supposedly final results, final check in progress

CODE/SM.Rmd

@@ -1,13 +1,13 @@
 ---
 title: Supplementary Materials to Establishing the reliability and validity of measures extracted from long-form recordings
 output:
-  pdf_document:
-    toc: yes
-    toc_depth: 3
   html_document:
     toc: yes
     toc_depth: '3'
     df_print: paged
+  pdf_document:
+    toc: yes
+    toc_depth: 3
 ---
 
 ```{r setup, include=FALSE, eval=TRUE}
@@ -92,7 +92,7 @@ if(RECALC) source("regenerate_data.R") # This code cannot be reproduced without
 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...
 ```
 
 
@@ -264,7 +264,7 @@ ggarrange(bad, good,
 ```{r readin}
 df.icc.mixed<-read.csv("../output/df.icc.mixed.csv")
 
-df.icc.mixed[df.icc.mixed$formula=="no_exp",c("data_set","metric")]
+#df.icc.mixed[df.icc.mixed$formula=="no_exp",c("data_set","metric")]
 
 
 
@@ -340,6 +340,8 @@ read.csv("../input/quechua_md.csv")->x ##OH this needs to be done!!!
 ```
 
 
+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 > .5)` of them
+
 Six measures had higher ICCs, and surprisingly, they corresponded to the "other child" category, known to have the worst accuracy according to previous analyses (Cristia et al., 2020). We reasoned this may be because children in our corpora vary in terms of the number of siblings they have, and that siblings' presence may be stable across recordings. To address this possibility, we fit the full model again to predict number of vocalizations from other children, but this time including 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 did this for the metric with the highest Child ICC, ACLEW's total vocalization duration by other children. Results indicated the full model was singular, a first sign that included variables explained shared variance. When we fitted the No Corpus model, Child ICC was indeed reduced from `r round(df.icc.mixed[df.icc.mixed$metric=="voc_dur_och_ph" & df.icc.mixed$data_set=="aclew","icc_child_id"],2)` to `r round(icc.result.split["icc_child_id"],2)`.
 
 ```{r reg model icc}
@@ -421,16 +423,11 @@ 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),]
-
-matches=dist_contig_lena[((dist_contig_lena$session_id == dist_contig_aclew$session_id) & (dist_contig_lena$next_session == dist_contig_aclew$next_session)),c("session_id","next_session","child_id")]
-#dim(matches) #??? what??? there's only 14 cases in which the same sessions are chosen as contiguos in lena vs aclew??? this doesn't make ANY sense!
+# 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
@@ -445,7 +442,7 @@ 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 <- matches  %>%
+    close_sessions <- dist_contig_lena  %>%
     group_by(child_id)%>%
     slice_sample(n = 1) 
   #table(close_sessions$experiment) #these have to be similar
@@ -487,7 +484,7 @@ rownames(mytab)<-c("AWC","CVC","CTC","Chi vocs")
 print(mytab)
 
 ```
-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(matches$child_id)))` children (belonging to `r length(levels(factor(gsub(" .*","",matches$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). 
