analyses at all. In addition, `r sum(df.icc.age$formula=="no_exp")` were singular when including a random intercept per corpus. The remaining `r sum(df.icc.age$formula=="full")` could be analyzed with the full model.
 
-#table(dist_contig_lena$experiment) #this is the number of eligible recordings per corpus
-
-#table(dist_contig_lena$experiment[!duplicated(dist_contig_lena$child_id)])#this is the number of eligible children per corpus
-#sum(table(dist_contig_lena$experiment[!duplicated(dist_contig_lena$child_id)])) #and overall
-# maximally, we'll have 148 rows in the samples below
-
-#given those two numbers, with 5 draws we'd cover many combinations in winni, lucid, & trio; but we'll do 10 because there are a lot of recs in cougar & bergelson. Later increased to 20 bc there was a lot of variability still in the average r
-
-mydat_aclew <- read.csv(paste0('../data_output/', "aclew",'_metrics_scaled.csv')) #1254
-#mydat_aclew=mydat_aclew[order(mydat_aclew$experiment,mydat_aclew$child_id,mydat_aclew$age),]
-#dim(mydat_aclew)
-# dim(dist_contig_aclew) #686 -- for some reason, we have 2 more eligible recs here... not sure why
-#length(dist_contig_aclew$session_id[!(dist_contig_aclew$session_id %in% dist_contig_lena$session_id)])  # in fact, we have lots of sessions not in common!
-#length(dist_contig_lena$session_id[!(dist_contig_lena$session_id %in% dist_contig_aclew$session_id)])
-# they are present in aclew but not in lena
-# NOTE: I have "winnipeg C175 C175_20151201" "winnipeg C175 C175_20160301" for lena but not aclew; and i have "fausey-trio T066 T066/T066_000700"   "quechua 1096 20190630_190025_009107" "quechua 1096 20190702_193551_008712" 
-
-#one thing that drove me crazy was that, probably because of the small differences in inclusion (2 recs in aclew & lena respectively), I was ending up with different lists of pairings across aclew & lena. So to simplify, I'll impose the same pairing across both, which involves losing a couple of additional recs in lena
-xxx=mydat_aclew[mydat_aclew$session_id %in% mydat_lena$session_id,]
-rownames(xxx)<-xxx$session_id
-xxx=xxx[mydat_lena$session_id,]
-
-dist_contig_aclew <- define_contiguous(xxx) 
-mydat_aclew = merge(mydat_aclew,dist_contig_aclew[,c("session_id","next_session")],by="session_id", all.x=T)
-
-# dist_contig_lena=dist_contig_lena[((dist_contig_lena$session_id %in% dist_contig_aclew$session_id) & (dist_contig_lena$next_session %in% dist_contig_aclew$next_session)),]
-# dist_contig_lena=dist_contig_lena[order(dist_contig_lena$session_id),]
-# 
-# dist_contig_aclew=dist_contig_aclew[((dist_contig_aclew$session_id %in% dist_contig_lena$session_id) & (dist_contig_aclew$next_session %in% dist_contig_aclew$next_session)),]
-# dist_contig_aclew=dist_contig_aclew[order(dist_contig_aclew$session_id),]
-
+## SM P: Code to reproduce Figure 5
 
-nsamples=20
 
-all_rs=data.frame(matrix(NA,nrow=dim(df.icc.mixed)[1],ncol=(nsamples)))
-colnames(all_rs[,1:nsamples])<-paste0("sample",1:nsamples)
-all_rs$data_set<-df.icc.mixed$data_set
-all_rs$metric<-df.icc.mixed$metric
+```{r relBYage-fig5, 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."}
 
-for(i in 1:nsamples){#i=1
-  
-    #for each child, sample 2 contiguous recordings that are less than 2 months away
-    #step 1: sample one session per child among the list of sessions that are close by
-      #we use the lena one because there is no difference across lena & aclew in terms of which kids have which sessions, since in this paper all recs have both lena and aclew, so it's just a question of using one of them
-    close_sessions <- dist_contig_lena  %>%
-    group_by(child_id)%>%
-    slice_sample(n = 1) 
-  #table(close_sessions$experiment) #these have to be similar
-    #dim(close_sessions)
-  
-  for(j in 1:dim(df.icc.mixed)[1]){# j=1
-    data_set=df.icc.mixed[j,"data_set"]
-    metric=df.icc.mixed[j,"metric"]
-    
-    if(data_set=="aclew") dat_for_cor<-mydat_aclew else  dat_for_cor<-mydat_lena
-
-    #step 2: get data from those sampled sessions as rec1
-    rec1 = subset(dat_for_cor,session_id %in% close_sessions$session_id)  
-    #step 3: get the next session
-    rec2 = subset(dat_for_cor,session_id %in% close_sessions$next_session)   
-
-     all_rs[all_rs$data_set==data_set & all_rs$metric==metric,i]<-
-      cor.test(rec1[,metric],rec2[,metric])$estimate
-    
+#this complicated section is just to add N of participants in each facet, we first estimate it:
+facet_labels_chi=facet_labels_cor=NULL
+for(thisage in levels(df.icc.age$age_bin)){#thisage="(0,6]"
+  facet_labels_cor = c(facet_labels_cor,paste0("N cor=",min(df.icc.age$ncor[df.icc.age$age_bin==thisage],na.rm=T))) #checked: there is no variation across metrics in n of corpora included
+    if(min(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T) !=max(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T)){
+    facet_labels_chi = c(facet_labels_chi,paste0("N chi=",paste(range(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T),collapse="-"))) 
+  } else {
+    facet_labels_chi = c(facet_labels_chi,paste0("N chi=",min(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T))) 
   }
 }
 
+#and then we structure it so that it goes on the plot
+f_labels<-data.frame(age_bin=levels(df.icc.age$age_bin),facet_labels_chi=facet_labels_chi,facet_labels_cor=facet_labels_cor)
 
+f_labels$age_bin<-factor(f_labels$age_bin,levels=age_levels)
+
+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$icc_child_id,na.rm=T),aes(label=facet_labels_chi),data=f_labels,size=3) +
+  geom_text(x=1.5,y=max(df.icc.age$icc_child_id,na.rm=T)*.95,aes(label=facet_labels_cor),data=f_labels,size=3)
 
 ```
 
-```{r}
+## SM Q: Code to reproduce text below Figure 5
 
+```{r reg model age}
 
-#summary(all_rs)
-rval_tab=cbind(apply(all_rs[,1:20],1,mean),apply(all_rs[,1:20],1,sd),all_rs[,21:22])
-colnames(rval_tab) <-c("m","sd","p","met") 
-rval_tab[,1:2]=round(rval_tab[,1:2],2)
 
-rval_tab$fin<-paste0(rval_tab$m," ","[",rval_tab$m-2*rval_tab$sd,",",rval_tab$m+2*rval_tab$sd,"]")
+age_icc <- lm(icc_child_id ~ Type * data_set * age_bin, data=df.icc.age) 
+#plot(age_icc)
+#binomial could be used,  diagnostic plots look good
 
-mytab=rbind(cbind(rval_tab[rval_tab$p=="aclew" & rval_tab$met== "wc_adu_ph","fin"],rval_tab[rval_tab$p=="lena" & rval_tab$met== "wc_adu_ph","fin"]),
-            cbind(rval_tab[rval_tab$met== "can_voc_chi_ph","fin"],rval_tab[rval_tab$met== "lena_CVC","fin"]),
-            cbind(rval_tab[rval_tab$met== "simple_CTC","fin"],rval_tab[rval_tab$met== "lena_CTC","fin"]),
-            cbind(rval_tab[rval_tab$p=="aclew" & rval_tab$met== "voc_chi_ph","fin"],rval_tab[rval_tab$p=="lena" & rval_tab$met== "voc_chi_ph","fin"])
-       )
-mytab=gsub("0.",".",mytab,fixed=T)
-colnames(mytab)<-c("aclew","lena")
-rownames(mytab)<-c("AWC","CVC","CTC","Chi vocs")
-print(mytab)
+reg_sum_age_icc=summary(age_icc)
 
-rval_tab$Type<-get_type(rval_tab)
+reg_anova_age_icc=Anova(age_icc)
 
 ```
 
-Out of our `r length(levels(factor(mydat_aclew$experiment)))` corpora and `r length(levels(factor(mydat_aclew$child_id)))` children, `r length(levels(factor(dist_contig_lena$child_id)))` children (belonging to `r length(levels(factor(gsub(" .*","",dist_contig_lena$child_id))))`  corpora) could be included in this analysis, as some children did not have recordings less than two months apart (in particular, no child from the Warlaumont corpus did). 
-
-## Code to reproduce Fig. 4
-
-```{r r-fig4, echo=F,fig.width=4, fig.height=3,fig.cap="Distribution of correlation coefficients."}
+As we did in the previous section for corpus, we checked whether Child ICC differed by talker types and pipelines across age bins by fitting a linear model with the formula $lm(Child_ICC ~ type * pipeline * age_bin)$. We found an adjusted R-squared of `r round(reg_sum_age_icc$adj.r.squared*100)`%, suggesting this model explained about a third of the variance in Child ICC.  A Type 3 ANOVA on this model revealed type was a signficant predictor (F(`r reg_anova["Type","Df"]`) = `r round(reg_anova["Type","F value"],1)`, p<.001), whereas as was pipeline (F(`r reg_anova["data_set","Df"]`) = `r round(reg_anova["data_set","F value"],1)`, p = `r round(reg_anova["data_set","Pr(>F)"],3)`); the interaction between type and pipeline was not significant. See below for more information.
 
 
-ggplot(rval_tab, aes(y = m, x = toupper(p))) +
-  geom_violin(alpha = 0.5) +
-  geom_quasirandom(aes(colour = Type,shape = Type)) +  
-  theme() +labs( y = "r",x="Pipeline")
-
+```{r print out anova results rec on icc by age}
+kable(round(reg_anova_age_icc,2))
 
 ```
 
-## Code to reproduce results of regression on correlation values
 
-```{r reg model cor}
 
 
-lr_cor <- lm(m ~ Type * p, data=rval_tab) 
-#plot(lr_cor)
-#binomial could be used, but diagnostic plots look great
+## SM R: Code to reproduce Figure 6
 
-reg_sum_cor=summary(lr_cor)
+```{r icc-bycor-fi68, echo=F,fig.width=4, fig.height=4,fig.cap="Correlations in Child ICC across corpora. Each point indicates the correlation in Child ICC for the corpus named in the x-axis with every other corpus."}
 
-reg_anova_cor=Anova(lr_icc_chi)
+r_X_age = NULL
 
-r_msds=aggregate(rval_tab$m,by=list(rval_tab$p),get_msd)
-rownames(r_msds)<-r_msds$Group.1
+for(ageA in levels(factor(df.icc.age$age_bin))){#ageA="(0,6]"
+  for(ageB in levels(factor(df.icc.age$age_bin))) if(ageA!=ageB){#ageB="(6,12]"
+    #check:
+    # print(sum(df.icc.age[df.icc.age$age_bin==ageA,c("metric","data_set")] == df.icc.age[df.icc.age$age_bin==ageB,c("metric","data_set")]))
+    #dim(df.icc.age[df.icc.age$age_bin==ageA,c("metric","data_set")])
+    #yes, the metric & data_set are matching across the two
+    r_X_age = rbind(r_X_age, cbind(ageA,ageB,cor.test(df.icc.age$icc_child_id[df.icc.age$age_bin==ageA],df.icc.age$icc_child_id[df.icc.age$age_bin==ageB])$estimate))
+  }
+}
+r_X_age=data.frame(r_X_age)
+colnames(r_X_age)[3]<-c("cor")
+r_X_age$cor=as.numeric(as.character(r_X_age$cor))
+r_X_age$ageA=factor(r_X_age$ageA,levels=age_levels)
+#r_X_age$ageB=factor(r_X_age$ageB,levels=age_levels)
 
-cor_t=t.test(rval_tab$m ~ rval_tab$p)
+#summary(r_X_age$cor) #mean correlation across corpora is zero!
 
+ggplot(r_X_age, aes(y = cor, x = ageA)) +
+  geom_violin(alpha = 0.5) +
+  geom_quasirandom() +  
+  theme() +labs( y = "r",x="Age")
 ```
 
-To see whether correlations in this analysis differed by talker types and pipelines, we fit a linear model with the formula $lm(cor ~ type * pipeline)$, where type indicates whether the measure pertained to the key child, (female/male) adults, other children; and pipeline LENA or ACLEW. We found an adjusted R-squared of `r round(reg_sum_cor$adj.r.squared*100)`%, suggesting this model did not explain a great deal of variance in correlation coefficients. A Type 3 ANOVA on this model revealed a significant effect of pipeline (F = `r round(reg_anova_cor["data_set","F value"],2)`, p = `r round(reg_anova_cor["data_set","Pr(>F)"],2)`), due to higher correlations for ACLEW (`r r_msds["aclew","x"]`) than for LENA metrics (m = `r r_msds["lena","x"]`). See below for fuller results.
 
-```{r print out anova results rec on cor}
-
-kable(round(reg_anova_cor,2))
-```
 
 
+## SM S: Code to reproduce text at the beginning of the "Reliability within corpus" section
 
 
 
-## Code to reproduce text and figures in "Exploratory analyses: Reliability within corpus"
+Figure 7 addresses this question, showing the distribution of ICC across our `r dim(df.icc.mixed)[1]` metrics in each of the `r length(levels(factor(df.icc.corpus$corpus)))` included corpora.  Out of `r dim(df.icc.corpus)[1]` fitted models (`r dim(df.icc.mixed)[1]` metrics times `r length(levels(factor(df.icc.corpus$corpus)))` corpora), `r sum(df.icc.corpus$formula=="no_chi_effect")` were singular when including a random intercept per child, and therefore they could not be included in these analyses at all. (Including a random intercept per corpus is not relevant here, since only data from one corpus is included in each model fit.)
 
-```{r read in icc by corpus}
-df.icc.corpus<-read.csv("../output/df.icc.corpus.csv")
-df.icc.corpus$Type <- get_type(df.icc.corpus)
 
-```
+## SM T: Code to reproduce Figure 7
 
-Figure 5 addresses this question, showing the distribution of ICC across our `r dim(df.icc.mixed)[1]` metrics in each of the `r length(levels(factor(df.icc.corpus$corpus)))` included corpora.  Out of `r dim(df.icc.corpus)[1]` fitted models (`r dim(df.icc.mixed)[1]` metrics times `r length(levels(factor(df.icc.corpus$corpus)))` corpora), `r sum(df.icc.corpus$formula=="no_chi_effect")` were singular when including a random intercept per child, and therefore they could not be included in these analyses at all. (Including a random intercept per corpus is not relevant here, since only data from one corpus is included in each model fit.)
 
-
-```{r icc-bycor-fig5, echo=F,fig.width=4, fig.height=10,fig.cap="Child ICC by metric type and pipeline, when considering each corpus separately."}
+```{r icc-bycor-fig7, echo=F,fig.width=4, fig.height=10,fig.cap="Child ICC by metric type and pipeline, when considering each corpus separately."}
 
 ggplot(df.icc.corpus, aes(y = icc_child_id, x = toupper(data_set))) +
   geom_violin(alpha = 0.5) +
   geom_quasirandom(aes(colour = Type,shape = Type)) +  
-  theme(legend.position = "top", axis.title.y=element_blank() ,axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +labs( y = "ICC child ID",x="Pipeline") +   
+  theme(legend.position = "top", axis.title.y=element_blank() ,axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +labs( y = "Child ICC",x="Pipeline") +   
   facet_grid(cols=vars(corpus)) 
 
 ```
 
+## SM U: Code to reproduce text below Figure 7
 
-```{r icc-bycor-fig6, echo=F,fig.width=4, fig.height=10,fig.cap="Correlations in Child ICC across corpora. Each point indicates the correlation in Child ICC for the corpus named in the x-axis with every other corpus."}
-
-
-
-r_X_corpus = NULL
-
-for(corpusA in levels(factor(df.icc.corpus$corpus))){
-  for(corpusB in levels(factor(df.icc.corpus$corpus))) if(corpusA!=corpusB){
-    #check:
-    # print(sum(df.icc.corpus[df.icc.corpus$corpus==corpusA,c("metric","data_set")] == df.icc.corpus[df.icc.corpus$corpus==corpusB,c("metric","data_set")]))
-    #yes, the metric & data_set are matching across the two
-    r_X_corpus = rbind(r_X_corpus, cbind(corpusA,corpusB,cor.test(df.icc.corpus$icc_child_id[df.icc.corpus$corpus==corpusA],df.icc.corpus$icc_child_id[df.icc.corpus$corpus==corpusB])$estimate))
-  }
-}
-r_X_corpus=data.frame(r_X_corpus)
-colnames(r_X_corpus)[3]<-c("cor")
-r_X_corpus$cor=as.numeric(as.character(r_X_corpus$cor))
-
-#summary(r_X_corpus$cor) #mean correlation across corpora is zero!
-
-ggplot(r_X_corpus, aes(y = cor, x = corpusA)) +
-  geom_violin(alpha = 0.5) +
-  geom_quasirandom() +  
-  theme() +labs( y = "r",x="Corpus")
-```
-
-
-
-```{r reg model corpusm}
+```{r reg model corpus}
 
 
 cor_icc <- lm(icc_child_id ~ Type * data_set * corpus, data=df.icc.corpus) 
@@ -642,104 +640,34 @@ kable(round(reg_anova_cor_icc,2))
 
 ```
 
+## SM V: Code to reproduce Figure 8
 
-
-## Code to reproduce text and figures in "Exploratory analyses: Reliability across age groups"
-
+```{r icc-bycor-fig8, echo=F,fig.width=4, fig.height=10,fig.cap="Correlations in Child ICC across corpora. Each point indicates the correlation in Child ICC for the corpus named in the x-axis with every other corpus."}
 
 
-```{r prepAge}
-df.icc.age<-read.csv("../output/df.icc.age.csv")
-age_levels=c("(0,6]" , "(6,12]",  "(12,18]" ,"(18,24]" ,"(24,30]", "(30,36]" )
-#not present in data: , "(36,42]", "(42,48]", "(48,54]"
-
-df.icc.age$age_bin<-factor(df.icc.age$age_bin,levels=age_levels)
 
-df.icc.age$Type<-get_type(df.icc.age)
-```
-
-Out of `r dim(df.icc.age)[1]` fitted models (`r dim(df.icc.mixed)[1]` metrics times `r length(levels(factor(df.icc.age$age_bin)))` age bins), `r sum(df.icc.age$formula=="no_chi_effect")` were singular when including a random intercept per child, and therefore they could not be included in these analyses at all. In addition, `r sum(df.icc.age$formula=="no_exp")` were singular when including a random intercept per corpus. The remaining `r sum(df.icc.age$formula=="full")` could be analyzed with the full model.
-
-
-```{r relBYage-fig7, echo=F,fig.width=6, fig.height=10,fig.cap="Distribution of ICC attributed to corpus (a) and children (b), when binning children's age."}
-
-#this complicated section is just to add N of participants in each facet, we first estimate it:
-facet_labels_chi=facet_labels_cor=NULL
-for(thisage in levels(df.icc.age$age_bin)){#thisage="(0,6]"
-  facet_labels_cor = c(facet_labels_cor,paste0("N cor=",min(df.icc.age$ncor[df.icc.age$age_bin==thisage],na.rm=T))) #checked: there is no variation across metrics in n of corpora included
-    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_chi = c(facet_labels_chi,paste0("N chi=",paste(range(df.icc.age$nchi[df.icc.age$age_bin==thisage],na.rm=T),collapse="-"))) 
-  } else {
-    facet_labels_chi = c(facet_labels_chi,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 on the plot
-f_labels<-data.frame(age_bin=levels(df.icc.age$age_bin),facet_labels_chi=facet_labels_chi,facet_labels_cor=facet_labels_cor)
-
-f_labels$age_bin<-factor(f_labels$age_bin,levels=age_levels)
-
-ggplot(df.icc.age, aes(y = icc_child_id, x = toupper(data_set))) +
-  geom_violin(alpha = 0.5) +
-  geom_quasirandom(aes(colour = Type,shape = Type)) +  
-  theme(legend.position="none") +labs( y = "r",x="Pipeline") + facet_wrap(~age_bin, ncol = 3) +
-  geom_text(x=1.5,y=max(df.icc.age$icc_child_id,na.rm=T),aes(label=facet_labels_chi),data=f_labels,size=3) +
-  geom_text(x=1.5,y=max(df.icc.age$icc_child_id,na.rm=T)*.95,aes(label=facet_labels_cor),data=f_labels,size=3)
-
-```
-
-
-
-
-```{r icc-bycor-fig8, echo=F,fig.width=4, fig.height=4,fig.cap="Correlations in Child ICC across corpora. Each point indicates the correlation in Child ICC for the corpus named in the x-axis with every other corpus."}
-
-r_X_age = NULL
+r_X_corpus = NULL
 
-for(ageA in levels(factor(df.icc.age$age_bin))){#ageA="(0,6]"
-  for(ageB in levels(factor(df.icc.age$age_bin))) if(ageA!=ageB){#ageB="(6,12]"
+for(corpusA in levels(factor(df.icc.corpus$corpus))){
+  for(corpusB in levels(factor(df.icc.corpus$corpus))) if(corpusA!=corpusB){
     #check:
-    # print(sum(df.icc.age[df.icc.age$age_bin==ageA,c("metric","data_set")] == df.icc.age[df.icc.age$age_bin==ageB,c("metric","data_set")]))
-    #dim(df.icc.age[df.icc.age$age_bin==ageA,c("metric","data_set")])
+    # print(sum(df.icc.corpus[df.icc.corpus$corpus==corpusA,c("metric","data_set")] == df.icc.corpus[df.icc.corpus$corpus==corpusB,c("metric","data_set")]))
     #yes, the metric & data_set are matching across the two
-    r_X_age = rbind(r_X_age, cbind(ageA,ageB,cor.test(df.icc.age$icc_child_id[df.icc.age$age_bin==ageA],df.icc.age$icc_child_id[df.icc.age$age_bin==ageB])$estimate))
+    r_X_corpus = rbind(r_X_corpus, cbind(corpusA,corpusB,cor.test(df.icc.corpus$icc_child_id[df.icc.corpus$corpus==corpusA],df.icc.corpus$icc_child_id[df.icc.corpus$corpus==corpusB])$estimate))
   }
 }
-r_X_age=data.frame(r_X_age)
-colnames(r_X_age)[3]<-c("cor")
-r_X_age$cor=as.numeric(as.character(r_X_age$cor))
-r_X_age$ageA=factor(r_X_age$ageA,levels=age_levels)
-#r_X_age$ageB=factor(r_X_age$ageB,levels=age_levels)
+r_X_corpus=data.frame(r_X_corpus)
+colnames(r_X_corpus)[3]<-c("cor")
+r_X_corpus$cor=as.numeric(as.character(r_X_corpus$cor))
 
-#summary(r_X_age$cor) #mean correlation across corpora is zero!
+#summary(r_X_corpus$cor) #mean correlation across corpora is zero!
 