+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
 
@@ -555,10 +552,7 @@ ggplot(df.icc.corpus, aes(y = icc_child_id, x = toupper(data_set))) +
 ```
 
 
-```{r icc-bycor-fig5B, 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.",eval=F}
-
-#this is bugging because of the missing VCM measures -- remove this ugly fix!!!! ,eval=F
-#ac !! dugh ugh ugh
+```{r icc-bycor-fig5B, 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."}
 
 
 

CODE/SM.log

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CODE/create-all-icc.R

@@ -45,7 +45,6 @@ df.icc.corpus.cols = c("data_set","corpus","metric", "iqr",
 df.icc.corpus = data.frame(matrix(ncol=length(df.icc.corpus.cols),nrow=0, dimnames=list(NULL, df.icc.corpus.cols)),
                            stringsAsFactors=FALSE)
 
-#UGLY FIX!!!! BECAUSE VCM DATA MISSING IN LYON/QUECHUA :(
 
 for (data_set in data_sets){   # data_set = "aclew"
   # Load data 
@@ -53,8 +52,7 @@ for (data_set in data_sets){   # data_set = "aclew"
   for(corpus in corpora){
     mycordat <- mydat[mydat$experiment == corpus, ]
     metrics <- colnames(mycordat)[!is.element(colnames(mycordat), no.scale.columns)] #this should be defined here bc it varies by data_set
-    for(metric in metrics) if(!(corpus%in% c("lyon","quechua") & 
-                                metric %in% metrics[1:22])){  # metric = "avg_voc_dur_mal"
+    for(metric in metrics) {  # metric = "avg_voc_dur_mal"
       icc.row <- new_fit_models(mycordat, data_set, metric, corpus, FALSE)
       df.icc.corpus[nrow(df.icc.corpus) + 1,] <- icc.row
     } 

CODE/sessionInfo.txt

@@ -23,19 +23,21 @@ other attached packages:
 [13] lme4_1.1-34           Matrix_1.6-0         
 
 loaded via a namespace (and not attached):
- [1] beeswarm_0.4.0    gtable_0.3.3      xfun_0.39         insight_0.19.3   
- [5] rstatix_0.7.2     lattice_0.21-8    vctrs_0.6.3       tools_4.3.1      
- [9] generics_0.1.3    parallel_4.3.1    tibble_3.2.1      fansi_1.0.4      
-[13] highr_0.10        pkgconfig_2.0.3   webshot_0.5.5     lifecycle_1.0.3  
-[17] farver_2.1.1      compiler_4.3.1    munsell_0.5.0     mnormt_2.1.1     
-[21] vipor_0.4.5       htmltools_0.5.5   yaml_2.3.7        pillar_1.9.0     
-[25] nloptr_2.0.3      MASS_7.3-60       boot_1.3-28.1     abind_1.4-5      
-[29] nlme_3.1-162      tidyselect_1.2.0  rvest_1.0.3       digest_0.6.33    
-[33] stringi_1.7.12    purrr_1.0.1       labeling_0.4.2    splines_4.3.1    
-[37] cowplot_1.1.1     fastmap_1.1.1     grid_4.3.1        colorspace_2.1-0 
-[41] cli_3.6.1         magrittr_2.0.3    utf8_1.2.3        broom_1.0.5      
-[45] withr_2.5.0       scales_1.2.1      backports_1.4.1   rmarkdown_2.23   
-[49] httr_1.4.6        gridExtra_2.3     ggsignif_0.6.4    evaluate_0.21    
-[53] knitr_1.43        viridisLite_0.4.2 mgcv_1.8-42       rlang_1.1.1      
-[57] Rcpp_1.0.11       glue_1.6.2        xml2_1.3.5        svglite_2.1.1    
-[61] rstudioapi_0.15.0 minqa_1.2.5       R6_2.5.1          systemfonts_1.0.4
+ [1] beeswarm_0.4.0    gtable_0.3.3      xfun_0.39         bslib_0.5.0      
+ [5] insight_0.19.3    rstatix_0.7.2     lattice_0.21-8    vctrs_0.6.3      
+ [9] tools_4.3.1       generics_0.1.3    parallel_4.3.1    tibble_3.2.1     
+[13] fansi_1.0.4       highr_0.10        pkgconfig_2.0.3   webshot_0.5.5    
+[17] lifecycle_1.0.3   farver_2.1.1      compiler_4.3.1    mnormt_2.1.1     
+[21] munsell_0.5.0     vipor_0.4.5       htmltools_0.5.5   sass_0.4.7       
+[25] yaml_2.3.7        pillar_1.9.0      nloptr_2.0.3      jquerylib_0.1.4  
+[29] MASS_7.3-60       cachem_1.0.8      boot_1.3-28.1     abind_1.4-5      
+[33] nlme_3.1-162      tidyselect_1.2.0  rvest_1.0.3       digest_0.6.33    
+[37] stringi_1.7.12    purrr_1.0.1       labeling_0.4.2    splines_4.3.1    
+[41] cowplot_1.1.1     fastmap_1.1.1     grid_4.3.1        colorspace_2.1-0 
+[45] cli_3.6.1         magrittr_2.0.3    utf8_1.2.3        broom_1.0.5      
+[49] withr_2.5.0       scales_1.2.1      backports_1.4.1   rmarkdown_2.23   
+[53] httr_1.4.6        gridExtra_2.3     ggsignif_0.6.4    evaluate_0.21    
+[57] knitr_1.43        viridisLite_0.4.2 mgcv_1.8-42       rlang_1.1.1      
+[61] Rcpp_1.0.11       glue_1.6.2        xml2_1.3.5        svglite_2.1.1    
+[65] rstudioapi_0.15.0 minqa_1.2.5       jsonlite_1.8.7    R6_2.5.1         
+[69] systemfonts_1.0.4

OUTPUT/df.icc.age.csv

@@ -8,19 +8,19 @@
 "aclew","(0,6]","pc_adu_ph","1.40783336887353","-1.18285522434645","0.609092872386157","-1.94199485492672","0.335441159827977","0.324545752594483","0.335441159827978",NA,"0.324386970030734",NA,"0.642658845685762","0.569549795918437",NA,"0.801660056187011","0.357026582433854","0.0324808298393709","124","64",NA,"no_exp","0.211739186293532"
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OUTPUT/df.icc.corpus.csv

@@ -8,19 +8,19 @@
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@@ -50,19 +50,19 @@
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@@ -92,19 +92,19 @@
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@@ -167,6 +167,28 @@
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@@ -196,19 +218,19 @@
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@@ -238,19 +260,19 @@
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@@ -271,6 +293,28 @@
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@@ -399,6 +443,28 @@
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+"lena","lyon","avg_voc_dur_fem","1.11761804854325","0.0998454376073551","0.197747042997916","0.504914946356024","0.450430087275385","0.4458552442884","0.450430087275385",NA,"0.483307633982032",NA,"0.589683819376514","0.695203304064381",NA,"0.767908731150073","0.45601185486177","0.0101566105733701","53","16",NA,"no_exp","0.217948711474097"
+"lena","lyon","voc_dur_chi_ph","1.5875611042946","0.0685109096952197","0.227571897482009","0.301051713560704","0.682302324487057","0.679407204898594","0.682302324487057",NA,"0.810710758511836",NA,"0.377487976002683","0.900394779256208",NA,"0.614400501304062","0.683650367327565","0.00424316242897163","53","16",NA,"no_exp","0.428118683603868"
+"lena","lyon","voc_dur_och_ph","1.57603157900722","0.541497991684673","0.223851043124821","2.4190103567341","0.624413131235467","0.49026461287784","0.624413131235466",NA,"0.750027923611061",NA,"0.4511446432231","0.866041525338746",NA,"0.671673018084767","0.705103974266989","0.214839361389149","52","16",NA,"no_exp","0.659535348326878"
+"lena","lyon","voc_dur_mal_ph","0.995623490898574","-0.094545626812142","0.132079002443415","-0.71582632411724","0.244069889253119","0.240227081470434","0.244069889253119",NA,"0.1530557203135",NA,"0.474042201441074","0.391223363711193",NA,"0.688507226861907","0.255971783917938","0.0157447024475042","54","16",NA,"no_exp","0.00532097495484487"
+"lena","lyon","voc_dur_fem_ph","1.13299655714959","0.109912627902773","0.127424304055808","0.862571930191858","0.243318013103157","0.2378007931923","0.243318013103157",NA,"0.142181016785141",NA,"0.442161321752951","0.377068981467769",NA,"0.664952119895073","0.260475727285723","0.0226749340934229","54","16",NA,"no_exp","0.703064994357543"
+"lena","lyon","peak_voc_chi_ph","1.83595930888045","0.318768426941132","0.19390952594921","1.64390287367638","0.389704339228632","0.353554508372681","0.389704339228632",NA,"0.434625413838901",NA,"0.680644215180843","0.659261263717883",NA,"0.825011645481955","0.446316705805495","0.092762197432814","54","16",NA,"no_exp","0.0257293949431904"
+"lena","lyon","voc_chi_ph","1.64215014212725","0.265074651561597","0.220629815508017","1.20144528495046","0.557717703098921","0.524021488876616","0.557717703098921",NA,"0.683908469186418",NA,"0.542354325389273","0.82698758708122",NA,"0.736447096123865","0.584439535560581","0.060418046683965","54","16",NA,"no_exp","0.0307775331184172"