-ggplot(r_X_age, aes(y = cor, x = ageA)) +
+ggplot(r_X_corpus, aes(y = cor, x = corpusA)) +
   geom_violin(alpha = 0.5) +
   geom_quasirandom() +  
-  theme() +labs( y = "r",x="Age")
-```
-
-
-
-```{r reg model age}
-
-
-age_icc <- lm(icc_child_id ~ Type * data_set * age_bin, data=df.icc.age) 
-#plot(age_icc)
-#binomial could be used,  diagnostic plots look good
-
-reg_sum_age_icc=summary(age_icc)
-
-reg_anova_age_icc=Anova(age_icc)
-
+  theme() +labs( y = "r",x="Corpus")
 ```
 
-As we did in the previous section for corpus, we checked whether Child ICC differed by talker types and pipelines across age bins by fitting a linear model with the formula $lm(Child_ICC ~ type * pipeline * age_bin)$. We found an adjusted R-squared of `r round(reg_sum_age_icc$adj.r.squared*100)`%, suggesting this model explained about a third of the variance in Child ICC. However, a Type 3 ANOVA on this model revealed only an interaction of type and age bin, as well as a main effect of age bin, suggesting less complex effects than in the case of corpus. See below for more information.
-
-
-```{r print out anova results rec on icc by age}
-kable(round(reg_anova_age_icc,2))
-
-```
 
 
 ## Save information about packages used

CODE/SM.html

@@ -1938,13 +1938,15 @@ where type indicates whether the measure pertained to the key child,
 found an adjusted R-squared of 65%, suggesting much of the variance
 across Child ICCs was explained by these factors. A Type 3 ANOVA on this
 model revealed type was a signficant predictor (F(4) = 31.2, p<.001),
-whereas as was pipeline (F(1) = 5.1, p = 0.027) nor the interaction
-between type and pipeline were significant. The main effect of type
-emerged because output metrics tended to have higher Child ICC (M = .4,
-SD = .06) than those associated to adults in general (M = .32, SD =
-.05), females (M = .36, SD = .06), and males (M = .36, SD = .04);
-whereas those associated with other children had even higher Child ICCs
-(M = .58, SD = .06).</p>
+whereas as was pipeline (F(1) = 5.1, p = 0.027); the interaction between
+type and pipeline was not significant. The main effect of type emerged
+because output metrics tended to have higher Child ICC (M = .4, SD =
+.06) than those associated to adults in general (M = .32, SD = .05),
+females (M = .36, SD = .06), and males (M = .36, SD = .04); whereas
+those associated with other children had even higher Child ICCs (M =
+.58, SD = .06). The main effect of pipeline arose because of slightly
+higher Child ICCs for the ACLEW metrics (M = .4, SD = .09) than for LENA
+metrics (M = .38, SD = .09).</p>
 </div>
 <div id="code-to-reproduce-table-3" class="section level2">
 <h2>Code to reproduce Table 3</h2>
@@ -2055,8 +2057,8 @@ adults, other children; and pipeline LENA or ACLEW. We found an adjusted
 R-squared of 32%, suggesting this model did not explain a great deal of
 variance in correlation coefficients. A Type 3 ANOVA on this model
 revealed a significant effect of pipeline (F = 5.13, p = 0.03), due to
-higher correlations for ACLEW (m = 0.59) than for LENA metrics (m =
-0.54). See below for fuller results.</p>
+higher correlations for ACLEW (M = 0.59, SD = 0.1) than for LENA metrics
+(m = M = 0.54, SD = 0.12). See below for fuller results.</p>
 <table>
 <thead>
 <tr>

CODE/create-all-icc.R

@@ -17,10 +17,12 @@ df.icc.mixed.cols = c("data_set","age_bin", "metric", "iqr",
 df.icc.mixed = data.frame(matrix(ncol=length(df.icc.mixed.cols),nrow=0, dimnames=list(NULL, df.icc.mixed.cols)),
                           stringsAsFactors = FALSE)
 
+
 for (data_set in data_sets){   # data_set = "aclew"
   # Load data
   mydat <- read.csv(paste0('../data_output/', data_set,'_metrics_scaled.csv'))
   metrics <- colnames(mydat)[!is.element(colnames(mydat), no.scale.columns)]
+  
   for(metric in metrics) 
   {  # metric = "voc_chi_ph"
     icc.row <- new_fit_models(mydat, data_set, metric, NA, TRUE)

CODE/create-all-rs.R

@@ -0,0 +1,86 @@
+
+mydat_lena <- read.csv(paste0('../data_output/', "lena",'_metrics_scaled.csv'))
+#table(mydat_lena$session_id)[table(mydat_lena$session_id)>1]
+mydat_lena=mydat_lena[order(mydat_lena$experiment,mydat_lena$child_id,mydat_lena$age),]
+# dim(mydat_lena) #1253 obs 
+key=mydat_lena[,c("experiment","child_id","age")]
+
+dist_contig_lena <- define_contiguous(mydat_lena)
+# dim(dist_contig_lena) #684
+# table(dist_contig_lena$session_id)[table(dist_contig_lena$session_id)>1] #0=no repeats
+mydat_lena = merge(mydat_lena,dist_contig_lena[,c("session_id","next_session")],by="session_id", all.x=T) #note that we need to do all.x=T, bc we need to keep others that are next session
+
+
+#table(dist_contig_lena$experiment) #this is the number of eligible recordings per corpus
+
+#table(dist_contig_lena$experiment[!duplicated(dist_contig_lena$child_id)])#this is the number of eligible children per corpus
+#sum(table(dist_contig_lena$experiment[!duplicated(dist_contig_lena$child_id)])) #and overall
+# maximally, we'll have 148 rows in the samples below
+
+#given those two numbers, with 5 draws we'd cover many combinations in winni, lucid, & trio; but we'll do 10 because there are a lot of recs in cougar & bergelson. Later increased to 20 bc there was a lot of variability still in the average r
+
+mydat_aclew <- read.csv(paste0('../data_output/', "aclew",'_metrics_scaled.csv')) #1254
+#mydat_aclew=mydat_aclew[order(mydat_aclew$experiment,mydat_aclew$child_id,mydat_aclew$age),]
+#dim(mydat_aclew)
+# dim(dist_contig_aclew) #686 -- for some reason, we have 2 more eligible recs here... not sure why
+#length(dist_contig_aclew$session_id[!(dist_contig_aclew$session_id %in% dist_contig_lena$session_id)])  # in fact, we have lots of sessions not in common!
+#length(dist_contig_lena$session_id[!(dist_contig_lena$session_id %in% dist_contig_aclew$session_id)])
+# they are present in aclew but not in lena
+# NOTE: I have "winnipeg C175 C175_20151201" "winnipeg C175 C175_20160301" for lena but not aclew; and i have "fausey-trio T066 T066/T066_000700"   "quechua 1096 20190630_190025_009107" "quechua 1096 20190702_193551_008712" 
+
+#one thing that drove me crazy was that, probably because of the small differences in inclusion (2 recs in aclew & lena respectively), I was ending up with different lists of pairings across aclew & lena. So to simplify, I'll impose the same pairing across both, which involves losing a couple of additional recs in lena
+xxx=mydat_aclew[mydat_aclew$session_id %in% mydat_lena$session_id,]
+rownames(xxx)<-xxx$session_id
+xxx=xxx[mydat_lena$session_id,]
+
+dist_contig_aclew <- define_contiguous(xxx) 
+mydat_aclew = merge(mydat_aclew,dist_contig_aclew[,c("session_id","next_session")],by="session_id", all.x=T)
+
+# dist_contig_lena=dist_contig_lena[((dist_contig_lena$session_id %in% dist_contig_aclew$session_id) & (dist_contig_lena$next_session %in% dist_contig_aclew$next_session)),]
+# dist_contig_lena=dist_contig_lena[order(dist_contig_lena$session_id),]
+# 
+# dist_contig_aclew=dist_contig_aclew[((dist_contig_aclew$session_id %in% dist_contig_lena$session_id) & (dist_contig_aclew$next_session %in% dist_contig_aclew$next_session)),]
+# dist_contig_aclew=dist_contig_aclew[order(dist_contig_aclew$session_id),]
+
+
+nsamples=20
+
+all_rs=data.frame(matrix(NA,nrow=dim(df.icc.mixed)[1],ncol=(nsamples)))
+colnames(all_rs[,1:nsamples])<-paste0("sample",1:nsamples)
+all_rs$data_set<-df.icc.mixed$data_set
+all_rs$metric<-df.icc.mixed$metric
+
+all_iccs=data.frame(matrix(NA,nrow=dim(df.icc.mixed)[1],ncol=(nsamples)))
+colnames(all_iccs[,1:nsamples])<-paste0("sample",1:nsamples)
+all_iccs$data_set<-df.icc.mixed$data_set
+all_iccs$metric<-df.icc.mixed$metric
+
+for(i in 1:nsamples){#i=1
+  
+  #for each child, sample 2 contiguous recordings that are less than 2 months away
+  #step 1: sample one session per child among the list of sessions that are close by
+  #we use the lena one because there is no difference across lena & aclew in terms of which kids have which sessions, since in this paper all recs have both lena and aclew, so it's just a question of using one of them
+  close_sessions <- dist_contig_lena  %>%
+    group_by(child_id)%>%
+    slice_sample(n = 1) 
+  #table(close_sessions$experiment) #these have to be similar
+  #dim(close_sessions)
+  
+  for(j in 1:dim(df.icc.mixed)[1]){# j=1
+    data_set=df.icc.mixed[j,"data_set"]
+    metric=df.icc.mixed[j,"metric"]
+    
+    if(data_set=="aclew") dat_for_cor<-mydat_aclew else  dat_for_cor<-mydat_lena
+    
+    #step 2: get data from those sampled sessions as rec1
+    rec1 = subset(dat_for_cor,session_id %in% close_sessions$session_id)  
+    #step 3: get the next session
+    rec2 = subset(dat_for_cor,session_id %in% close_sessions$next_session)   
+    
+    all_rs[all_rs$data_set==data_set & all_rs$metric==metric,i]<-
+      cor.test(rec1[,metric],rec2[,metric])$estimate
+    
+  }
+}
+
+write.csv(all_rs,'../data_output/all_rs.csv')

CODE/create-sib-subdataset.R

@@ -0,0 +1,34 @@
+mydat_aclew <- read.csv(paste0('../data_output/', 'aclew','_metrics_scaled.csv')) 
+mydat_aclew <- mydat_aclew[is.element(mydat_aclew$experiment, corpora),]
+
+
+read.csv("../input/aclew_md.csv")->x
+x$labname[x$labname=="ROW"]<-"luc"
+x$labname[x$labname=="SOD"]<-"win"
+x$ch_id=paste(tolower(x$labname),as.character(x$child_level_id))
+x$n_of_siblings<-x$number_older_sibs
+
+x$ch_id[x$labname %in% c("BER")] = paste(tolower(x$labname[x$labname %in% c("BER")]),as.numeric(as.character(x$child_level_id[x$labname %in% c("BER")])))
+x=x[!duplicated(x$ch_id),]
+
+mydat_aclew$lab=substr(mydat_aclew$experiment,1,3)
+mydat_aclew$ch_id=paste(mydat_aclew$lab,gsub(".* ","",mydat_aclew$child_id))
+mydat_aclew$ch_id[mydat_aclew$experiment=="warlaumont"]=gsub(" 0"," ",mydat_aclew$ch_id[mydat_aclew$experiment=="warlaumont"])
+mydat_aclew$ch_id[mydat_aclew$experiment=="winnipeg"]=gsub(" C"," CW",mydat_aclew$ch_id[mydat_aclew$experiment=="winnipeg"])
+
+#sort(factor(mydat_aclew$ch_id[mydat_aclew$experiment=="winnipeg"]))
+#sort(x$ch_id[x$lab=="win"])
+#sum(mydat_aclew$ch_id %in% x$ch_id)
+#sum(x$ch_id %in% mydat_aclew$ch_id)
+
+metadata=x[,c("ch_id","n_of_siblings")]
+read.csv("../input/quechua_md.csv")->x 
+x$ch_id=paste("que",x$child_id)
+metadata=rbind(metadata,x[,c("ch_id","n_of_siblings")])
+mydat2=merge(mydat_aclew,metadata,all.x=T,by="ch_id")
+#table(mydat2$n_of_siblings,mydat2$experiment)
+
+mydat2$sib_presence=ifelse(mydat2$n_of_siblings!=0,"present","absent")
+
+
+write.csv(mydat2[,c("age_s","n_of_siblings","sib_presence","experiment","child_id")],"../data_output/dat_sib_ana.csv")

CODE/regenerate_data.R

@@ -1,14 +1,21 @@
 # This code cannot be reproduced without access to the underlying datasets
 # It also relies on packages, functions, & variables that are called in SM.Rmd
 
+#contains ugly fix due to simple ctc & lena ctc not controlling for length
+
 remove_single_rec_kids = TRUE
 
 for (data_set in data_sets){ #data_set="aclew";data_set="lena"
   if(data_set=="aclew"){
     mydat <- read.csv(paste0('../input/el1000-metrics/output/', data_set,'_metrics.csv'))
+    #ugly fix for simple CTC & lena CTC not controlling for length
+    mydat$simple_CTC_ph = mydat$simple_CTC/(mydat$duration_vtc/(60*60*1000))
   } else{
     mydat <- read.csv(paste0('../input/el1000-metrics/output/', data_set,'_metrics_avass.csv'))
     mydat=mydat[,colnames(mydat)!="X"] #for some reason, this version has row names - remove that col to avoid having issues downastream
+    #ugly fix for simple CTC & lena CTC not controlling for length
+    mydat$lena_CTC_ph=mydat$lena_CTC/(mydat$duration_its/(60*60*1000))
+    mydat$lena_CVC_ph=mydat$lena_CVC/(mydat$duration_its/(60*60*1000))
   }
   
   # Remove Cougar non-normatives
@@ -19,9 +26,14 @@ for (data_set in data_sets){ #data_set="aclew";data_set="lena"
   
   if(data_set=="aclew"){
     mydat2 <- read.csv(paste0('../input/laac-metrics/output/', data_set,'_metrics.csv'))
+    #ugly fix for simple CTC & lena CTC not controlling for length
+    mydat2$simple_CTC_ph = mydat2$simple_CTC/(mydat2$duration_vtc/(60*60*1000))
   } else{
     mydat2 <- read.csv(paste0('../input/laac-metrics/output/', data_set,'_metrics_avass.csv'))
     mydat2=mydat2[,colnames(mydat2)!="X"] #for some reason, this version has row names - remove that col to avoid having issues downastream
+    #ugly fix for simple CTC & lena CTC not controlling for length
+    mydat2$lena_CTC_ph=mydat2$lena_CTC/(mydat2$duration_its/(60*60*1000))
+    mydat2$lena_CVC_ph=mydat2$lena_CVC/(mydat2$duration_its/(60*60*1000))
   }
   # Remove FauseyElse
   # if needed, in the terminal, do `datalad get input/el1000-metrics/EL1000/fausey-trio/metadata/recordings.csv`
@@ -34,8 +46,12 @@ for (data_set in data_sets){ #data_set="aclew";data_set="lena"
   #columns are not in the same order across el1000-metrics & laac-metrics
   mydat2=mydat2[,colnames(mydat)]
   
+  
   mydat=rbind(mydat,mydat2)
   
+  #ugly fix removing columns that we do not want to include
+  mydat=mydat[,!(colnames(mydat)%in%c("lena_CTC","simple_CTC","lena_CVC","voc_chi"))]
+  
   #remove no overlap metrics
   mydat = mydat[,grep("noov",colnames(mydat),invert=T)]
   mydat = mydat[,grep("no_overlap",colnames(mydat),invert=T)]

CODE/sessionInfo.txt

@@ -9,7 +9,7 @@ LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRla
 locale:
 [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
 
-time zone: Europe/Dublin
+time zone: Europe/Paris
 tzcode source: internal
 
 attached base packages:

OUTPUT/df.icc.age.csv

@@ -21,8 +21,6 @@
 "aclew","(0,6]","cp_dur","1.49247535245342","1.30373274664621","0.656538945133883","1.98576604831926","0.149183121134841","0.143197970685483","0.11207743385981","0.0371056872750314","0.111727521080077","0.0369898409915187","0.848160575193005","0.334256669462371","0.192327431718719","0.920956337289127","0.183317458760467","0.0401194880749842","130","63","6","full","0.668395278363138"
 "aclew","(0,6]","avg_can_voc_dur_chi","1.4730247029066","0.592366006393355","0.645865798181575","0.917165776638355","0.0743323680777528","0.0737370337173068","0.074332368077753",NA,"0.0745322782224193",NA,"0.928157130844398","0.273006004004343",NA,"0.963409119141187","0.0817461201468322","0.00800908642952538","122","61",NA,"no_exp","0.109768270159305"
 "aclew","(0,6]","can_voc_chi_ph","1.43017538407248","1.04513332678905","0.579030391272293","1.80497145321267","0.505419747485622","0.492634270881688","0.430189533687406","0.0752302137982145","0.447388271729528","0.0782378758602779","0.514353295644671","0.668870893169622","0.279710342783884","0.717184282904102","0.517931020103576","0.0252967492218883","127","62","6","full","0.00974474357672004"
-"aclew","(0,6]","voc_chi","1.56025431626062","-0.2259175609123","0.351394693844333","-0.642916825068455","0.800857631443877","0.799721253065946","0.499786911663312","0.301070719780567","0.439841196255219","0.264959530662766","0.175256725549928","0.663205244441884","0.514742198253423","0.418636746535618","0.801140204866258","0.00141895180031206","130","64","6","full","0.233257165463884"
-"aclew","(0,6]","simple_CTC","1.5674917532925","-0.136150667210233","0.339596314063276","-0.400919154808214","0.830486728658303","0.830092862516558","0.600309980325431","0.230176748332874","0.576278748597974","0.220962457451917","0.162727422633529","0.759130257991324","0.470066439401833","0.403394871848328","0.830567121921499","0.000474259404941835","129","63","6","full","0.022927807287034"
 "aclew","(0,6]","peak_voc_chi","1.47264563041752","-0.634395112192915","0.647540080566758","-0.979700147113152","0.298093165130105","0.295160635597787","0.067022715003012","0.231070450127093","0.0668120849124506","0.230344272594086","0.699700975256662","0.258480337574158","0.479941947108279","0.836481305981588","0.304998263142982","0.00983762754519519","128","64","6","full","0.134503054869378"
 "aclew","(0,6]","voc_fem_ph","1.50971740663039","-1.97474245310023","0.551383428372304","-3.5814323599273","0.539124264879389","0.493192673762422","0.472396667716825","0.0667275971625638","0.487054006828757","0.0687979949586853","0.475175607240312","0.697892546764011","0.262293718870059","0.689329824714057","0.578389341968856","0.0851966682064339","130","64","6","full","0.00310126272659958"
 "aclew","(0,6]","peak_voc_fem_ph","1.38262157869046","-1.49289888796614","0.657127938393447","-2.27185423224584","0.0669804243079954","0.063482631645807","0.0446454885503204","0.0223349357576749","0.0434007796157924","0.0217122413938681","0.90700714219283","0.208328537689373","0.147350742766598","0.952369225769517","0.11570374995501","0.0522211183092027","123","63","6","full","0.442972409125555"
@@ -41,6 +39,7 @@
 "aclew","(0,6]","avg_voc_dur_och","1.35718240243259","0.843761924161075","0.500055223177838","1.6873374880458","0.612530129466828","0.602338356546006","0.612530129466828",NA,"0.635177339339872",NA,"0.401795878439238","0.796980137355927",NA,"0.633873708588105","0.618977167325312","0.0166388107793055","128","61",NA,"no_exp","0.00024194537418672"
 "aclew","(0,6]","avg_voc_dur_chi","1.36471047599816","0.499505846248829","0.661757143634247","0.754817459930445","0.342921282112084","0.341019205658628","0.0832408537808605","0.259680428331224","0.0919810321189481","0.286946526063726","0.726071104626539","0.303283748524295","0.535673898994272","0.852098060452281","0.346565891013766","0.00554668535513863","127","63","6","full","0.143266989552209"
 "aclew","(0,6]","voc_dur_fem_ph","1.53364550225071","-1.86019176630539","0.56295004462424","-3.30436383133612","0.444721941867662","0.410008290320682","0.444721941867661",NA,"0.441135026465359",NA,"0.550799449744115","0.664179965420035",NA,"0.742158641898156","0.4880652699273","0.0780569796066178","130","64",NA,"no_exp","0.0172046544001357"
+"aclew","(0,6]","simple_CTC_ph","1.35010833513439","-0.197417883072709","0.491748335857651","-0.401461212326007","0.670628451660519","0.670029468857499","0.485314062078429","0.185314389582091","0.521364310145863","0.199079969928741","0.35383802675377","0.722055614302571","0.44618378492359","0.594842858874317","0.670922635262141","0.000893166404641247","128","62","6","full","0.088308016741458"
 "aclew","(6,12]","wc_adu_ph","1.42295855551096","-0.545076250935824","0.304114080845533","-1.7923413786706","0.483187143160126","0.480739783615116","0.329899358924752","0.153287784235373","0.342401760574875","0.159097026947497","0.536398835834254","0.585151057911438","0.398869686674103","0.732392542175474","0.485804818104659","0.00506503448954375","418","129","8","full","0.000370898387876723"
 "aclew","(6,12]","sc_fem_ph","1.39602920213464","-0.488889162520412","0.294740733781752","-1.65870918568867","0.530465843447215","0.528323272991253","0.412172360134887","0.11829348331233","0.434063137253143","0.124576137192578","0.494471460857086","0.65883468127683","0.352953449044741","0.703186647240323","0.532362308647231","0.00403903565597837","420","129","8","full","0.00674288338147925"
 "aclew","(6,12]","sc_mal_ph","1.33895635259617","-0.526064905540312","0.311550123527296","-1.68854019245549","0.477188089006927","0.474945632444857","0.37496888933565","0.102219199671278","0.396773899729125","0.108163401376102","0.553214217630389","0.629899912469532","0.328882047816693","0.743783716970457","0.479644946224329","0.00469931377947122","412","129","8","full","1.69305880623149e-07"
@@ -63,8 +62,6 @@
 "aclew","(6,12]","cp_dur","1.39904700131512","0.698389348940634","0.303077703811806","2.3043244031382","0.456065963737471","0.452147144745971","0.366173613224914","0.089892350512556","0.37322229597969","0.0916227391532443","0.554404529828063","0.61091922213963","0.302692482815884","0.744583460619468","0.460739803052206","0.00859265830623531","426","129","8","full","0.114219985485036"
 "aclew","(6,12]","avg_can_voc_dur_chi","1.37060950919718","-0.234406335412454","0.313003909139198","-0.748892676954426","0.428144097438557","0.427737148127648","0.428144097438557",NA,"0.445177923714566",NA,"0.594607341054742","0.667216549341041",NA,"0.771107866030909","0.428687644265149","0.00095049613750157","419","128",NA,"no_exp","0.601933897529673"
 "aclew","(6,12]","can_voc_chi_ph","1.37928155042673","1.24130423927665","0.296597721423448","4.18514421931267","0.495750263662524","0.482592915539735","0.366330261175405","0.129420002487123","0.371461418089558","0.131232777490916","0.511312719648672","0.609476347440619","0.362260648554208","0.715061339780492","0.509133190123928","0.0265402745841932","421","129","8","full","1.18140624849459e-05"
-"aclew","(6,12]","voc_chi","1.47412646390434","0.681305718395221","0.25691228545088","2.6519001113533","0.621557545559509","0.616423141794686","0.492581904346678","0.128975641212829","0.495825037868226","0.129824810095789","0.380934099787473","0.704148448743748","0.360312100956641","0.617198590234515","0.624683686295359","0.00826054450067255","427","128","8","full","0.00209051260529134"
-"aclew","(6,12]","simple_CTC","1.53265565704713","0.805397436411961","0.258894271814421","3.1109125388038","0.60723512468339","0.60008630218054","0.489566491045485","0.117668633637909","0.483482662279795","0.116206368897083","0.387883997457709","0.695329175484385","0.340890552666224","0.62280333770598","0.611859044257305","0.0117727420767652","425","127","8","full","0.0441932847052486"
 "aclew","(6,12]","peak_voc_chi","1.28674727669548","0.794782890817058","0.300560029863576","2.64433993827392","0.44807521385756","0.442936076639182","0.419515983818928","0.028559230038636","0.414495700357583","0.0282174661112487","0.545319986843605","0.643813404922252","0.167980552776947","0.738457843646884","0.45440543950381","0.0114693628646286","426","128","8","full","0.0118504037936313"
 "aclew","(6,12]","voc_fem_ph","1.36558374650489","-0.0437145418410082","0.286242241553367","-0.152718695898202","0.529957968657212","0.529940109188476","0.404307837564642","0.125650131092567","0.41485062320475","0.128926600838342","0.482298910675471","0.644088987023338","0.359063505300027","0.694477437124829","0.52997380897184","3.3699783363452e-05","423","127","8","full","0.877674584807725"
 "aclew","(6,12]","peak_voc_fem_ph","1.49087919984365","-0.147698335450341","0.333009709188416","-0.443525613142929","0.348755079407533","0.348627232787368","0.216410226974348","0.132344852433189","0.23378255664635","0.142968834668254","0.703523602685853","0.483510658255172","0.378112198518183","0.838763138606992","0.34899381273907","0.000366579951701287","423","127","8","full","0.291922639282524"
@@ -83,6 +80,7 @@
 "aclew","(6,12]","avg_voc_dur_och","1.58854561961483","0.419472085171305","0.243174672684229","1.72498262479829","0.654366629279535","0.652249494516557","0.651366770970105","0.00299985830942285","0.63923435172329","0.00294398266406753","0.339195570780156","0.799521326621929","0.0542584801120298","0.58240498862918","0.655484889887053","0.00323539537049573","424","129","8","full","0.000332484986780175"
 "aclew","(6,12]","avg_voc_dur_chi","1.55740545817867","0.0307948484959447","0.308183650541756","0.0999236930376107","0.454527709112742","0.45452019313769","0.447793150917955","0.00673455819478326","0.462303589565992","0.00695278706520416","0.563147956973035","0.679929106279465","0.0833833740334616","0.750431846987477","0.454536728928361","1.6535790671561e-05","422","129","8","full","0.677416519605636"
 "aclew","(6,12]","voc_dur_fem_ph","1.36841010946693","-0.558179506390883","0.297168936020881","-1.87832387148185","0.533247479155375","0.530525754437056","0.395735415348252","0.137512063807119","0.431532531265841","0.149951019476119","0.508973644973226","0.65691135723615","0.387235095873449","0.713423888703782","0.535629809917317","0.00510405548026042","421","129","8","full","5.82218160888712e-05"
+"aclew","(6,12]","simple_CTC_ph","1.49017116479248","1.00091278187782","0.277318847782271","3.60924902826532","0.542144677718745","0.532259685678796","0.48663452182975","0.0555101558889959","0.476972709090719","0.0544080377546364","0.448764901879478","0.690632108354889","0.233255305951733","0.669899172920431","0.550492811348392","0.0182331256695963","423","126","8","full","0.328782486083323"
 "aclew","(12,18]","wc_adu_ph","1.42151719731201","0.134772171258519","0.324971468731596","0.414720011527631","0.439152268892994","0.438997097693069","0.424505045396406","0.014647223496586","0.424324275675242","0.0146409861749427","0.560608901700696","0.651401777457847","0.120999942871651","0.748738206385046","0.439350440282681","0.000353342589611908","318","97","7","full","0.0865983789533906"
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@@ -105,8 +103,6 @@
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@@ -125,6 +121,7 @@
 "aclew","(12,18]","avg_voc_dur_och","1.66356761150842","0.268866860690081","0.222796416741377","1.20678269705829","0.747023468500106","0.745983329481664","0.733880886701367","0.0131425817987422","0.735750712192303","0.0131760672524573","0.253621080194074","0.857759122476877","0.114787051763068","0.503608062082086","0.747375707448759","0.00139237796709449","322","97","7","full","0.00559967041430653"
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@@ -147,8 +144,6 @@
 "aclew","(18,24]","cp_dur","1.47107387252174","1.59323696718432","0.604785301807024","2.63438440455468","0.368052414155344","0.347890456194641","0.314697326672477","0.053355087482868","0.292761813612363","0.0496360497945623","0.587898611329438","0.541074683950712","0.222791493990597","0.7667454671072","0.402670583114807","0.0547801269201657","97","53","5","full","0.747228484877589"
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@@ -167,6 +162,7 @@
 "aclew","(18,24]","avg_voc_dur_och","1.60444418159038","0.39376515545097","0.527590136365116","0.746346696630558","0.596259361758761","0.594284269873594","0.596259356753228","5.0055333706568e-09","0.598034589793415","5.02043626169357e-09","0.404942688511066","0.773326961765471","7.08550369535827e-05","0.636351073316503","0.597596740942125","0.00331247106853087","98","53","5","full","0.647455561602978"
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@@ -189,8 +185,6 @@
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@@ -209,6 +203,7 @@
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@@ -231,8 +226,6 @@
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@@ -251,9 +244,9 @@
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@@ -275,12 +268,12 @@
 "lena","(0,6]","peak_voc_mal_ph","1.37952452648307","-0.935849117069636","0.621118962387872","-1.50671477404553","0.369774950132164","0.362465840925768","0.369294399584552","0.000480550547612753","0.397892091544721","0.000517763775182357","0.679028881883643","0.630786882825508","0.022754423200388","0.82403208789685","0.382232212887373","0.0197663719616058","127","64","6","full","0.000420665751505312"
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@@ -302,12 +295,12 @@
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@@ -329,12 +322,12 @@
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@@ -356,12 +349,12 @@
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@@ -383,12 +376,12 @@
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+"lena","(24,30]","lena_CTC_ph","1.30893787656328","-0.500911022618703","0.549601879811028","-0.91140704029421","0.428884291848882","0.4263365607867","0.428884291848882",NA,"0.452529878685115",NA,"0.602602909541516","0.672703410638831",NA,"0.776275021845684","0.432276930043869","0.00594036925716818","93","39",NA,"no_exp","0.916309281429925"
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 "lena","(30,36]","peak_lena_CVC","1.40621708512048","0.496889850281877","0.78691852075027","0.631437483270985","0.435195612372277","0.432790311199631","0.386604912093404","0.0485907002788732","0.413675282990102","0.0519930581836315","0.604352421754798","0.643175934710015","0.228019863572522","0.777401068789333","0.438317253807198","0.00552694260756694","69","21","5","full","0.836524373282317"
-"lena","(30,36]","lena_CVC","1.5517592846906","0.602586160668071","0.671760377870216","0.897025458063694","0.545604733654347","0.540690707627036","0.545604733654347",NA,"0.531407632248157",NA,"0.442571513220211","0.728977113665551",NA,"0.665260485238835","0.549697275430064","0.00900656780302807","72","21",NA,"no_exp","0.216593035989655"
 "lena","(30,36]","lp_dur","1.60217362507511","0.189857551187878","0.657377732509243","0.288810438502659","0.648442622772876","0.647945508398724","0.544263679148575","0.104178943624302","0.607798437791708","0.116340299034277","0.392596516833646","0.779614287831943","0.341086937648273","0.626575228391329","0.648712136557784","0.000766628159059602","72","21","5","full","0.0210631934225797"
 "lena","(30,36]","lp_n","1.39178192829429","0.0863535801342969","0.544148278411542","0.158694943198896","0.749826396696267","0.749692062389549","0.749826396696267",NA,"0.768713871891495",NA,"0.256475258923913","0.876763292965379",NA,"0.506433864313903","0.749871216267446","0.000179153877897109","73","21",NA,"no_exp","0.513869713482082"
 "lena","(30,36]","peak_wc_adu_ph","1.49593779355642","1.06299532657133","0.784300911726991","1.35534118433022","0.311699270654767","0.303210023737521","0.212487935043096","0.0992113356116703","0.208558199305743","0.0973765287035765","0.675571347916564","0.456681726485463","0.312052124978467","0.821931473978557","0.330445400000715","0.0272353762631944","67","20","5","full","0.359980818245339"
@@ -410,6 +403,7 @@
 "lena","(30,36]","peak_voc_mal_ph","1.25688400896596","0.205173404042431","0.704256970374493","0.291333153484202","0.625092625180172","0.624484860411776","0.545621989603243","0.0794706355769291","0.588627865592978","0.0857345039025661","0.404457540275701","0.76722087145292","0.292804548978608","0.63596976364895","0.625457139949103","0.000972279537326837","67","20","5","full","0.221878003516201"
 "lena","(30,36]","voc_mal_ph","1.09296694387303","0.0565966791240339","0.644931623062613","0.0877560924292578","0.705938694511854","0.705887067398853","0.591022875854211","0.114915818657643","0.646026522093237","0.12561047921131","0.321428171781229","0.803757750875994","0.354415687027691","0.566946357057905","0.705960199971699","7.31325728460838e-05","67","20","5","full","0.147183367736302"
 "lena","(30,36]","peak_voc_fem_ph","1.49763718948152","-0.141681141484461","0.773773039653829","-0.183104262133308","0.395671314834752","0.395480591386486","0.395671314834751",NA,"0.401942868475752",NA,"0.613907544242744","0.633989643823739",NA,"0.783522523124093","0.395962616342845","0.000482024956358634","70","21",NA,"no_exp","0.049432088093319"
-"lena","(30,36]","lena_CTC","1.45320312602103","-0.237856880089995","0.601485414760391","-0.395449123541505","0.685626523285808","0.68468738540734","0.600162508387231","0.0854640148985759","0.617958005881204","0.0879981196480367","0.32369500603143","0.786103050421002","0.296644770134308","0.568942005859499","0.686057136790304","0.0013697513829647","72","21","5","full","0.867057377530462"
 "lena","(30,36]","peak_lena_CTC","1.21770895709078","-0.754902361516866","0.74553386400277","-1.01256615958904","0.381333623521543","0.376533332182822","0.102472433527701","0.278861189993843","0.111619099223581","0.30375227526059","0.673888394031115","0.334094446562018","0.551137256280675","0.820907055659235","0.389121499190995","0.0125881670081728","70","21","5","full","0.0576694042353197"
 "lena","(30,36]","standardScore","1.49701035820184","1.50028030374938","0.645773797359849","2.32322883009972","0.60405474405542","0.571030938406312","0.604054744055421",NA,"0.589574492175367",NA,"0.386453753571305","0.767837542827496",NA,"0.621654046533364","0.625701158240027","0.0546702198337147","71","20",NA,"no_exp","0.0649162349995381"
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OUTPUT/df.icc.corpus.csv

@@ -21,8 +21,6 @@
 "aclew","bergelson","cp_dur","1.18502697546257","0.284595609154949","0.0930318454104069","3.05912032486795","0.355719630107628","0.351647407733605","0.355719630107631",NA,"0.232259100892522",NA,"0.420668320690166","0.481932672572137",NA,"0.648589485491529","0.363095250442738","0.0114478427091327","522","44",NA,"no_exp","0.538218530311069"
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@@ -41,6 +39,7 @@
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@@ -63,8 +62,6 @@
 "aclew","cougar","cp_dur","1.95495129651426","0.391304563746481","0.10731658932791","3.64626351058207","0.654337803019238","0.525249005764223","0.654337803019238",NA,"0.66150490252097",NA,"0.349448307684311","0.813329516568143",NA,"0.591141529317905","0.722530588242532","0.197281582478309","233","26",NA,"no_exp","0.000182435698517968"
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@@ -83,6 +80,7 @@
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 "aclew","fausey-trio","wc_adu_ph","1.12375755259806","-0.7802715991219","0.681285811687978","-1.14529260074956","0.327631710351316","0.319426848148431","0.327631710351316",NA,"0.180272543096004",NA,"0.369956685029379","0.424585142340148",NA,"0.608240647301197","0.344469791012806","0.0250429428643755","84","28",NA,"no_exp","0.144703436298957"
 "aclew","fausey-trio","sc_fem_ph","1.00966486396315","-0.329901350827632","0.590772305351501","-0.558423859478899","0.242742896136723","0.241411679034484","0.242742896136723",NA,"0.111218245049679",NA,"0.346954771832497","0.333493995522677",NA,"0.589028668090524","0.246895741074716","0.00548406204023147","84","28",NA,"no_exp","0.240770526323518"
 "aclew","fausey-trio","sc_mal_ph","0.892278022290407","-0.893307350839171","0.699586324475407","-1.27690796630284","0.395607780766179","0.382474218481065","0.395607780766179",NA,"0.213421516169758",NA,"0.326056033428538","0.461975666209551",NA,"0.57101316397132","0.415672661291462","0.0331984428103974","84","28",NA,"no_exp","0.0430018976946938"
@@ -105,8 +103,6 @@
 "aclew","fausey-trio","cp_dur","0.704731963141091","-0.568739021112277","0.727043234407532","-0.782262999222794","0.666450989082656","0.655960446049933","0.666450989082656",NA,"0.312914641786232",NA,"0.156609219551189","0.5593877383231",NA,"0.395738827449606","0.671701353040203","0.0157409069902701","84","28",NA,"no_exp","0.900431320256573"
 "aclew","fausey-trio","avg_can_voc_dur_chi","1.28646697791179","-0.0163804199484597","1.08911833713833","-0.0150400735988887","0.657681496836175","0.65767752339672","0.657681496836175",NA,"0.690458454300225",NA,"0.359378674494983","0.830938297528899",NA,"0.59948200514693","0.65768356498292","6.04158619944677e-06","81","28",NA,"no_exp","0.0625467938527279"
 "aclew","fausey-trio","can_voc_chi_ph","0.464887607213079","0.325532336470312","0.361671730217579","0.900076807978534","0.561722416866011","0.550832937762677","0.561722416866011",NA,"0.0698996103816925",NA,"0.0545383829810787","0.264385344491128",NA,"0.233534543442889","0.570218810172913","0.0193858724102359","84","28",NA,"no_exp","0.00632655225762406"
-"aclew","fausey-trio","voc_chi","0.826480373006574","0.637203917922525","0.63039782701915","1.01079650121187","0.433701223478447","0.424223690813402","0.433701223478447",NA,"0.182978087176587",NA,"0.238920854470462","0.427759379998367",NA,"0.48879530937854","0.4460763676148","0.0218526768013977","84","28",NA,"no_exp","0.205646653766118"
-"aclew","fausey-trio","simple_CTC","0.672002144730323","0.762298405790837","0.486668792026473","1.56635974667011","0.412090064833244","0.391522685741941","0.412090064833244",NA,"0.105821052893767",NA,"0.150970027319702","0.325301480005497",NA,"0.388548616417176","0.441432598274352","0.0499099125324111","84","28",NA,"no_exp","0.495672807028776"
 "aclew","fausey-trio","peak_voc_chi","1.056301000952","1.57461958088359","0.69051067749432","2.28036963396057","0.440447537481759","0.395219376152656","0.440447537481758",NA,"0.221527022476784",NA,"0.281431908213024","0.470666572508378",NA,"0.530501562875194","0.497906205979638","0.102686829826982","84","28",NA,"no_exp","0.916946775512316"
 "aclew","fausey-trio","voc_fem_ph","1.07852991517852","0.107223930526101","0.61846136359693","0.173372075989507","0.236801408765964","0.236676568447259","0.236801408765965",NA,"0.119817445481889",NA,"0.386165378295596","0.346146566474217",NA,"0.621422061320321","0.237203762602716","0.000527194155457842","84","28",NA,"no_exp","0.466199660658598"
 "aclew","fausey-trio","peak_voc_fem_ph","1.07085376923572","0.088750236327799","0.820337789072234","0.10818742902015","0.486782138441897","0.486651384350927","0.486782138441897",NA,"0.33127470878782",NA,"0.349265275378755","0.57556468688395",NA,"0.590986696448198","0.486919993407514","0.000268609056587029","84","28",NA,"no_exp","0.610946081012489"
@@ -125,6 +121,7 @@
 "aclew","fausey-trio","avg_voc_dur_och","1.48206891067826","1.75919284794745","1.02140342577304","1.72232910479621","0.685581864962731","0.635801333258539","0.685581864962731",NA,"0.629071570614187",NA,"0.288501665732115","0.793140322146206",NA,"0.53712351068643","0.708411964093849","0.0726106308353094","84","28",NA,"no_exp","0.0199664225144248"
 "aclew","fausey-trio","avg_voc_dur_chi","1.47191697526344","-0.023546961085671","1.00093935198654","-0.0235248629589174","0.592489209100982","0.592481026800432","0.592489209100982",NA,"0.552206010739664",NA,"0.379804230556643","0.743105652474575",NA,"0.616282589853585","0.592494836841905","1.38100414727298e-05","84","28",NA,"no_exp","0.0315408948321885"
 "aclew","fausey-trio","voc_dur_fem_ph","1.12732218149292","-0.544681975110106","0.625353541345261","-0.870998465825245","0.239553957918097","0.23639562409141","0.239553957918097",NA,"0.123487412904666",NA,"0.392001514841984","0.351407758742839",NA,"0.626100243445076","0.249579851370038","0.0131842272786282","84","28",NA,"no_exp","0.275539715521177"
+"aclew","fausey-trio","simple_CTC_ph","0.770202543800538","1.1437917323401","0.448310680105935","2.55133723798377","0.283115899497122","0.25255048789619","0.283115899497122",NA,"0.0710441073578979",NA,"0.179892373016706","0.26654100502155",NA,"0.424137210129819","0.360511261753199","0.107960773857009","84","28",NA,"no_exp","0.499236392751216"
 "aclew","lucid","wc_adu_ph","1.17063990502602","-0.0105334375796715","0.0837494595808636","-0.125773200596011","0.24410746520945","0.244094631740752","0.244107465209449",NA,"0.152192179645338",NA,"0.471271668601803","0.390118161132417",NA,"0.686492293184565","0.244147204769141","5.25730283894597e-05","231","35",NA,"no_exp","0.00791096911992592"
 "aclew","lucid","sc_fem_ph","1.2058856318724","-0.0382779463168108","0.0826163660311725","-0.463321592992457","0.343382215045302","0.343170035673393","0.343382215045301",NA,"0.23936132010081",NA,"0.457708328859408","0.489245664365879",NA,"0.676541446520025","0.343787945869705","0.00061791019631243","232","35",NA,"no_exp","0.00723993124948715"
 "aclew","lucid","sc_mal_ph","1.11931900645209","0.0515076801170517","0.0881881883704311","0.584065520211115","0.305857417012861","0.305541707681616","0.305857417012861",NA,"0.230327495748263",NA,"0.522727630387775","0.479924468795104",NA,"0.722999052826333","0.306573918488626","0.00103221080700911","233","35",NA,"no_exp","0.00106864442252252"
@@ -147,8 +144,6 @@