+"lena","lyon","peak_voc_och_ph","1.61518578145691","0.4949227865332","0.182733465591246","2.70844087004997","0.502799143523271","0.383312183640145","0.502799143523271",NA,"0.443294173428",NA,"0.438358429083808","0.665803404488142",NA,"0.66208642115951","0.620955706749281","0.237643523109135","53","16",NA,"no_exp","0.93959090011088"
+"lena","lyon","voc_och_ph","1.6407792986736","0.539814675172765","0.20798027593124","2.59550898639654","0.567308077377132","0.433770885006451","0.567308077377132",NA,"0.613158688869834",NA,"0.467662673139827","0.783044499929496",NA,"0.683858664593662","0.669158318564724","0.235387433558274","51","16",NA,"no_exp","0.580496053776296"
+"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
@@ -458,6 +524,28 @@
 "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","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
+"lena","quechua","wc_adu_ph","0.899188394790519",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
+"lena","quechua","wc_mal_ph","0.598394607449793","0.00547941392588578","0.0416363671840135","0.131601633294982","0.252520439233679","0.252385542042567","0.252520439233679",NA,"0.0417695568276222",NA,"0.123641041040732","0.204376018230178",NA,"0.351626280361312","0.25291974510162","0.00053420305905299","46","19",NA,"no_exp","0.0277478165551345"
+"lena","quechua","wc_fem_ph","1.08747265330341",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
+"lena","quechua","avg_voc_dur_chi","0.979092183640351","-0.165128904203781","0.083850458864525","-1.9693261842559","0.440112019956033","0.384783283527754","0.440112019956033",NA,"0.245758427238273",NA,"0.31264128941301","0.495740282041184",NA,"0.559143353186828","0.510498405859125","0.125715122331371","46","19",NA,"no_exp","0.989472507342527"
+"lena","quechua","avg_voc_dur_och","0.959016899988272","-0.176075708694953","0.0600435681863607","-2.93246577465977","0.0347710403513672","0.0289379350463947","0.0347710403513672",NA,"0.0157477257547463",NA,"0.437150018908998","0.125489942843027",NA,"0.661173213998418","0.196695507038144","0.167757571991749","46","19",NA,"no_exp","0.210025154142199"
+"lena","quechua","avg_voc_dur_mal","0.903824922357865",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
+"lena","quechua","avg_voc_dur_fem","1.18917771463237","-0.167438392321079","0.0857081863780409","-1.95358692555392","0.242677123560897","0.217350486034323","0.242677123560897",NA,"0.171931541706294",NA,"0.536547029258594","0.414646285050637",NA,"0.732493705951521","0.321714000629132","0.104363514594809","46","19",NA,"no_exp","0.275110022598179"
+"lena","quechua","voc_dur_chi_ph","1.37887477852415",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
+"lena","quechua","voc_dur_och_ph","1.68484853633103","-0.0295743371446724","0.115414248446428","-0.256245113084109","0.343051701091719","0.34225832927455","0.343051701091719",NA,"0.3922855538903",NA,"0.751231742895871","0.626327034296221",NA,"0.866736259133002","0.34457101804608","0.00231268877153056","44","19",NA,"no_exp","0.815389449268677"
+"lena","quechua","voc_dur_mal_ph","0.579065926758747","0.00147019882933026","0.0449000150316523","0.0327438382435693","0.201362608397006","0.20135631681264","0.201362608397006",NA,"0.0410175536281771",NA,"0.162682398188632","0.20252790826989",NA,"0.403339061074714","0.201387561860649","3.12450480092375e-05","46","19",NA,"no_exp","0.0227027276537557"
+"lena","quechua","voc_dur_fem_ph","1.26173267615626","0.0388225166911393","0.0714489247833993","0.5433604047763","0.029938704987586","0.0297344057803022","0.029938704987586",NA,"0.0193386659242771",NA,"0.626603298910068","0.1390635319711",NA,"0.791582780832219","0.0365583217569113","0.00682391597660909","46","19",NA,"no_exp","0.0642498354580141"
+"lena","quechua","peak_voc_chi_ph","1.27461634945794","0.12615977776167","0.0984090950440503","1.28199306888451","0.0958512032876064","0.091868607091352","0.0958512032876063",NA,"0.100657092834577",NA,"0.949481970444061","0.31726501987231",NA,"0.97441365468884","0.133418384868314","0.0415497777769615","44","19",NA,"no_exp","0.708784432747772"