 "aclew","lucid","cp_dur","1.36548077117115","0.800503382435534","0.0777373038480027","10.2975449727551","0.407734380684215","0.319349661690653","0.407734380684215",NA,"0.274017194001822",NA,"0.398031097687479","0.523466516600462",NA,"0.630897057916328","0.536119998411447","0.216770336720794","232","35",NA,"no_exp","0.452966734484049"
 "aclew","lucid","avg_can_voc_dur_chi","1.37840899483821","0.463719637811064","0.0892831822930552","5.19380723111987","0.345949699130802","0.321304160728958","0.345949699130802",NA,"0.283676617250763",NA,"0.536317208335731","0.532613008901175",NA,"0.732336813451114","0.3925443972829","0.071240236553942","234","35",NA,"no_exp","0.0348928161468888"
 "aclew","lucid","can_voc_chi_ph","1.40120920630839","0.954079392262859","0.0752423045947578","12.6800926340756","0.356702932294251","0.245061074848214","0.35670293229425",NA,"0.207043655582507",NA,"0.373393556556004","0.455020500178296",NA,"0.611059372365733","0.558043804561078","0.312982729712864","232","35",NA,"no_exp","0.0223835129929461"
-"aclew","lucid","voc_chi","1.13366354054601","0.651957946506402","0.0765045228606353","8.52182226786831","0.304931066433748","0.249071365960334","0.304931066433748",NA,"0.165953723291449",NA,"0.378279848027875","0.407374180933756",NA,"0.615044590276083","0.43225932751082","0.183187961550486","230","35",NA,"no_exp","0.509635169810244"
-"aclew","lucid","simple_CTC","1.27903552495395","0.867411076651309","0.074247109456808","11.6827588709822","0.308574487832083","0.217420650863266","0.308574487832082",NA,"0.158298139384924",NA,"0.354700004100825","0.397866987050854",NA,"0.595566960215915","0.512823675297418","0.295403024434152","230","35",NA,"no_exp","0.65031188542"
 "aclew","lucid","peak_voc_chi","1.0165589830944","0.669041193506716","0.0684378472521982","9.77589477707055","0.279805621236077","0.215632984323748","0.279805621236079",NA,"0.122583651672795",NA,"0.315519239653211","0.350119481995497",NA,"0.561710992996586","0.444980188389335","0.229347204065587","235","35",NA,"no_exp","0.162533786105045"
 "aclew","lucid","voc_fem_ph","1.12291605957702","0.0431951640345368","0.0764793472614779","0.564795145110946","0.306128463362902","0.305831723571528","0.306128463362902",NA,"0.173327122514",NA,"0.392863687089118","0.416325740873657",NA,"0.626788390997407","0.306801054512254","0.000969330940725772","232","35",NA,"no_exp","0.434300020013938"
 "aclew","lucid","peak_voc_fem_ph","0.939121757940849","-0.0488213174338627","0.0768984487058063","-0.634880394280001","0.165034173844561","0.164795065878227","0.165034173844561",NA,"0.0789674520494098",NA,"0.399524064039772","0.28101148028045",NA,"0.632079159630953","0.166243904992964","0.00144883911473771","235","35",NA,"no_exp","0.0262391520186003"
@@ -167,6 +162,7 @@
 "aclew","lucid","avg_voc_dur_och","1.35325765675588","0.369181485811186","0.06851253074798","5.38852501550704","0.589313298756459","0.560175270088086","0.589313298756462",NA,"0.44998064350512",NA,"0.313587130130138","0.670805965615334",NA,"0.559988508926869","0.609619307291826","0.0494440372037404","234","35",NA,"no_exp","0.276522643197331"
 "aclew","lucid","avg_voc_dur_chi","1.52937315484221","0.708225481427128","0.0845127591815481","8.38010128039645","0.44734003714998","0.381784761429885","0.447340037149981",NA,"0.381575221707622",NA,"0.471411745742578","0.617717752462742",NA,"0.686594309430669","0.528329381352015","0.146544619922129","231","35",NA,"no_exp","0.374567993666282"
 "aclew","lucid","voc_dur_fem_ph","1.29556450231222","-0.0997201664703509","0.0867439427440311","-1.14959227487054","0.373856025777371","0.37249151013829","0.373856025777371",NA,"0.301001085161508",NA,"0.504124589984765","0.548635657938407",NA,"0.710017316679506","0.37614135271136","0.00364984257307058","230","35",NA,"no_exp","0.0332588945249874"
+"aclew","lucid","simple_CTC_ph","1.22964300015822","0.736725418207367","0.069941123996449","10.5335084155178","0.359131649987017","0.274588584132291","0.359131649987019",NA,"0.184405796212354",NA,"0.329071075622863","0.429424959931714",NA,"0.573647169977211","0.509998261496519","0.235409677364228","235","35",NA,"no_exp","0.564808978648138"
 "aclew","lyon","wc_adu_ph","0.784238126254885",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
 "aclew","lyon","sc_fem_ph","1.13013860346984","0.251288933262933","0.13510685200997","1.85992737988144","0.0918135872209948","0.0848478003487453","0.0918135872209949",NA,"0.0793035728217101",NA,"0.784441927403946","0.281608900466072",NA,"0.885687262753589","0.160716602375649","0.0758688020269041","54","16",NA,"no_exp","0.185028371477049"
 "aclew","lyon","sc_mal_ph","1.37521655744894","0.0159451656908636","0.143118924776453","0.111412000305127","0.144938593468635","0.144891091279612","0.144938593468635",NA,"0.126223100089299",NA,"0.744649847333183","0.355278904649993",NA,"0.862930963248615","0.145218831378963","0.000327740099351981","54","16",NA,"no_exp","0.0236787333431287"
@@ -189,8 +185,6 @@
 "aclew","lyon","cp_dur","1.576180188068","0.549920557405478","0.162473693281803","3.3846744435831","0.295930940391162","0.213612633865957","0.295930940391162",NA,"0.260789113946074",NA,"0.620460793891756","0.510675155011553",NA,"0.787693337468177","0.49177991984287","0.278167285976913","54","16",NA,"no_exp","0.707038209238824"
 "aclew","lyon","avg_can_voc_dur_chi","1.70462175832035","0.0160464016424373","0.275103935381622","0.058328506352255","0.727991582722126","0.727869590425897","0.727991582722126",NA,"1.22210261492808",NA,"0.456629178039031","1.10548750102752",NA,"0.675743426190023","0.728037164198898","0.00016757377300053","50","16",NA,"no_exp","0.617853650978208"
 "aclew","lyon","can_voc_chi_ph","1.1083322477253","0.624408741358823","0.142479099838728","4.38245849437278","0.400303016404477","0.228779081606632","0.400303016404476",NA,"0.238260562167818",NA,"0.356939954450506","0.48811941384032",NA,"0.597444519976965","0.657264323457851","0.428485241851219","53","16",NA,"no_exp","0.0118520069554767"
-"aclew","lyon","voc_chi","1.45852287791276","0.360257396223381","0.15650400426573","2.30190529573733","0.48645598675142","0.398141387916234","0.48645598675142",NA,"0.31962655712578",NA,"0.337424781188019","0.565355248605494",NA,"0.580882760277854","0.579688334074623","0.181546946158389","54","16",NA,"no_exp","0.0296651460486513"
-"aclew","lyon","simple_CTC","1.3287988339378","0.491482701780092","0.123949527663574","3.96518414425977","0.383462239046216","0.241079369206092","0.383462239046216",NA,"0.17612055651091",NA,"0.283169925255884","0.419667197325346",NA,"0.532137130123321","0.612388080578227","0.371308711372134","54","16",NA,"no_exp","0.898571188865062"
 "aclew","lyon","peak_voc_chi","1.4725463458816","0.563524281449565","0.156764177068456","3.5947261165635","0.41258874610548","0.27476116317035","0.41258874610548",NA,"0.293312464449185",NA,"0.417595109297867","0.541583294100903",NA,"0.646215992759284","0.608816767536964","0.334055604366614","54","16",NA,"no_exp","0.0325202008250231"
 "aclew","lyon","voc_fem_ph","1.25989464374552","0.401786879169795","0.0973124714613074","4.12883233912675","0.0041749477741808","0.00314957320526611","0.0041749477741808",NA,"0.00232477207222055",NA,"0.554513827585883","0.0482158902460647",NA,"0.744656852238588","0.248751344622847","0.245601771417581","54","16",NA,"no_exp","0.663669558061325"
 "aclew","lyon","peak_voc_fem_ph","1.14096919460234",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
@@ -209,6 +203,7 @@
 "aclew","lyon","avg_voc_dur_och","1.76078118075764","0.267055680351869","0.21701757548279","1.2305716703256","0.433493161022902","0.408617771241401","0.433493161022903",NA,"0.554567717644017",NA,"0.724732090305474","0.744693035850354",NA,"0.851311981770181","0.466001351925358","0.0573835806839565","50","16",NA,"no_exp","0.392743266479363"
 "aclew","lyon","avg_voc_dur_chi","2.10841548259604","-0.117218442515234","0.296101365614451","-0.395872684585528","0.821199360998994","0.814968669079064","0.821199360998993",NA,"1.63031238612027",NA,"0.354969706813882","1.27683686746596",NA,"0.59579334237123","0.822555976395463","0.00758730731639981","52","16",NA,"no_exp","0.691881412462411"
 "aclew","lyon","voc_dur_fem_ph","1.12421635809737",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
+"aclew","lyon","simple_CTC_ph","1.54797651639597","0.503238569244211","0.160044915722019","3.14435836323774","0.544049240669017","0.3789751145296","0.544049240669018",NA,"0.355210994326186",NA,"0.297691294241266","0.595995800594422",NA,"0.545610936695065","0.682392735214863","0.303417620685263","54","16",NA,"no_exp","0.647060202780056"
 "aclew","warlaumont","wc_adu_ph","1.60251500754507","-0.417967959615516","0.401717309656863","-1.04045294929545","0.59972892106284","0.591201367819991","0.599728921062839",NA,"0.735505563749938",NA,"0.490891126352108","0.857616210055487",NA,"0.700636229688494","0.605420380682896","0.0142190128629059","36","13",NA,"no_exp","0.903645295434216"
 "aclew","warlaumont","sc_fem_ph","1.57866487329289","-0.58721767655617","0.421920458256918","-1.39177341383763","0.599714718139425","0.585485033670358","0.599714718139425",NA,"0.837310921765388",NA,"0.558871123529564","0.915046950579799",NA,"0.747576834532454","0.609212456124177","0.0237274224538188","38","13",NA,"no_exp","0.0896868871941703"
 "aclew","warlaumont","sc_mal_ph","1.2521314930513","-0.358286989391099","0.391588974182786","-0.914956786356956","0.449905912953081","0.443623315074205","0.449905912953081",NA,"0.401312009245185",NA,"0.490678955290185","0.633491917269025",NA,"0.700484800184976","0.457587563371451","0.013964248297245","38","13",NA,"no_exp","0.157941419463455"
@@ -231,8 +226,6 @@
 "aclew","warlaumont","cp_dur","1.23020999829778","0.0824065796187924","0.442226887377321","0.186344571013116","0.00647313551208899","0.0064670834451202","0.00647313551208899",NA,"0.00462238905249058",NA,"0.709465713051656","0.0679881537658626",NA,"0.842297876675263","0.00740203482339552","0.000934951378275318","38","13",NA,"no_exp","0.0164171398515591"
 "aclew","warlaumont","avg_can_voc_dur_chi","1.88168889892545",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
 "aclew","warlaumont","can_voc_chi_ph","0.784701303476259","0.161369374313852","0.263259262084101","0.612967509809023","0.015993419513051","0.0158337993462231","0.015993419513051",NA,"0.00406540873631619",NA,"0.250127181722449","0.0637605578419464",NA,"0.500127165551371","0.025814164501883","0.00998036515565991","38","13",NA,"no_exp","0.0187500670111881"
-"aclew","warlaumont","voc_chi","0.82695810732623","0.0496627127059261","0.25364507831281","0.195796082605944","0.583361649466287","0.5830702646882","0.583361649466287",NA,"0.283319237834181",NA,"0.202347308969826","0.532277406841753",NA,"0.449830311306192","0.583569757204298","0.000499492516098072","38","13",NA,"no_exp","0.184782441069022"
-"aclew","warlaumont","simple_CTC","0.673976879213808","0.138294364590589","0.196162859427363","0.704997699331548","0.649812631869415","0.646268129037938","0.649812631869415",NA,"0.222986108059001",NA,"0.120168360048987","0.472214048985205",NA,"0.346653083137864","0.651722782668294","0.00545465363035638","38","13",NA,"no_exp","0.00405295739718954"
 "aclew","warlaumont","peak_voc_chi","0.617047119368001","0.0767791946027019","0.392654440726269","0.19553884189031","0.16135617389147","0.161193887823362","0.16135617389147",NA,"0.0937265293696915",NA,"0.487140797298161","0.306147888069951",NA,"0.697954724389885","0.162199650814605","0.00100576299124282","36","13",NA,"no_exp","0.00431603717538058"
 "aclew","warlaumont","voc_fem_ph","1.41894038453439","-0.193958019433675","0.317172210456275","-0.611522740768029","0.643196434758663","0.64043575388615","0.643196434758663",NA,"0.56571076566069",NA,"0.313819553677839","0.752137464603838",NA,"0.560195995770979","0.64472788102387","0.0042921271377203","37","13",NA,"no_exp","0.597591999493347"
 "aclew","warlaumont","peak_voc_fem_ph","1.67427136936061","-0.314777958924871","0.537357746196227","-0.585788445692049","0.560061858159304","0.556899716454289","0.560061858159304",NA,"0.99544958233346",NA,"0.78194262502931","0.997722196973416",NA,"0.88427519756539","0.562545774401188","0.00564605794689881","34","13",NA,"no_exp","0.673194192987137"
@@ -251,6 +244,7 @@
 "aclew","warlaumont","avg_voc_dur_och","1.44124711942263","0.655190642806743","0.25985382595397","2.52138155134496","0.768824834971718","0.734439290274182","0.768824834971719",NA,"0.693690984859359",NA,"0.208583438787318","0.832881134892224",NA,"0.456709359207055","0.779164100313391","0.0447248100392086","38","13",NA,"no_exp","0.0490597409106801"
 "aclew","warlaumont","avg_voc_dur_chi","1.78425503716542",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
 "aclew","warlaumont","voc_dur_fem_ph","1.63500768083813","-0.458714090166475","0.391375656787176","-1.17205575311475","0.648927348118696","0.639214027485412","0.648927348118696",NA,"0.884264297034411",NA,"0.478391013452934","0.94035328309865",NA,"0.691658162283171","0.654182298835732","0.0149682713503202","38","13",NA,"no_exp","0.0708230988947146"
+"aclew","warlaumont","simple_CTC_ph","0.77310722033348","0.410999494624527","0.199320569161979","2.06200241326085","0.670229850201931","0.641814980373998","0.670229850201931",NA,"0.251648184096597",NA,"0.12381731333056","0.501645476503673",NA,"0.351876843981755","0.684210689576404","0.0423957092024057","38","13",NA,"no_exp","0.697147423164399"
 "aclew","winnipeg","wc_adu_ph","1.60360536528286","-0.615609520686824","0.5009031858887","-1.22899901224348","0.779122469281485","0.673500167173583","0.779122469281485",NA,"1.06168945034742",NA,"0.300983931831533","1.03038315705732",NA,"0.548620025000485","0.809065891783359","0.135565724609775","34","9",NA,"no_exp","0.948536991262563"
 "aclew","winnipeg","sc_fem_ph","1.53308073537155","-0.512125909826183","0.499738025381938","-1.02478875693875","0.677347380422326","0.615158336230123","0.677347380422326",NA,"0.957835515801106",NA,"0.456262395382807","0.978690715088841",NA,"0.675471979717003","0.706970965893543","0.0918126296634204","33","9",NA,"no_exp","0.924845776921682"
 "aclew","winnipeg","sc_mal_ph","1.53263502528792","-0.323343917134552","0.382062053208532","-0.846312567341171","0.790021503871411","0.735146918743472","0.790021503871411",NA,"0.62397937232877",NA,"0.16584643528663","0.789923649683164",NA,"0.407242477262171","0.804606528208572","0.0694596094650995","34","9",NA,"no_exp","0.339541990209025"
@@ -273,8 +267,6 @@
 "aclew","winnipeg","cp_dur","0.763227682726025","0.288606817880222","0.326919915048109","0.88280586344136","0.594636011339268","0.555267870549428","0.594636011339268",NA,"0.393931815167624",NA,"0.268543728956237","0.62763987697375",NA,"0.518212050184321","0.621473313945251","0.0662054433958234","34","9",NA,"no_exp","0.576776256279571"
 "aclew","winnipeg","avg_can_voc_dur_chi","1.54460472714213","0.666057843600313","0.476831161968232","1.39684210413389","0.748510060785466","0.626371025928425","0.748510060785466",NA,"0.90611199319773",NA,"0.304442200618824","0.951899150749558",NA,"0.551762811920869","0.789547235916667","0.163176209988242","31","9",NA,"no_exp","0.626928921584875"
 "aclew","winnipeg","can_voc_chi_ph","0.759730257963547","0.253118754814569","0.197492638512238","1.28166172026146","0.610542416719807","0.530123098965374","0.610542416719807",NA,"0.145405178579447",NA,"0.0927521952532334","0.381320309686551",NA,"0.304552450742452","0.661840921596753","0.131717822631378","34","9",NA,"no_exp","0.0544998543343993"
-"aclew","winnipeg","voc_chi","1.29418227195075","-0.112301804368064","0.339868299180694","-0.330427417440183","0.707525841782578","0.699892627134684","0.707525841782577",NA,"0.461358744873535",NA,"0.190714603728353","0.679233939724404",NA,"0.436708831749889","0.710681229044492","0.0107886019098074","34","9",NA,"no_exp","0.070317170229625"
-"aclew","winnipeg","simple_CTC","0.849530774795723","-0.0149502300655827","0.262504049853543","-0.0569523787306284","0.777163326249469","0.77690188069958","0.777163326249468",NA,"0.291066355040012",NA,"0.0834576931348758","0.539505658024095",NA,"0.28889045178904","0.777238290745008","0.00033641004542836","34","9",NA,"no_exp","0.0382813139806771"
 "aclew","winnipeg","peak_voc_chi","1.160885258472","0.481431248977123","0.250351687817193","1.92301978538552","0.295294942420327","0.240383281448655","0.295294942420327",NA,"0.168951977161294",NA,"0.403194554629979","0.411037683383524",NA,"0.63497602681517","0.426338586072565","0.185955304623909","34","9",NA,"no_exp","0.241624033218101"
 "aclew","winnipeg","voc_fem_ph","1.27029419349646","-0.442032154310401","0.454145757120857","-0.973326618997273","0.735019359149984","0.670463713344719","0.735019359149984",NA,"0.841097976091663",NA,"0.303222871544723","0.917113938445853",NA,"0.550656763823639","0.758292210651132","0.0878284973064128","34","9",NA,"no_exp","0.974130284371251"
 "aclew","winnipeg","peak_voc_fem_ph","1.41080734677087","-0.0976546476861551","0.455453014181373","-0.214412122975361","0.453160540655547","0.451563410056433","0.453160540655547",NA,"0.679130868012787",NA,"0.819523156519775","0.824093967950735",NA,"0.90527518275924","0.455087835631536","0.00352442557510366","33","9",NA,"no_exp","0.601580294408424"
@@ -293,6 +285,7 @@
 "aclew","winnipeg","avg_voc_dur_och","0.880842216655181","0.571002352506899","0.215175232906928","2.65366206320725","0.325100504621302","0.223181178246601","0.325100504621302",NA,"0.130886091297792",NA,"0.271715840834725","0.361781828313408",NA,"0.521263696064405","0.536682156946783","0.313500978700182","34","9",NA,"no_exp","0.710386520660642"
 "aclew","winnipeg","avg_voc_dur_chi","1.64011691421256","0.64063500473125","0.434671590079704","1.47383684453309","0.674745118869556","0.55677443774318","0.674745118869555",NA,"0.736996323130181",NA,"0.355262520275636","0.858484899768296",NA,"0.59603902579918","0.731611836084082","0.174837398340902","34","9",NA,"no_exp","0.0200207191958106"
 "aclew","winnipeg","voc_dur_fem_ph","1.63196598608172","-0.518870136751592","0.516508997750117","-1.00457134147083","0.704011507804879","0.639682153618747","0.704011507804879",NA,"1.06279913267899",NA,"0.446834049302391","1.0309214968556",NA,"0.668456467769137","0.731057583527147","0.0913754299083995","34","9",NA,"no_exp","0.832311847796414"
+"aclew","winnipeg","simple_CTC_ph","0.612927005537093","0.394114717282214","0.212615905317418","1.85364644612938","0.456734728180801","0.360107207295069","0.4567347281808",NA,"0.149086855599056",NA,"0.177332061992074","0.386117670664081",NA,"0.421108135746716","0.571668787625525","0.211561580330456","34","9",NA,"no_exp","0.00297846309959828"
 "aclew","quechua","wc_adu_ph","1.34912413250801","0.0509970403881791","0.101378944411157","0.503033846765591","0.570146740233352","0.564531883714405","0.570146740233351",NA,"0.435492608366334",NA,"0.328332873101988","0.659918637686749",NA,"0.57300337966018","0.574379973903693","0.00984809018928729","48","20",NA,"no_exp","0.952865266922495"
 "aclew","quechua","sc_fem_ph","1.44059179488123","0.0651566815202495","0.09348576096296","0.696969044794589","0.366990256140832","0.360855881445253","0.366990256140832",NA,"0.270211116774136",NA,"0.466078504687756","0.5198183497859",NA,"0.682699424847975","0.377571242665156","0.0167153612199028","45","19",NA,"no_exp","0.537319320278391"
 "aclew","quechua","sc_mal_ph","1.02044678186056","0.0586087436274094","0.0780868718991912","0.75055822063243","0.345779911454672","0.33958910656504","0.345779911454672",NA,"0.190320513446975",NA,"0.360088886122565","0.436257393572848",NA,"0.600074067197179","0.357492994803048","0.0179038882380082","48","20",NA,"no_exp","0.0826018432049434"
@@ -315,8 +308,6 @@
 "aclew","quechua","cp_dur","0.995008283530584","0.0489635204052383","0.0911549106517026","0.537146271716781","0.545317454854439","0.539319902262144","0.545317454854439",NA,"0.343416720975225",NA,"0.286338879030083","0.586017679746289",NA,"0.535106418416078","0.550318179575486","0.0109982773133421","48","20",NA,"no_exp","0.689796988356853"
 "aclew","quechua","avg_can_voc_dur_chi","0.880233565105352","-0.182255411046095","0.0874046485483319","-2.08519128070533","0.831677123460697","0.688745046808733","0.831677123460697",NA,"0.391402284707761",NA,"0.0792157876988654","0.625621518737776",NA,"0.281452993764261","0.86060510237598","0.171860055567248","47","20",NA,"no_exp","0.941265192836557"
 "aclew","quechua","can_voc_chi_ph","1.26101872342133","0.110643154320495","0.094881737360382","1.16611644557317","0.578341698806978","0.54882006410218","0.578341698806978",NA,"0.384483375883662",NA,"0.280319761563952","0.620067234970259",NA,"0.529452322276475","0.59986537998671","0.0510453158845296","48","20",NA,"no_exp","0.279905215094906"
-"aclew","quechua","voc_chi","1.18836412014673","0.0800746299848807","0.0903613892811495","0.886159792604973","0.0231529483274631","0.0227422941773223","0.0231529483274631",NA,"0.0245158992246179",NA,"1.03435137235902","0.156575538397982",NA,"1.01703066441431","0.0404788754685136","0.0177365812911913","46","20",NA,"no_exp","0.568257256127107"
-"aclew","quechua","simple_CTC","1.14593842076699",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
 "aclew","quechua","peak_voc_chi","1.0291090939968","0.191970946982444","0.102321777878127","1.87614944700331","0.0793149753598696","0.0728509441950392","0.0793149753598696",NA,"0.0950709586214473",NA,"1.10357984080312","0.308335788745723",NA,"1.05051408405748","0.154349187565874","0.0814982433708343","45","20",NA,"no_exp","0.00296733418418721"
 "aclew","quechua","voc_fem_ph","1.26197499446658","0.152730383120781","0.0972978891637173","1.56971938891491","0.43735472864307","0.400825814435836","0.43735472864307",NA,"0.32985773779234",NA,"0.424353240595276","0.574332427947735",NA,"0.651424009839426","0.484348208993151","0.0835223945573151","46","19",NA,"no_exp","0.544888548045699"
 "aclew","quechua","peak_voc_fem_ph","1.21533403968816","0.0418341170780366","0.0993725710685195","0.420982537014073","0.0609742251576579","0.0606984722245063","0.0609742251576579",NA,"0.0614905068210628",NA,"0.94697670472726","0.247972794517993",NA,"0.973127280846272","0.0652209230213568","0.00452245079685048","43","20",NA,"no_exp","0.00777358117503271"
@@ -335,9 +326,9 @@
 "aclew","quechua","avg_voc_dur_och","1.1325901381817","0.0974154685473635","0.0844619372576754","1.1533653111717","0.391997845739364","0.375130030631393","0.391997845739364",NA,"0.241666756591046",NA,"0.374833459463019","0.491596131586738",NA,"0.61223644081598","0.418160407689093","0.0430303770576999","48","20",NA,"no_exp","0.755707743369048"
 "aclew","quechua","avg_voc_dur_chi","0.742238915116269","-0.163723403842593","0.0820894504393387","-1.99445121104299","0.564507891733964","0.488471097439995","0.564507891733965",NA,"0.283962509890646",NA,"0.219064133401117","0.532881328149755",NA,"0.468042875601282","0.623166812783161","0.134695715343166","48","20",NA,"no_exp","0.0461625132554059"
 "aclew","quechua","voc_dur_fem_ph","1.50286144026239","0.0264564704403699","0.104102182723123","0.254139440195364","0.495134031520854","0.49395372249446","0.495134031520854",NA,"0.423673700026926",NA,"0.432001072974578","0.650902220019971",NA,"0.657267885245109","0.496337539706071","0.00238381721161077","48","20",NA,"no_exp","0.530591699654749"
+"aclew","quechua","simple_CTC_ph","1.14670330974059","0.159796932756354","0.0949710085126013","1.68258645726765","0.366914339299268","0.335545755408457","0.366914339299268",NA,"0.292760704661307",NA,"0.505138623068484","0.541073659182654",NA,"0.710731048335785","0.421038690764209","0.085492935355752","48","20",NA,"no_exp","0.883446711501448"
 "lena","bergelson","voc_fem_ph","1.44432106725794","-0.384256845100495","0.106493471760121","-3.60826667352945","0.501806353357788","0.495517868883456","0.501806353357792",NA,"0.54155149368433",NA,"0.537652645642701","0.735901823400602",NA,"0.733248011004941","0.50804956449124","0.0125316956077845","514","44",NA,"no_exp","4.44003895315091e-05"
 "lena","bergelson","peak_lena_CVC","0.811430246141258","0.385781810301356","0.0747286966317156","5.16243193966836","0.295748524518628","0.285435314905824","0.295748524518628",NA,"0.113623944439574",NA,"0.270567133519354","0.337081510082612",NA,"0.520160680481863","0.320306865421891","0.0348715505160667","521","44",NA,"no_exp","6.48228750104638e-13"
-"lena","bergelson","lena_CVC","0.832709315558171","0.377499684097209","0.0690720786985389","5.46530075842635","0.391066389156776","0.377835757571466","0.391066389156777",NA,"0.148441754338498",NA,"0.231140225740563","0.385281396304698",NA,"0.480770450153255","0.411667945537909","0.0338321879664435","521","44",NA,"no_exp","8.61819958224128e-08"
 "lena","bergelson","lp_dur","1.0867176776109","0.483409480624364","0.0918867774856235","5.26092538940107","0.319864336904603","0.30868217922118","0.319864336904601",NA,"0.192776914017797",NA,"0.409906448195514","0.439063678773133",NA,"0.640239367889475","0.343641242778047","0.0349590635568669","521","44",NA,"no_exp","0.714403024480289"
 "lena","bergelson","lp_n","1.11088910655532","0.512185439004922","0.0906590699127935","5.64957747192423","0.384410639106742","0.370324293944226","0.384410639106747",NA,"0.248156768362271",NA,"0.397394481048808","0.498153358276616",NA,"0.630392323120141","0.406968298374616","0.0366440044303897","518","44",NA,"no_exp","0.760242581712819"
 "lena","bergelson","peak_wc_adu_ph","1.32446577089541","-0.311092680953496","0.1349878224631","-2.30459811320043","0.218973648820759","0.217180522333377","0.218973648820757",NA,"0.234355834003261",NA,"0.835890906941789","0.48410312331492",NA,"0.914270696753313","0.225369299828013","0.00818877749463555","505","44",NA,"no_exp","1.11184715771888e-10"
@@ -359,12 +350,12 @@
 "lena","bergelson","peak_voc_mal_ph","1.52680327255262","-0.187870823975776","0.118503159058766","-1.5853655334421","0.404850569151546","0.403646132936778","0.404850569151548",NA,"0.44592663063634",NA,"0.655533178524549","0.667777381045765",NA,"0.809650034598004","0.406621147192758","0.00297501425597979","504","44",NA,"no_exp","3.11171755210047e-10"
 "lena","bergelson","voc_mal_ph","1.41687385380349","-0.0992750171616973","0.114531176731873","-0.866794701621797","0.522628384750069","0.522249949394083","0.522628384750067",NA,"0.656607859398261",NA,"0.59974919765723","0.810313432813662",NA,"0.774434760103929","0.522974049666913","0.000724100272830268","498","44",NA,"no_exp","3.16054605569192e-11"
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 "lena","cougar","lp_n","1.59817322420889","0.546702718028517","0.0707326965785996","7.72913722327849","0.409846284476985","0.195834357602359","0.409846284476984",NA,"0.18262281608173",NA,"0.262965744797331","0.427343908441117",NA,"0.512801857248325","0.718010439173361","0.522176081571002","239","26",NA,"no_exp","6.84542356897702e-09"
 "lena","cougar","peak_wc_adu_ph","1.61422065193601","-0.0337661114590718","0.105928043621393","-0.318764609490555","0.249112454131352","0.248726368860806","0.249112454131351",NA,"0.29753287096958",NA,"0.896838851660949","0.545465737667895",NA,"0.947015761041467","0.250276212171097","0.00154984331029136","230","26",NA,"no_exp","0.0738081385937209"
@@ -386,12 +377,12 @@
 "lena","cougar","peak_voc_mal_ph","1.38658664548146","0.0353153494855863","0.118931410439839","0.296938793166423","0.429645156551239","0.42892875823996","0.42964515655124",NA,"0.531333592172233",NA,"0.705346454304366","0.728926328357148",NA,"0.839849066382982","0.430596176807017","0.00166741856705712","234","26",NA,"no_exp","1.29895017312522e-07"
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-"lena","cougar","lena_CTC","2.10599918818077","0.336616374785288","0.104055058821538","3.23498327325545","0.619427585335743","0.516297251817864","0.619427585335741",NA,"0.5735161936371",NA,"0.352364744206897","0.757308519453664",NA,"0.593603187497252","0.682790213964489","0.166492962146625","226","26",NA,"no_exp","0.00735949251965056"
 "lena","cougar","peak_lena_CTC","1.73887202550873","0.292534429705845","0.106788670305046","2.73937702258308","0.428222061650278","0.373968342117643","0.428222061650278",NA,"0.417025938276605",NA,"0.556828460232137","0.645775454997018",NA,"0.746209394360683","0.500663635030748","0.126695292913105","225","26",NA,"no_exp","0.0515961463932071"
 "lena","cougar","standardScore","1.16269024434181","0.239301761177863","0.0980451356333721","2.44073058425564","0.341261242062345","0.316760200021127","0.341261242062345",NA,"0.232104927117411",NA,"0.448033625138716","0.481772692374123",NA,"0.669353139335819","0.388555760200042","0.0717955601789142","189","25",NA,"no_exp","0.0811051002247038"
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@@ -413,12 +404,12 @@
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@@ -440,12 +431,12 @@
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-"lena","lyon","lena_CVC","1.5172453558479","0.488667530454491","0.192186044256827","2.54267958084127","0.488494978785961","0.384288082708492","0.488494978785961",NA,"0.482754903932794",NA,"0.50549456616937","0.694805659111088",NA,"0.710981410565262","0.597610431151972","0.213322348443479","54","16",NA,"no_exp","9.85441661659465e-05"
 "lena","lyon","lp_dur","2.26524373239595","0.990973627487602","0.168387168043791","5.88508993292106","0.705743743700322","0.26691225708104","0.705743743700322",NA,"0.454945568744439",NA,"0.189687235733699","0.674496529823867",NA,"0.43553098137067","0.888712295035924","0.621800037954883","52","16",NA,"no_exp","0.419182936550476"
 "lena","lyon","lp_n","2.51890701476622","0.906298626244227","0.181833756709846","4.98421548695392","0.747751595248551","0.333604578511657","0.74775159524855",NA,"0.561626269915243",NA,"0.189460419145661","0.749417286907129",NA,"0.435270512607575","0.887460992016509","0.553856413504852","52","16",NA,"no_exp","0.0272241996384273"
 "lena","lyon","peak_wc_adu_ph","1.17408757672888","-0.0746886663998239","0.146008571585766","-0.511536176189161","0.300358040647961","0.297721598894084","0.300358040647961",NA,"0.212334224979233",NA,"0.494602817628868","0.460797379527307",NA,"0.703280042109023","0.306499262206142","0.00877766331205857","54","16",NA,"no_exp","0.171639981354037"
@@ -467,12 +458,12 @@
 "lena","lyon","peak_voc_mal_ph","0.912520906336113","-0.145741819411955","0.160519198349196","-0.907940115019178","0.327439367630662","0.318237559020744","0.327439367630662",NA,"0.269933429444141",NA,"0.554443405257711","0.519551180774465",NA,"0.744609565650154","0.346339887328124","0.02810232830738","54","16",NA,"no_exp","0.00012386651730328"
 "lena","lyon","voc_mal_ph","1.02234726802092","-0.113171532777885","0.13555713133615","-0.834862258166606","0.232403961542677","0.227540631517427","0.232403961542677",NA,"0.156286802909525",NA,"0.516192279943052","0.395331257187596",NA,"0.718465225284462","0.248466823966851","0.0209261924494242","54","16",NA,"no_exp","0.0124840649485656"
 "lena","lyon","peak_voc_fem_ph","1.18004127038106","0.0944751698630692","0.113414019103268","0.833011391449285","0.077446832849523","0.0762305877152521","0.0774468328495229",NA,"0.0486212751382634",NA,"0.579180706548105","0.220502324564308",NA,"0.761039227995578","0.0919348468762952","0.0157042591610431","54","16",NA,"no_exp","0.296950656130724"
-"lena","lyon","lena_CTC","0.993762841106293",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
 "lena","lyon","peak_lena_CTC","1.27310273296175","0.466878561921155","0.116617060161977","4.0035185355614","0.111229547667135","0.0796639043136365","0.111229547667135",NA,"0.068667959957837",NA,"0.548685624571091","0.262045721121023",NA,"0.740733166917137","0.363452196323611","0.283788292009975","54","16",NA,"no_exp","0.0304569144338614"
 "lena","lyon","standardScore","1.39501922326288","0.399308592179057","0.191112225964898","2.08939323563945","0.341059174477198","0.290400538716571","0.341059174477198",NA,"0.324993700021883",NA,"0.62790164583726","0.570082187076463",NA,"0.79240245193794","0.438933812563493","0.148533273846922","45","15",NA,"no_exp","0.883976852012427"
+"lena","lyon","lena_CTC_ph","1.14738130568859","0.429422643269549","0.113425578656288","3.78594183390348","0.135448200972858","0.0987376498991772","0.135448200972858",NA,"0.0753900744572332",NA,"0.48120701517366","0.2745725304127",NA,"0.693690864271442","0.36976783568243","0.271030185783252","54","16",NA,"no_exp","0.000928834888426137"
+"lena","lyon","lena_CVC_ph","1.19634598676742","0.489239600177419","0.214887497510359","2.27672435970284","0.579714964313324","0.469919313865168","0.579714964313324",NA,"0.656594527892671",NA,"0.476021616785276","0.810305206630607",NA,"0.68994319823104","0.659315236356508","0.18939592249134","53","16",NA,"no_exp","0.000160471044267879"
 "lena","warlaumont","voc_fem_ph","1.63383541016697","-0.630549219217165","0.347255329346944","-1.81580861668269","0.671365770669538","0.648561876940596","0.671365770669538",NA,"0.767155955112728",NA,"0.37552362229209","0.87587439459818",NA,"0.612799822366236","0.682528299902852","0.0339664229622562","37","13",NA,"no_exp","0.146933838678041"
 "lena","warlaumont","peak_lena_CVC","0.887869327299492","-0.661690498862921","0.39038545076532","-1.6949671089579","0.282442152238748","0.266072549051008","0.282442152238748",NA,"0.197667381072578",NA,"0.502183471591545","0.444597999402356",NA,"0.708649046843037","0.32402991503901","0.0579573659880013","38","13",NA,"no_exp","0.000302376406726965"
-"lena","warlaumont","lena_CVC","0.901040462334855","-0.0488012353066104","0.241883805536309","-0.201754868203795","0.555107912426215","0.554794595048083","0.555107912426215",NA,"0.230208082067511",NA,"0.184500620356381","0.479800043838588",NA,"0.429535354023834","0.555359021119337","0.000564426071253866","38","13",NA,"no_exp","0.295831238873014"
 "lena","warlaumont","lp_dur","1.30434481122876",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
 "lena","warlaumont","lp_n","1.26430134925892","0.180692238413939","0.370441406812027","0.487775489162927","0.288932814718663","0.287438539892882","0.288932814718662",NA,"0.179269910712338",NA,"0.441185439390056","0.42340277598563",NA,"0.664217915589497","0.292610243312015","0.00517170341913315","37","13",NA,"no_exp","0.0785300932724228"
 "lena","warlaumont","peak_wc_adu_ph","2.04052112798255","-0.817834934544693","0.45965308532325","-1.77924387034094","0.672123039195554","0.647465062550561","0.672123039195554",NA,"1.23790277014505",NA,"0.60387722839013","1.11261079005421",NA,"0.777095379210384","0.684151763060792","0.0366867005102306","33","13",NA,"no_exp","0.493602805299678"
@@ -494,12 +485,12 @@
 "lena","warlaumont","peak_voc_mal_ph","1.20185680346708","-0.551132479506802","0.377447878682206","-1.46015519131008","0.605992732140648","0.589988648848038","0.605992732140648",NA,"0.670361863890225",NA,"0.435859099391291","0.81875629090116",NA,"0.660196258237875","0.616398343954571","0.0264096951065331","37","13",NA,"no_exp","0.0701689274988425"
 "lena","warlaumont","voc_mal_ph","1.25608791538144","0.0166977857033478","0.317568910100668","0.0525800390789345","0.640536982343847","0.64051694299496","0.640536982343848",NA,"0.561361690821983",NA,"0.315030627335593","0.749240742900427",NA,"0.561275892352053","0.640548228228194","3.12852332337354e-05","38","13",NA,"no_exp","0.131090869894561"
 "lena","warlaumont","peak_voc_fem_ph","1.9020074484623","-1.02938406999466","0.413033610148928","-2.49225255451607","0.646262578855277","0.599748783375867","0.646262578855277",NA,"0.923312584428519",NA,"0.505383141794682","0.96089155705965",NA,"0.710903046691095","0.671722307762502","0.0719735243866348","34","13",NA,"no_exp","0.963229167520024"
-"lena","warlaumont","lena_CTC","0.674591679505429","-0.276510751910454","0.193027076500745","-1.43249722745185","0.660834074772862","0.6466367860089","0.660834074772863",NA,"0.22631078270581",NA,"0.116151555943413","0.475721328832133",NA,"0.340810146479551","0.668120679327873","0.0214838933189739","38","13",NA,"no_exp","0.00335132887353038"
 "lena","warlaumont","peak_lena_CTC","1.10232065902785","-0.865269533277662","0.403815605390207","-2.14273426219265","0.199251209551061","0.179909505096799","0.199251209551061",NA,"0.136457351266706",NA,"0.548393453775642","0.369401341722937",NA,"0.740535923352569","0.276981460132061","0.0970719550352622","38","13",NA,"no_exp","0.00274296613018859"
 "lena","warlaumont","standardScore","1.95528312574916","-1.19313141083961","0.561391976885114","-2.12530898189837","0.11124965705866","0.0992329031512094","0.11124965705866",NA,"0.133516951576979",NA,"1.06663912177234","0.365399714801447",NA,"1.03278222378793","0.20724900158389","0.10801609843268","36","13",NA,"no_exp","0.0988266983074217"
+"lena","warlaumont","lena_CTC_ph","0.589315183628168","-0.0839020289904269","0.200038974821773","-0.419428409214656","0.66740514567884","0.666174747574326","0.667405145678841",NA,"0.250155911620968",NA,"0.124662762224475","0.500155887320111",NA,"0.35307614224764","0.668018302591066","0.00184355501673927","38","13",NA,"no_exp","0.149565888751685"
+"lena","warlaumont","lena_CVC_ph","0.901213751236309","0.161504420110592","0.277479782214183","0.582040315953284","0.59209571277219","0.589544265666345","0.59209571277219",NA,"0.350933131927234",NA,"0.241763495251785","0.592396093781208",NA,"0.491694514156692","0.59385344583909","0.00430918017274538","38","13",NA,"no_exp","0.234252968541846"
 "lena","winnipeg","voc_fem_ph","1.26087041863769","-0.74512571684412","0.43066602018476","-1.73017067035949","0.738546698931857","0.578847986004281","0.738546698931857",NA,"0.780548391136599",NA,"0.276322341974101","0.883486497427436",NA,"0.525663715672008","0.795081723367869","0.216233737363588","35","10",NA,"no_exp","0.820207357247447"
 "lena","winnipeg","peak_lena_CVC","0.812900228471224","0.355946316094303","0.212340059932462","1.676303172409","0.502876376270706","0.413592760833285","0.502876376270705",NA,"0.15995340227809",NA,"0.158123584086417","0.399941748606082",NA,"0.397647562656201","0.591138614348102","0.177545853514818","36","10",NA,"no_exp","0.96811177784662"
-"lena","winnipeg","lena_CVC","1.02766126120061","-0.0357210356009205","0.249094597789011","-0.143403493764955","0.626218904610395","0.625104015589706","0.626218904610395",NA,"0.242804644124849",NA,"0.144926614604734","0.492752112247983",NA,"0.38069228335328","0.626884365899287","0.00178035030958117","36","10",NA,"no_exp","0.117659239474465"
 "lena","winnipeg","lp_dur","1.49123788484982","0.47653176462977","0.349119451431062","1.36495334956685","0.538133730930965","0.468475234687755","0.538133730930965",NA,"0.445398295178161",NA,"0.382273842020203","0.667381671293242",NA,"0.618282978918393","0.59791981739901","0.129444582711255","36","10",NA,"no_exp","0.341651619502249"
 "lena","winnipeg","lp_n","1.39273253328548","0.353527123557186","0.36240025024205","0.975515671749847","0.540372114940662","0.502145871089732","0.540372114940661",NA,"0.480804763606288",NA,"0.40896128892047","0.693400867901308",NA,"0.639500812290704","0.572886464088553","0.0707405929988215","36","10",NA,"no_exp","0.451064183674093"