+"lena","quechua","voc_chi_ph","1.46406911195945",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"no_chi_effect",NA
+"lena","quechua","peak_voc_och_ph","1.24778300713409","-0.00845828039962052","0.0937117003507208","-0.090258530876774","0.0959287078129378","0.095908298836375","0.0959287078129377",NA,"0.0958433935004839",NA,"0.903267255288613","0.309585841892816",NA,"0.950403732783396","0.0961210503372355","0.000212751500860514","45","19",NA,"no_exp","0.226998932275729"
+"lena","quechua","voc_och_ph","1.84391221306266","-0.00130963198647204","0.120947600595221","-0.0108280939847251","0.343102847921711","0.343101427689104","0.343102847921711",NA,"0.430845298747434",NA,"0.82488691495236","0.656388070235462",NA,"0.908232852825948","0.343105567067765","4.13937866055737e-06","44","19",NA,"no_exp","0.865523932013976"
+"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"

OUTPUT/df.icc.mixed.csv

@@ -1,26 +1,26 @@
 "data_set","age_bin","metric","iqr","age_b","age_se","age_t","icc_adjusted","icc_conditional","icc_child_id","icc_corpus","child_id_var","corpus_var","residual_var","child_id_sd","corpus_sd","residual_sd","r2_cond","r2_marg","nobs","nchi","ncor","formula","sw"
-"aclew",NA,"wc_adu_ph",1.39834708718581,0.0353940221544056,0.0456039377746422,0.776117674954074,0.455118598106989,0.45459073598759,0.325238217199891,0.129880380907095,0.340247186190594,0.135874051104645,0.570026380656676,0.583307111383527,0.368610975290543,0.755000914341616,0.455750570196946,0.00115983420935644,1219,191,8,"full",4.48030725380745e-08
+"aclew",NA,"wc_adu_ph",1.39834708718581,0.0353940221658755,0.0456039377746273,0.776117675205839,0.455118598090009,0.45459073597026,0.325238217290086,0.129880380799921,0.340247186266423,0.135874050985126,0.570026380643396,0.583307111448526,0.368610975128422,0.755000914332821,0.45575057018043,0.00115983421017037,1219,191,8,"full",4.48030725270666e-08
 "aclew",NA,"sc_fem_ph",1.43206436001967,0.0184672181802355,0.0454878617194332,0.405981232842738,0.517602734914694,0.517442268052595,0.368315144434488,0.149287590480197,0.394665588521939,0.159968102442144,0.516909509161195,0.628224154678837,0.399960126065266,0.718964191849076,0.517752287400012,0.000310019347417168,1233,190,8,"full",3.25227585449576e-08
 "aclew",NA,"sc_mal_ph",1.41177766561036,0.0273933341149073,0.0447157937211623,0.61260981490625,0.422506973018207,0.422193735000548,0.360889020391458,0.0616179526267468,0.359026962740003,0.0613000261350204,0.57451337049796,0.599188586957398,0.24758842084197,0.757966602495097,0.422935114522456,0.00074137952190854,1227,191,8,"full",1.17281030399006e-11
 "aclew",NA,"pc_fem_ph",1.41592624527927,0.0232272496259159,0.0453056540165029,0.512678828506817,0.530004908965321,0.529745223621515,0.369686394098888,0.160318514866436,0.399886437517412,0.173415091281051,0.508389450097684,0.632365746635135,0.416431376436804,0.713014340737747,0.53023519143014,0.000489967808624928,1235,191,8,"full",6.90250236144521e-08
 "aclew",NA,"pc_mal_ph",1.42679899943196,0.0307332884444745,0.0447292134637975,0.687096554231813,0.424959729873028,0.424564519923687,0.36263993530909,0.062319794563934,0.361116009141238,0.0620579073406468,0.572623771473926,0.600929287970921,0.249114245559436,0.756719083593063,0.425494513806776,0.000929993883089478,1227,191,8,"full",1.40672320334034e-11
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-"aclew",NA,"pc_adu_ph",1.39163710073087,0.0410012543212746,0.0455637174668546,0.899866310318095,0.458055433487074,0.457343385866841,0.324054415069557,0.134001018417522,0.339295923629607,0.14030359469573,0.567434275541597,0.582491136095312,0.374571214451578,0.753282334547676,0.458897886846804,0.00155450097996226,1219,191,8,"full",6.32368398019767e-08
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@@ -33,8 +33,8 @@
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@@ -43,28 +43,28 @@
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