 "lena","winnipeg","peak_wc_adu_ph","0.947973476041135","-0.443914831048874","0.354356079395891","-1.25273660270106","0.51328902351978","0.457492154454675","0.51328902351978",NA,"0.449466357295161",NA,"0.426193040626564","0.670422521470722",NA,"0.652834619659959","0.566196737074597","0.108704582619921","36","10",NA,"no_exp","0.138920236680597"
@@ -521,12 +512,12 @@
 "lena","winnipeg","peak_voc_mal_ph","0.948131478290695","-0.400944583476837","0.1845561348839","-2.1724803877642","0.231838959532346","0.18636211893875","0.231838959532346",NA,"0.0827722112585458",NA,"0.274252386442849","0.287701601070529",NA,"0.523691117399225","0.382519143993254","0.196157025054504","36","10",NA,"no_exp","0.862449924151584"
 "lena","winnipeg","voc_mal_ph","1.14741740802858","-0.474553467658915","0.212425086926819","-2.23398033878362","0.307840346485045","0.237795729177898","0.307840346485045",NA,"0.127551980916388",NA,"0.286792605076939","0.357144201851841",NA,"0.53553020930377","0.465331262147907","0.22753553297001","36","10",NA,"no_exp","0.272085444186776"
 "lena","winnipeg","peak_voc_fem_ph","1.37754466162627","-0.409776921436332","0.434343505669302","-0.943439733960994","0.581829143879791","0.541643746120245","0.58182914387979",NA,"0.713670928861439",NA,"0.512927869717973","0.844790464471184",NA,"0.716189827991136","0.610711097218505","0.0690673510982596","36","10",NA,"no_exp","0.284701037701721"
-"lena","winnipeg","lena_CTC","0.709956905167575","-0.391261765246073","0.259021100865175","-1.51054012178618","0.863609935980341","0.711390944953242","0.863609935980341",NA,"0.334852870933917",NA,"0.0528833708380162","0.578664731026453",NA,"0.229963846806441","0.887649907113404","0.176258962160161","36","10",NA,"no_exp","0.378687072055071"
 "lena","winnipeg","peak_lena_CTC","0.693478724458835","0.0633683731122525","0.257015351339322","0.246554817764917","0.693543063011204","0.689718638892693","0.693543063011204",NA,"0.272199011434084",NA,"0.120276994673249","0.521726951032898",NA,"0.34680973843485","0.695232967162241","0.00551432826954778","36","10",NA,"no_exp","0.133601851173249"
 "lena","winnipeg","standardScore","1.6188982918145","0.0237707049749344","0.46116914151559","0.0515444396318759","0.418488179101832","0.418408203716143","0.418488179101832",NA,"0.667521970794657",NA,"0.927557661388279","0.817020177715739",NA,"0.963097950048841","0.418599309199363","0.000191105483220609","32","10",NA,"no_exp","0.789148930076198"
+"lena","winnipeg","lena_CTC_ph","0.639375496589207","-0.0835068245855258","0.251160093289896","-0.33248444644086","0.691500655302652","0.684606640458689","0.691500655302652",NA,"0.259518015100631",NA,"0.115778831128744","0.50942910704104",NA,"0.340262885323603","0.694576283713685","0.00996964325499601","36","10",NA,"no_exp","0.195407767533849"
+"lena","winnipeg","lena_CVC_ph","0.889930627373244","0.267025049973563","0.210053104017949","1.27122639402056","0.45773809910303","0.409512962414422","0.457738099103029",NA,"0.150202561892281",NA,"0.177938272761015","0.387559752673418",NA,"0.421827302057388","0.514868266644304","0.105355304229882","36","10",NA,"no_exp","0.402314605773761"
 "lena","quechua","voc_fem_ph","1.33727232706971","0.04905152900626","0.0698996056764102","0.701742571100282","0.0188343068532066","0.0186199317006687","0.0188343068532066",NA,"0.0115967462405136",NA,"0.604127863690605","0.107688189884098",NA,"0.777256626662394","0.0300020948038149","0.0113821631031462","45","19",NA,"no_exp","0.0233557157973061"
 "lena","quechua","peak_lena_CVC","1.18480575795264","0.179897357950644","0.0735536106361788","2.44579914425245","0.0340688476924013","0.0297332868233416","0.0340688476924013",NA,"0.0216276221533598",NA,"0.613193442199327","0.147063327017172",NA,"0.783066690773734","0.156992092527731","0.12725880570439","44","19",NA,"no_exp","0.0381663998635709"
-"lena","quechua","lena_CVC","1.11436099582049","0.0668110528214253","0.0758953775560898","0.880304637420759","0.03880002042581","0.0380568922304818","0.03880002042581",NA,"0.0267392425288717",NA,"0.662416129953473","0.163521382482144",NA,"0.813889507214261","0.0572096706871088","0.0191527784566269","43","19",NA,"no_exp","0.716244610734989"
 "lena","quechua","lp_dur","1.21632056549292","0.25531776092559","0.0821866896554136","3.10655851934258","0.548995219430669","0.394338635305661","0.548995219430669",NA,"0.268698399432941",NA,"0.220738284025965","0.518361263437905",NA,"0.469827930231872","0.676047070372534","0.281708435066873","46","19",NA,"no_exp","0.610358318849178"
 "lena","quechua","lp_n","1.04530024701997","0.235731423912052","0.0765126991092284","3.08094508044378","0.565905299166012","0.406838920702382","0.565905299166012",NA,"0.23684071690667",NA,"0.181675803888785","0.486662836989501",NA,"0.426234447093128","0.687921954733075","0.281083034030694","46","19",NA,"no_exp","0.481174386375168"
 "lena","quechua","peak_wc_adu_ph","0.883205484423704",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
@@ -548,6 +539,7 @@
 "lena","quechua","peak_voc_mal_ph","0.683277849378501","-0.0672110551615461","0.072412093681484","-0.928174449107693","0.291177714100204","0.283329651428319","0.291177714100204",NA,"0.139830744932572",NA,"0.340394004975493","0.373939493678553",NA,"0.583432948140138","0.310282478831805","0.0269528274034855","46","19",NA,"no_exp","0.000661587885771638"
 "lena","quechua","voc_mal_ph","0.608999633486328","0.00889375672351836","0.0465473084288231","0.191069194411489","0.167781408140976","0.167610026756792","0.167781408140976",NA,"0.0382194315085333",NA,"0.189573575666731","0.195497906660233",NA,"0.435400477338657","0.168631483103376","0.00102145634658456","46","19",NA,"no_exp","0.0222058767762607"
 "lena","quechua","peak_voc_fem_ph","1.17672188565425",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
-"lena","quechua","lena_CTC","1.02824393612689","0.10992094690701","0.0672642216663783","1.63416663694116","0.140681466035797","0.131191494193391","0.140681466035797",NA,"0.0691945850170212",NA,"0.422658300560773","0.263048636219657",NA,"0.65012175825823","0.198648651911602","0.0674571577182106","46","19",NA,"no_exp","0.204960815477131"
 "lena","quechua","peak_lena_CTC","1.20582494626051","0.131605755958313","0.0702008656792463","1.87470275024286","0.0655921389540143","0.060389934412116","0.0655921389540142",NA,"0.0388340104298487",NA,"0.553218803354357","0.197063468024514",NA,"0.743786799663961","0.139701337041408","0.0793114026292917","46","19",NA,"no_exp","0.0276164865660995"
 "lena","quechua","standardScore","1.19337561472986","0.129554661423708","0.130077580013558","0.995979948352396","0.445162218915273","0.425975683342455","0.445162218915273",NA,"0.394027013495519",NA,"0.491104286405997","0.627715710728606",NA,"0.700788332098928","0.469075782042587","0.0431000987001312","36","16",NA,"no_exp","0.257137793159206"
+"lena","quechua","lena_CTC_ph","1.00291932483181","0.155179287504736","0.0663567781808541","2.33855970345333","0.2964052494145","0.251876450784195","0.2964052494145",NA,"0.118886771040527",NA,"0.282208591728413","0.344799609977341",NA,"0.531233086063371","0.402105904271488","0.150229453487293","46","19",NA,"no_exp","0.347316332381785"
+"lena","quechua","lena_CVC_ph","1.23149782957208","0.1654155355195","0.0889410960776662","1.85983243758379","0.216855686515401","0.196674907620543","0.216855686515401",NA,"0.170281702350035",NA,"0.614948812404956","0.41265203543668",NA,"0.784186720370191","0.289735777820157","0.0930608701996142","46","19",NA,"no_exp","0.935024258305362"

OUTPUT/df.icc.mixed.csv

@@ -21,8 +21,6 @@
 "aclew",NA,"cp_dur",1.5091220597277,0.434410885706191,0.0403765159746657,10.7589987699474,0.518339380579984,0.424622220636666,0.398988392796131,0.119350987783862,0.335082971807033,0.100234704559829,0.404513701831278,0.5788635174262,0.316598649017694,0.636013916381771,0.605424921443337,0.180802700806671,1245,191,8,"full",0.164056220291004
 "aclew",NA,"avg_can_voc_dur_chi",1.33173611303315,-0.0490538456326353,0.043979172993209,-1.11538808699768,0.409660109252537,0.408727434297979,0.392353953313448,0.0173061559390947,0.412554230240014,0.0181971604505869,0.620733440176977,0.642303845730363,0.134896851151489,0.787866384723309,0.411004138652375,0.00227670435439597,1228,191,8,"full",4.19083127262851e-07
 "aclew",NA,"can_voc_chi_ph",1.26029942826758,0.617614182772372,0.035165933208523,17.5628549116019,0.60142886480456,0.396185037072012,0.423048856275563,0.178380008529002,0.289202348691621,0.121943167227513,0.272469022689249,0.537775370105048,0.349203618577346,0.521985653719763,0.737445391776211,0.341260354704199,1216,191,8,"full",2.74622771533917e-12
-"aclew",NA,"voc_chi",1.40047815807443,0.426071194723197,0.0370541897105316,11.4985970021657,0.569118813369816,0.46034535266793,0.365823556671903,0.203295256697903,0.275654688339025,0.153186665002338,0.324676793075461,0.525028273847252,0.391390680781157,0.569804170812623,0.651471455243641,0.191126102575711,1242,191,8,"full",6.78905386390098e-06
-"aclew",NA,"simple_CTC",1.47591655295752,0.533515063640339,0.0345348183238148,15.4486135886933,0.599008857509623,0.426732479923682,0.356642385731142,0.242366471778476,0.2444856573989,0.166147178672371,0.274887638164247,0.494454909368792,0.407611553654175,0.524297280332682,0.714334867477982,0.2876023875543,1238,191,8,"full",1.58310970464183e-06
 "aclew",NA,"peak_voc_chi",1.3052115338496,0.505156336537043,0.0383068469149264,13.1871030173514,0.422824117229295,0.316464612774181,0.333180821775678,0.0896432954536128,0.239537048608345,0.0644481585285885,0.414954878705157,0.489425222693258,0.253866418670506,0.64416991446757,0.568010113896663,0.251545501122482,1225,190,8,"full",3.91282012613783e-09
 "aclew",NA,"voc_fem_ph",1.45747376455369,0.160102840972138,0.0419742218494536,3.81431349808864,0.557399830871733,0.542985214783098,0.372868788692384,0.184531042179349,0.356228947829331,0.17629606175386,0.422848458597379,0.596849183487195,0.419876245760414,0.65026798982987,0.568845674887816,0.0258604601047186,1240,190,8,"full",0.000102584296420943
 "aclew",NA,"peak_voc_fem_ph",1.34706605098303,0.133908110318364,0.0455239404303699,2.94148768872897,0.295658448779726,0.290805054636012,0.211317009168655,0.0843414396110688,0.212989590853703,0.0850090051219526,0.709916439788045,0.461507953185753,0.29156303798999,0.842565391995212,0.30722059819225,0.0164155435562376,1227,191,8,"full",7.64031189116186e-06
@@ -41,9 +39,9 @@
 "aclew",NA,"avg_voc_dur_och",1.57732385012919,0.328088243652795,0.0363815120015201,9.01799363476391,0.619026629010372,0.552665185167556,0.619026629010358,NA,0.550330079163304,NA,0.338694808252464,0.741842354657176,NA,0.581974920638737,0.659868075532558,0.107202890365002,1241,191,NA,"no_exp",1.99757810759153e-09
 "aclew",NA,"avg_voc_dur_chi",1.43664754541997,0.0907886124783394,0.0420413864726481,2.15950567038045,0.476518055245447,0.472791224783953,0.469574451938309,0.00694360330713043,0.485746590338466,0.00718274092037829,0.541510656550113,0.69695522836009,0.0847510526210636,0.735874076558016,0.480612188586834,0.00782096380288083,1236,191,8,"full",0.0340392264029626
 "aclew",NA,"voc_dur_fem_ph",1.41251202242163,-0.0127051802398437,0.0451728095392448,-0.28125725119678,0.534036822729506,0.533957457848417,0.397161431008609,0.136875391720896,0.425187859217311,0.146534255951145,0.498844727481715,0.652064306044512,0.382797930965078,0.706289407737165,0.534106070973414,0.000148613124996302,1237,191,8,"full",1.3246955366345e-07
+"aclew",NA,"simple_CTC_ph",1.42654678027831,0.557327783280278,0.0330812476399664,16.8472419585214,0.57916521955604,0.385746609076581,0.455319225291674,0.123845994264371,0.269240384177356,0.0732328907333216,0.248848965007666,0.518883786774414,0.270615762167176,0.498847637067337,0.719707634270263,0.333961025193682,1233,191,8,"full",7.72444882960434e-06
 "lena",NA,"voc_fem_ph",1.41199701375671,-0.148069406838096,0.0469306552890104,-3.15506795987076,0.525501476514726,0.515811640079551,0.391131447436056,0.134370029078673,0.444258049284887,0.152621241253182,0.538948708455843,0.666526855636656,0.390667686471741,0.734131261053392,0.534250858365751,0.0184392182861992,1230,191,8,"full",6.44023205691597e-07
 "lena",NA,"peak_lena_CVC",1.28770452105026,0.533109738168644,0.0372883867375471,14.2969375940267,0.464883174252291,0.335310298413581,0.30609815879595,0.158785015456346,0.213320870202865,0.110657828866272,0.372924774777203,0.46186672342015,0.332652715104314,0.610675670693702,0.614031669749751,0.27872137133617,1225,191,8,"full",1.31443564234571e-14
-"lena",NA,"lena_CVC",1.30294516434396,0.470182821952378,0.0376344897083364,12.4934023443987,0.564734490329905,0.442182481147162,0.351171516925993,0.213562973403896,0.263260905562717,0.160100631922334,0.326303207159354,0.513089568752588,0.400125770130261,0.571229557322933,0.659190670448919,0.217008189301757,1214,191,8,"full",1.32551154640784e-11
 "lena",NA,"lp_dur",1.46085830365267,0.658294462602961,0.0365181800764954,18.0264860194023,0.506108249694229,0.309402769511224,0.401088467348818,0.105019782345401,0.273311154634873,0.0715629600672663,0.336549503487254,0.522791693349151,0.26751254188779,0.580128868000252,0.698065630276379,0.388662860765155,1247,191,8,"full",0.000697263500353877
 "lena",NA,"lp_n",1.36264779324034,0.641754894227622,0.0370134430905113,17.3384273561986,0.521254651583072,0.328938891001934,0.425639716318834,0.0956149352642469,0.300984708681944,0.0676126600333393,0.338537555828478,0.548620733004089,0.260024345078186,0.581839802547469,0.697886697984696,0.368947806982762,1244,191,8,"full",6.90923411403355e-06
 "lena",NA,"peak_wc_adu_ph",1.35395428942435,-0.12103313455136,0.0458406148298421,-2.64030347325463,0.297288589217105,0.293209418830801,0.228279094631213,0.0690094945858947,0.239495937409746,0.0724003817463968,0.737240211706023,0.48938322142238,0.269073190315194,0.858626933950958,0.306930666582172,0.0137212477513707,1220,191,8,"full",7.26427538050777e-14
@@ -65,6 +63,7 @@
 "lena",NA,"peak_voc_mal_ph",1.39103796697578,-0.0620305861657772,0.0450427375450158,-1.37714955943306,0.373736887758606,0.372323823687572,0.326484045957326,0.0472528418012873,0.332592143147522,0.0481368817836664,0.637979690749811,0.576708022440751,0.219401189111788,0.798736308646233,0.376104729767002,0.00378090607942982,1228,191,8,"full",1.53846663192333e-17
 "lena",NA,"voc_mal_ph",1.26902285836936,-0.0420294067923732,0.0442922239043894,-0.948911639277793,0.443028723433027,0.44223773476507,0.396618922823173,0.0464098006098643,0.39191861010468,0.0458598001849815,0.550370635436569,0.626034032704836,0.214149013971537,0.741869688986259,0.444023146468916,0.00178541170384678,1213,191,8,"full",1.34954608614842e-17
 "lena",NA,"peak_voc_fem_ph",1.46716804925015,-0.150553036002144,0.0470234770273995,-3.20165682164294,0.334287060866377,0.327551021786435,0.255674428459157,0.0786126324072278,0.276857331417044,0.0851257740365337,0.720867976234762,0.52617234003418,0.291763215701592,0.849039443273846,0.347701484871917,0.020150463085482,1233,191,8,"full",2.35975761597958e-12
-"lena",NA,"lena_CTC",1.40311532814563,0.387791415578942,0.0384536906437075,10.0846345067895,0.605096245167969,0.517819224984212,0.322461769003377,0.282634476164604,0.280958351988516,0.246257151301284,0.344076472986546,0.530055046187201,0.496243036526745,0.586580321001776,0.662055816899486,0.144236591915274,1222,191,8,"full",1.05952155960706e-09
 "lena",NA,"peak_lena_CTC",1.34555573402461,0.414993756226962,0.0396532112758969,10.465577512488,0.497010387384358,0.416061535722033,0.25280695224223,0.244203435142126,0.218815629256953,0.211368903637325,0.43535981751303,0.46777732871202,0.459748739679975,0.659818018481634,0.578933084721922,0.162871548999889,1229,191,8,"full",7.05536450660863e-10
 "lena",NA,"standardScore",1.32339098819807,0.0369236307390135,0.0499325816004787,0.739469692042912,0.320372731791413,0.320016552905101,0.278948089485189,0.0414246423062238,0.28707999643355,0.04263225529696,0.699439792161846,0.535798466247852,0.206475798332299,0.836325171307098,0.32112831695668,0.00111176405157913,1130,186,8,"full",0.0558041643321107
+"lena",NA,"lena_CTC_ph",1.36605878487377,0.409471862151145,0.0388334014206044,10.5443213103111,0.55895641114171,0.467658576790165,0.351084190536585,0.207872220605114,0.287332326860783,0.170125600770854,0.360956386102145,0.536033885925864,0.412462847746138,0.60079645979495,0.63099480933299,0.163336232542825,1218,191,8,"full",5.95081443722613e-11
+"lena",NA,"lena_CVC_ph",1.270413167644,0.524845035287362,0.0361986890070732,14.4990067232769,0.543752773913815,0.391936261548657,0.428258735292795,0.115494038621015,0.287060178279864,0.0774152085751226,0.305820755694453,0.53577997189132,0.278235886569512,0.553010628916347,0.671137618577911,0.279201357029254,1217,191,8,"full",1.74332594140541e-11

data_output/all_rs.csv

@@ -0,0 +1,69 @@
+"","X1","X2","X3","X4","X5","X6","X7","X8","X9","X10","X11","X12","X13","X14","X15","X16","X17","X18","X19","X20","data_set","metric"
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+"2",0.60744380590414,0.623856925303961,0.54062580484287,0.55472480161382,0.593846071676229,0.607708294249994,0.58955399461157,0.4950946361123,0.575656311630062,0.539511217976342,0.575363709505421,0.555682618490975,0.55506906381151,0.581382045925099,0.578563624653707,0.592981973159342,0.640648579918711,0.571084849328106,0.558285903519239,0.510690830649518,"aclew","sc_fem_ph"
+"3",0.454071162360524,0.416643085255234,0.51533218912316,0.497063740374322,0.591178498094461,0.471474384596598,0.522868299409386,0.50036765480733,0.501438535746882,0.547862286324877,0.472576246450398,0.510839534253933,0.437894885070397,0.493173218253865,0.514134236371331,0.567551797533735,0.520590013113675,0.502958346383655,0.494607071492018,0.439444364379745,"aclew","sc_mal_ph"
+"4",0.612101582025413,0.624543074576438,0.545722890942746,0.556923250940681,0.592617310167979,0.622195219519041,0.603225900057828,0.497357241828521,0.591652144509599,0.5441069215985,0.591090795985835,0.556432397307222,0.557269218266729,0.586705264190521,0.583995318170169,0.600459305601605,0.638250562783884,0.572114153468442,0.574749936931428,0.526607430019331,"aclew","pc_fem_ph"
+"5",0.453895468816131,0.412407372282287,0.512433831300369,0.511873974622775,0.592634100402089,0.47391917352104,0.521497047396641,0.502339897135917,0.520126249863827,0.547309497991841,0.473486921390159,0.510471864543098,0.434204701889369,0.451121011024018,0.515945996981483,0.579683360433923,0.529088826493948,0.511613647409361,0.494454020675439,0.436541952599067,"aclew","pc_mal_ph"
+"6",0.519086082742011,0.569154313347122,0.533682891117097,0.508831093574555,0.595335039076691,0.58003562049415,0.648947186251174,0.574291848723576,0.548825632106,0.589004529725621,0.498902142013653,0.581185791252215,0.574064323777902,0.57031334119447,0.551804092696188,0.590081859535413,0.555797831436618,0.538727391210747,0.538449358391666,0.511443108935004,"aclew","sc_adu_ph"
+"7",0.51995062874752,0.568085421558203,0.532163353275416,0.50895545693479,0.596963031453519,0.582120235442166,0.647471002167913,0.579350351756224,0.545731445140159,0.588359810481756,0.498806213999138,0.582346981795963,0.572016233979889,0.572732787904484,0.551544693764168,0.593614620187932,0.556089134847895,0.536833080599536,0.539653156576613,0.508977330996001,"aclew","pc_adu_ph"
+"8",0.605615876579067,0.618545460689729,0.538651399072411,0.550073425134171,0.588960992728224,0.61685127983049,0.596794575012263,0.490719972563589,0.587764411316614,0.536838233451771,0.586824465022847,0.549664569846853,0.550812138441852,0.58274680029187,0.578317047217438,0.593486845415488,0.633032385125835,0.565887572830481,0.568657662905796,0.519801321589111,"aclew","wc_fem_ph"
+"9",0.422639081158376,0.409995609387467,0.510338182131191,0.486450751205043,0.583515143596572,0.4702179808776,0.519274100005391,0.498202497040722,0.505042641406648,0.545207557735081,0.460438809035565,0.508254560818523,0.431286147794675,0.492716552758193,0.514096181113092,0.580789868119779,0.529737125416281,0.511045856852237,0.492660660373013,0.434700772038992,"aclew","wc_mal_ph"
+"10",0.776329844851422,0.762317655392172,0.773629386583529,0.80731330904492,0.731206223987224,0.670378070668104,0.712161527016787,0.770412314251489,0.753567128474684,0.762713383064226,0.731973977307767,0.753508249756244,0.731202953941381,0.780639483102848,0.763748531544185,0.748497438673731,0.805129836561287,0.705149471214602,0.770480367705703,0.733992810824964,"aclew","can_voc_dur_chi_ph"
+"11",0.621767184114722,0.634605658364187,0.692825057591668,0.63645398810317,0.72163885643533,0.589857132730738,0.658281085267822,0.652289219478783,0.647537718892408,0.663932338728842,0.568432136722615,0.711800494221145,0.648943983754027,0.653799334450546,0.659337711603198,0.705469969349028,0.672474965368139,0.635086274996597,0.639090886506478,0.689460043423256,"aclew","non_can_voc_chi_ph"
+"12",0.605880566400882,0.520254021515901,0.623455921534492,0.558988310256889,0.609208373584932,0.512370889879785,0.575505767971488,0.582134245247768,0.521647027804582,0.530269924759634,0.620067712422063,0.6167847547,0.508475328776737,0.610819952814321,0.533115322397369,0.507456558400073,0.568092736409478,0.602522703242067,0.610243090959404,0.436259727960763,"aclew","avg_cry_voc_dur_chi"
+"13",0.537049457883616,0.596773739324188,0.543011654069975,0.582362892425719,0.516567932934974,0.512553516541952,0.516733100973558,0.528549854993053,0.554601085191362,0.463284997450611,0.568776087293533,0.543250750719489,0.511597658204418,0.604439235255224,0.505335046483145,0.474642800944769,0.540290629181634,0.571127659135166,0.522856594543378,0.474344201119857,"aclew","cry_voc_dur_chi_ph"
+"14",0.510725516342005,0.558775343450323,0.478223549799327,0.53377530366097,0.48002450765795,0.505111192860029,0.475047514267117,0.504943205238094,0.517192488490542,0.436340884408221,0.513046600497162,0.53184215268911,0.486452959533396,0.542002860036901,0.502944900567508,0.466410477262084,0.486997585796251,0.476688375276069,0.510592386506566,0.459310544745964,"aclew","cry_voc_chi_ph"
+"15",0.604819565510691,0.621973933703232,0.675704734556044,0.636292273760913,0.68973507440383,0.603146973672804,0.67341626947081,0.639817700452688,0.63918958475856,0.64945794552826,0.580148858722549,0.691380507410264,0.626026883384086,0.636363294533376,0.669760257894985,0.698986969787719,0.637613477605976,0.620030470618626,0.654009589287115,0.646942730696498,"aclew","non_can_voc_dur_chi_ph"
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+"17",0.465291632371695,0.536160464205058,0.40292859355782,0.521774482081191,0.397405860516335,0.481425593640985,0.503985713205109,0.411959654758617,0.426225705755156,0.480293869525321,0.477832797936293,0.409158246328431,0.403245765766393,0.490631410119349,0.53868515186307,0.512274032640272,0.530966114894488,0.518820153189833,0.437108619568531,0.36402312668394,"aclew","lp_n"
+"18",0.448729999595054,0.471972559297468,0.453237504370271,0.560383304976782,0.485241347788393,0.422328651051034,0.487204239923691,0.444060162507449,0.420185755373799,0.50502300040171,0.473107702809308,0.41991245225398,0.410315739698773,0.520833384000972,0.516806327928826,0.532917333199025,0.517929227055006,0.541310155751209,0.38491444908078,0.377088423972374,"aclew","lp_dur"
+"19",0.76364071209065,0.764088798553397,0.705123509039246,0.773229824468831,0.6736735425919,0.762336113838847,0.652349693260087,0.771745180406618,0.719328661602541,0.70740190926681,0.733502194974017,0.73273532045883,0.736740847850496,0.731450124257904,0.760200318401558,0.751329727656444,0.747361781687061,0.665839563423871,0.726412676351454,0.736094835523757,"aclew","cp_n"
+"20",0.720578597004699,0.71898788323355,0.653631045997614,0.723505272436522,0.629428734830478,0.738437824784773,0.62713089686823,0.70394266472892,0.656885947990794,0.655098176259349,0.699178023852168,0.669505163536581,0.698510393187217,0.704875463889374,0.703843261595945,0.706063117205952,0.705593786409236,0.648606912874211,0.675373338966844,0.669072854819628,"aclew","cp_dur"
+"21",0.624891235600395,0.560785946582761,0.524651163445485,0.63494566535634,0.517129020073855,0.523666664062182,0.647101288158205,0.586194620871482,0.643552310827299,0.65590147084155,0.604914090294954,0.649630440960945,0.5972047304685,0.654868060417701,0.535978490117409,0.577111710214785,0.621798586737808,0.638580413851963,0.582505489738037,0.533987881682835,"aclew","avg_can_voc_dur_chi"
+"22",0.815568063272495,0.805459505293446,0.853550032509681,0.813069836761214,0.767985633544625,0.761304170639183,0.762571137914819,0.802329182297835,0.808060976544563,0.802342492863332,0.771130745356674,0.815447705566579,0.779142018686698,0.806977696867667,0.808095122730841,0.807796002621576,0.832297872016938,0.767529886442811,0.819154903719279,0.789411685260068,"aclew","can_voc_chi_ph"
+"23",0.615767914984622,0.632523861245143,0.65993627254879,0.621370437052487,0.664720580844794,0.507925314756752,0.611127401071665,0.652484625138452,0.599253133584702,0.620533065434235,0.535939584241867,0.66980682029105,0.652449920986237,0.688435754754551,0.577394823562626,0.65316091499019,0.643632815469961,0.614956383752459,0.582881224416441,0.60999651903111,"aclew","voc_chi"
+"24",0.509705084696262,0.64644608411566,0.652356954781486,0.567004744678091,0.601767721482991,0.513381336683878,0.552965283098975,0.614592413044375,0.570598971152563,0.599267822862678,0.586362793497837,0.646863705071988,0.632287139786668,0.571074576537507,0.633372671345156,0.568269722022662,0.620818842514788,0.589585122364277,0.625776279461507,0.671739466947045,"aclew","peak_voc_chi"
+"25",0.706175264700129,0.67642278122811,0.650235335542526,0.63289114125847,0.698423643342167,0.665391588639897,0.690560831062713,0.706674186835948,0.66739813139353,0.679868269324616,0.673890400883986,0.718101177283261,0.672604754785653,0.706579846690095,0.655273202256679,0.713129054331586,0.698329332805901,0.640938109077421,0.692695962972803,0.661563313833428,"aclew","voc_fem_ph"
+"26",0.368286967172532,0.385924560544755,0.397877945541796,0.271993481829834,0.463467862276035,0.326331683661567,0.30856617894889,0.532146804341895,0.385636339427868,0.392915186158719,0.374988726818699,0.363838272820678,0.397204557847419,0.432641018643323,0.289689393306845,0.347644288797615,0.37512872089502,0.258062440879496,0.344182443561966,0.368282234799181,"aclew","peak_voc_fem_ph"
+"27",0.616031312165352,0.703420710919501,0.679859144110372,0.600042644398157,0.655110255935954,0.56185914088316,0.613779732860626,0.66893619329032,0.588925643884016,0.654720667228016,0.637983330743261,0.667715734319248,0.618656423381066,0.6574256828032,0.670309190108829,0.633315045754006,0.680241044790779,0.58936288865026,0.662435540920276,0.669323298739891,"aclew","peak_simple_CTC"
+"28",0.254626944903326,0.37738503293213,0.358232330837139,0.253076181277259,0.401727817180259,0.409008314756607,0.401004553363269,0.37328885351539,0.357317005036179,0.498959536317855,0.312637024197299,0.429834320619208,0.40218153414165,0.315850267819196,0.355500899775946,0.404750215996279,0.279884157529219,0.329667447491777,0.28801348419087,0.320820123083396,"aclew","peak_voc_mal_ph"
+"29",0.79307731314616,0.780474988230087,0.736967831313103,0.774948582308733,0.809721789260532,0.777066035706324,0.744249497365171,0.792131138180702,0.755047823361635,0.753653409524792,0.743237844222134,0.812676974313951,0.777521845728702,0.774474730389284,0.756744397684686,0.732055074471672,0.789229100582446,0.764910997238315,0.774292978328385,0.742019748640869,"aclew","voc_och_ph"
+"30",0.670776183183699,0.719846859111819,0.612354210034068,0.619794303082141,0.704366579107451,0.649355023198546,0.618233278192651,0.698673928408857,0.681952519170603,0.573115627313923,0.625629369728263,0.688174794232266,0.602803435332205,0.687852742372292,0.656172419406978,0.675345882733445,0.694113396927643,0.683967390501495,0.675921596043818,0.656707514378964,"aclew","peak_voc_och_ph"
+"31",0.663558793043939,0.688335572292661,0.718383890018228,0.683190083369334,0.734751951893677,0.616098888781895,0.689462694148258,0.704826076987046,0.679089068877865,0.708190937085788,0.635990468089049,0.749411595857203,0.689072307223663,0.711060206495477,0.688898210244804,0.699559532547737,0.701617481390255,0.657893100145003,0.676814057691932,0.715651154692895,"aclew","voc_chi_ph"
+"32",0.509705084696262,0.64644608411566,0.652356954781486,0.567004744678091,0.601767721482991,0.513381336683878,0.552965283098975,0.614592413044375,0.570598971152563,0.599267822862678,0.586362793497837,0.646863705071988,0.632287139786668,0.571074576537507,0.633372671345156,0.568269722022662,0.620818842514788,0.589585122364277,0.625776279461507,0.671739466947045,"aclew","peak_voc_chi_ph"
+"33",0.456798399927522,0.457878410815907,0.519246778287842,0.469861959180112,0.653792607028568,0.566152540372533,0.579095649315059,0.634125614796008,0.600340066186205,0.661616739432527,0.524825889839146,0.543047435328301,0.550992248338118,0.538796686989278,0.541562021079575,0.566303911402961,0.531959626399694,0.516953773264406,0.537714245759343,0.565130417801082,"aclew","voc_mal_ph"
+"34",0.412099697932018,0.41164257065729,0.479641712036116,0.444799987968187,0.6184600175909,0.510890602078329,0.56607008434508,0.537543828260123,0.551754768593841,0.634655641744801,0.5678309823274,0.513898391867727,0.503910463081003,0.540764534321028,0.506271773521366,0.578459503990369,0.509669430850683,0.524484432585359,0.548300147372986,0.513141423650995,"aclew","voc_dur_mal_ph"
+"35",0.739078902186085,0.775211074029269,0.724944716035553,0.768978165635619,0.777658099034006,0.738828663574653,0.702553986526181,0.772451204023906,0.742122896540218,0.731488474395282,0.723195396373732,0.78248304448843,0.752659560166616,0.733263195260178,0.726865600244635,0.664518249620336,0.7554697905541,0.751973871055236,0.748100787037682,0.730991306204573,"aclew","voc_dur_och_ph"
+"36",0.706707332769253,0.708745500243963,0.736918148151124,0.752688965068398,0.728484246403939,0.626360848497273,0.718275291539139,0.694203610335758,0.703810792057042,0.686733842512463,0.669158590456941,0.736769923958224,0.676539110540014,0.735097108855217,0.710781227728438,0.666640252187855,0.711954448755433,0.687867829733402,0.693002646194955,0.692676331243829,"aclew","voc_dur_chi_ph"
+"37",0.440122840513334,0.456952390841685,0.432335663743403,0.541863564526901,0.609346472668207,0.480414588748178,0.521706656599047,0.574240246186688,0.503906001261885,0.419597684789231,0.489809401289438,0.543077736496381,0.502097310529312,0.528905851547599,0.429127533711739,0.465459083030913,0.471876129428944,0.349329475757884,0.437058860055576,0.498492624743001,"aclew","avg_voc_dur_fem"
+"38",0.470822435854684,0.444124569754123,0.470513614123499,0.47306858630739,0.502944762299035,0.474514981027506,0.5188859088533,0.494309389044907,0.645400795022864,0.46971937268549,0.537206918208407,0.512653656173001,0.397020419405271,0.552273578878179,0.516001088399303,0.457804384336194,0.479581204411402,0.532271886095274,0.503480830640904,0.49574517692668,"aclew","avg_voc_dur_mal"
+"39",0.658947967388154,0.70415466881598,0.636798527108917,0.663209801428839,0.707034188429744,0.656159814503536,0.647626748786253,0.705251281714332,0.685586408417439,0.616459840717674,0.637852788090831,0.657794211184732,0.65763405074612,0.618164891469975,0.623889650322506,0.623512817740204,0.660229950592527,0.697790534781385,0.699195082812112,0.693024196237678,"aclew","avg_voc_dur_och"
+"40",0.653575962364304,0.626072665604683,0.530378149335105,0.664839988949207,0.568123427990754,0.585026185260148,0.664271769352599,0.614962622271652,0.653345004475812,0.608766936685168,0.636316207247498,0.6745105192045,0.62257920677784,0.594878126141789,0.609257782186337,0.668734329815782,0.644147542712362,0.686650662214933,0.569969275773014,0.577388249501231,"aclew","avg_voc_dur_chi"
+"41",0.609284005093348,0.60056000968558,0.508583658434065,0.555940663656364,0.631000968329319,0.598798332311116,0.612243391633638,0.59501386546207,0.586663705282806,0.56212342247169,0.591400060958242,0.588684249654415,0.585060855444436,0.633005080892077,0.55259354759516,0.568805235732638,0.595342829461703,0.52352221890042,0.579636408132324,0.547782212595295,"aclew","voc_dur_fem_ph"
+"42",0.767556122318934,0.749274426090441,0.749711862363772,0.752563930884069,0.78908216504436,0.725157628475316,0.770939026894618,0.794043352616229,0.750670235321324,0.751042993679118,0.750891295253276,0.79963776485252,0.764053382264747,0.784412038440407,0.745911217966578,0.745639180835196,0.76226990172277,0.725090971030278,0.761725724291896,0.767890406987292,"aclew","simple_CTC_ph"
+"43",0.577023856622849,0.544631119207252,0.498760160652994,0.579404038504559,0.585592718633337,0.577860011022351,0.573453835148534,0.619495370553328,0.574648213969999,0.570482938368478,0.546598841749175,0.577152805526624,0.619823478758052,0.601106972797835,0.517498227378343,0.567176278230629,0.609237975860836,0.491011246700207,0.543106110839461,0.50572333080369,"lena","voc_fem_ph"
+"44",0.575023664616254,0.696758086752186,0.645189786079153,0.641425054321643,0.629453963277946,0.592555542265142,0.644511920607655,0.658923176694353,0.580677349116494,0.623689877019895,0.640882475134395,0.661099093221465,0.593221941963173,0.594665178059069,0.614106889293579,0.593167342291599,0.725763384050695,0.631959061798077,0.639070822456476,0.674279299217425,"lena","peak_lena_CVC"
+"45",0.777161222937707,0.763658500781102,0.773891586458685,0.800911835303582,0.800250345170872,0.783075481663763,0.754179278523275,0.768031417382833,0.770998670324637,0.807818200876252,0.786758062920237,0.7636373819806,0.753697556930721,0.781068039805793,0.770126140655244,0.766969080995379,0.755641750312263,0.75010284381333,0.754536701655649,0.741592130919926,"lena","lp_dur"
+"46",0.79077637531595,0.775892305953215,0.778540101181717,0.818951135274011,0.77968598559923,0.781561820944014,0.777397390085828,0.760677633238953,0.788727690483725,0.824719327683834,0.7905301880737,0.782711852829207,0.768965848172053,0.790343241539025,0.783933777661662,0.786803693321835,0.769893435356823,0.752394046882338,0.776009882632672,0.784722534577451,"lena","lp_n"
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+"54",0.364900069260369,0.479264498456893,0.398319461934832,0.416228797503012,0.571532062107263,0.401837408734622,0.441807201825882,0.400458521778958,0.447276962986031,0.498825040089607,0.458625150944248,0.432328467824555,0.476695084204696,0.503388313690748,0.354405910615426,0.474238605318202,0.459705328778063,0.355763731449345,0.361516846865296,0.393206280701846,"lena","avg_voc_dur_fem"
+"55",0.622199541279469,0.616934907628417,0.601918166973835,0.668288822566744,0.618052795570861,0.533003299791367,0.609294042075418,0.612406516857663,0.602522842653481,0.583914917947506,0.624999686236765,0.652101227976342,0.527175048535465,0.670863944066618,0.585884944211583,0.613304727759076,0.643922141339969,0.578853715849266,0.588505687758261,0.613489683402696,"lena","voc_dur_chi_ph"
+"56",0.70816980177094,0.665030122170378,0.73953518776717,0.737379028893827,0.722312118952761,0.664679195864159,0.695303728847736,0.699074038686938,0.640825349381162,0.691493325766367,0.608412025982768,0.733923919811333,0.689486277017726,0.678049190355402,0.670224146291901,0.642662826383981,0.712466729172385,0.689500557429511,0.701436018774193,0.660699624490525,"lena","voc_dur_och_ph"
+"57",0.410247586816077,0.481438182087989,0.499339697603498,0.521765499959078,0.449726601647751,0.415491664354224,0.443743534234547,0.469448748818048,0.537673880251299,0.610337066586464,0.478811233748408,0.526506838358629,0.392284119532809,0.573059365253609,0.470675357616884,0.484413397651443,0.39403917688958,0.417718690737818,0.385245328585946,0.505948425739961,"lena","voc_dur_mal_ph"
+"58",0.524702931585103,0.526804530556653,0.44776505063486,0.542107711185311,0.526967957568277,0.56143906129784,0.551012499575954,0.578910522472196,0.551261193891636,0.587169761657119,0.529973693270416,0.515136489450672,0.61366977467668,0.532829935726937,0.452673323274802,0.520855502606575,0.555989763864913,0.435403475897326,0.522096847563674,0.487574331901232,"lena","voc_dur_fem_ph"
+"59",0.455612270898647,0.566611625504985,0.525770211275486,0.457873720479021,0.560021934427905,0.417797272219518,0.547179260569881,0.591096135864837,0.430339244539447,0.483560626524835,0.512844874573083,0.619521882220236,0.531273191646718,0.510365567806462,0.479989882994034,0.444448054105518,0.557318576502302,0.456022297100602,0.515027796846623,0.580505323476756,"lena","peak_voc_chi_ph"
+"60",0.574927663350819,0.585985512441094,0.610773630529681,0.668344551647968,0.602125495152683,0.530440073033008,0.631801891586782,0.622291783428742,0.598128488302872,0.600504177805796,0.594325656154649,0.678383598635582,0.556337765564854,0.638573434729219,0.593594951719832,0.597744632232196,0.620694646079525,0.543883135348932,0.607000557981912,0.615504085360421,"lena","voc_chi_ph"
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+"63",0.286484562164517,0.332074549164244,0.389989344642855,0.365276501608557,0.409887886333878,0.298290365236591,0.353706242169203,0.404723544833092,0.5155604556104,0.501068005820689,0.325310197418498,0.472635885764813,0.425138046567129,0.366001251141388,0.384037229722257,0.414133661984493,0.388517296927434,0.372985817271637,0.337488128410496,0.395800058117127,"lena","peak_voc_mal_ph"
+"64",0.335229515634742,0.487034309928276,0.38757652290954,0.424489940988794,0.417957387666436,0.436160598384061,0.404142274859563,0.487991415187703,0.52571301497637,0.592933022039014,0.496478303919758,0.539576882267025,0.350186804051403,0.519252297390048,0.442797901768391,0.466293418302344,0.343304052337942,0.326915201596124,0.38712436195036,0.533075131858983,"lena","voc_mal_ph"
+"65",0.342992005830307,0.291684165433152,0.273424725276735,0.26770372239029,0.357344802199788,0.305670113844583,0.337084544463392,0.416165673082298,0.37586799053497,0.319303957240314,0.348238599072395,0.3214757017161,0.408797540026915,0.352482443973355,0.231927735737371,0.255166839355655,0.401433771954828,0.228845848404545,0.315723437627807,0.311058487615618,"lena","peak_voc_fem_ph"
+"66",0.605702513602109,0.594960093725274,0.599230236546151,0.595049762046316,0.629742492369314,0.595853104408573,0.610603751606132,0.662293288971053,0.556855282975889,0.588735662785858,0.647560057000835,0.565504952204795,0.615952043915052,0.576532536018628,0.574290308509147,0.596380510375799,0.632187474854145,0.500564341416473,0.652903522616657,0.609605032571637,"lena","peak_lena_CTC"
+"67",0.514674874112739,0.463872063365489,0.382343729503505,0.505179002037924,0.647481248001895,0.514252742479428,0.351661746285737,0.403323626273577,0.502829551834331,0.502668849325504,0.462302102757342,0.306867628625978,0.415661956926733,0.541541974013581,0.449488633955382,0.299640143665633,0.502540276769894,0.604939869917928,0.450664771123084,0.437438198817996,"lena","standardScore"
+"68",0.724467792452633,0.635598483391995,0.693338144883635,0.737890602864552,0.667514767141115,0.687357786792968,0.687703695902893,0.742686061759789,0.704225016614418,0.681940831486715,0.668915328546687,0.749318583234338,0.701987009492323,0.668156090147111,0.684059709119377,0.661727902964816,0.724959702737149,0.613448433738284,0.733768200879471,0.699212927412113,"lena","lena_CTC_ph"