fixing order in progress, knits ok

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}
@@ -202,6 +202,8 @@ nrecs=length(levels(mydat_aclew$session_id))
 
 Please see code in the RMarkDown version of the document.
 
+> A variable with one of the lowest Child ICC (LENA's peak hourly AWC, Child ICC = `r round(df.icc.mixed[df.icc.mixed$data_set=="lena" & df.icc.mixed$metric=="peak_wc_adu","icc_child_id"],2)`) has a flatter regression line and wider confidence intervals, with points spread widely around the regression line. In contrast, the variable with the highest Child ICC (ACLEW's other child vocalization duration, Child ICC = `r round(df.icc.mixed[df.icc.mixed$data_set=="aclew" & df.icc.mixed$metric=="voc_och_ph","icc_child_id"],2)`) has points that are less scattered and closer to the diagonal. 
+
 
 ```{r icc-examples-fig1,  fig.width=6, fig.height=4.5,fig.cap="Figure 2 (reproduced). Scatterplots for two selected variables. The left one has relatively low ICCs; the right one has relatively higher ICCs."}
 # 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
@@ -271,9 +273,10 @@ fig2 = ggarrange(bad, good,
           font.label = list(size = 20))  + labs(color= "Corpus")  +  theme(text = element_text(size = 20))
 fig2 
 
-ggsave("fig2.png", plot = fig2, width = 6, height = 4.5, units = "in")
+ggsave("fig1.png", plot = fig2, width = 6, height = 4.5, units = "in")
 ```
 
+
 ## SM E: Code to reproduce text at the beginning of the "Setting the stage" section
 
 Please see code in the RMarkDown version of the document.
@@ -332,11 +335,27 @@ reg_anova_cor=Anova(lr_cor)
 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)
+
+r_msds_type=aggregate(rval_tab$m,by=list(rval_tab$Type),get_msd)
+rownames(r_msds_type)<-r_msds_type$Group.1
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Other children","Adults")] ~ rval_tab$Type[rval_tab$Type %in% c("Other children","Adults")]) #marginal
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Other children","Male")] ~ rval_tab$Type[rval_tab$Type %in% c("Other children","Male")]) #sig
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Other children","Female")] ~ rval_tab$Type[rval_tab$Type %in% c("Other children","Female")]) #sig
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Other children","Output")] ~ rval_tab$Type[rval_tab$Type %in% c("Other children","Output")]) #not sig
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Male","Output")] ~ rval_tab$Type[rval_tab$Type %in% c("Male","Output")]) #sig
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Female","Output")] ~ rval_tab$Type[rval_tab$Type %in% c("Female","Output")]) #sig
+
+cor_t=t.test(rval_tab$m[rval_tab$Type %in% c("Adults","Output")] ~ rval_tab$Type[rval_tab$Type %in% c("Adults","Output")]) #not sig
 
 ```
 
-> 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. The model was overall significant (F(`r round(reg_sum_cor$fstatistic["dendf"],2)`) = `r round(reg_sum_cor$fstatistic["value"],2)`, p < .001). 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"]`). 
+> 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. The model was overall significant (F(`r round(reg_sum_cor$fstatistic["dendf"],2)`) = `r round(reg_sum_cor$fstatistic["value"],2)`, p < .001). We found an adjusted R-squared of `r round(reg_sum_cor$adj.r.squared*100)`%. A Type 3 ANOVA on this model revealed   a significant effect of type (F = `r round(reg_anova_cor["Type","F value"],2)`, p < .001), with higher correlations for other children metrics (`r r_msds_type["Other children","x"]`) and output metrics (`r r_msds_type["Output","x"]`) than female (`r r_msds_type["Female","x"]`), and male (`r r_msds_type["Male","x"]`) metrics. There was also a marginal 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)`), with numerically higher correlations  for ACLEW (`r r_msds["aclew","x"]`) than for LENA metrics (`r r_msds["lena","x"]`). 
 
 See table below for results of the Type 3 ANOVA.
 
@@ -386,15 +405,39 @@ May it be that correlation coefficients are higher than Child ICC because we con
 
 This is still lower than the correlation observed for this same variable, `r rval_tab[rval_tab$metric=="can_voc_chi_ph","m"]`. We believe this is due to another difference between the two analyses, namely that here we are including all recordings, whereas the other analysis is focused on recordings less than 2 months away.
 
+## SM J: Information that is incorporated in Figure 2 (model fitting)
 
+> Out of the `r dim(df.icc.mixed)[1]` fitted models, `r table(df.icc.mixed$formula)["full"]` could be fit with the full model, yielding a measure of Corpus ICC.  For the `r table(df.icc.mixed$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. 
 
 
-## SM J: Code to reproduce text at the beginning of the "Overall reliability" section
+## SM J: Code to reproduce Table 4
 
 Please see code in the RMarkDown version of the document.
 
 
-> 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 tab4, results="as.is"}
+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$metric
+x["lena_CTC_ph","ACLEW ICC"]<-df.icc.mixed[df.icc.mixed$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,]
+
+kable(x,row.names = F,digits=2,caption="Table 4 (reproduced). Most commonly used metrics.")
+#x
+```
+
+
+
+
+## SM K: Code to reproduce text below Table 4 at the beginning of the "Overall reliability" section
+
+Please see code in the RMarkDown version of the document.
+
 
 ```{r best_metric}
 
@@ -524,28 +567,6 @@ 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. The model was overall significant (F(`r round(reg_sum$fstatistic["dendf"],2)`) = `r round(reg_sum$fstatistic["value"],2)`, p < .001). 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 significant predictor (F(`r reg_anova["Type","Df"]`) = `r round(reg_anova["Type","F value"],1)`, p<.001), 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"]`). 
 
 
-## SM O: Code to reproduce Table 4
-
-Please see code in the RMarkDown version of the document.
-
-
-```{r tab4, results="as.is"}
-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$metric
-x["lena_CTC_ph","ACLEW ICC"]<-df.icc.mixed[df.icc.mixed$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,]
-
-kable(x,row.names = F,digits=2,caption="Table 4 (reproduced). Most commonly used metrics.")
-#x
-```
-
-
 
 ## SM P: Code to reproduce text at the beginning of the "Reliability across age groups" section
 
@@ -801,7 +822,7 @@ bias_tab$recXcor<-bias_tab$recXcor/sum(bias_tab$recXcor)
 
 ```
 
-> Our data draws mainly from urban (`r round(sum(bias_tab$recXcor[urban])*100)`% of recordings, `r round(sum(bias_tab$chiXcor[urban])*100)`% of the children, `r round(sum(urban)/length(urban)*100)`% of the corpora), English-speaking settings (`r round(sum(bias_tab$recXcor[english])*100)`% of recordings, `r round(sum(bias_tab$chiXcor[english])*100)`% of the children, `r round(sum(english)/length(english)*100)`% of the corpora), and almost exclusively from North America (`r round(sum(bias_tab$recXcor[northam])*100)`% of recordings, `r round(sum(bias_tab$chiXcor[northam])*100)`% of the children, `r round(sum(northam)/length(northam)*100)`% of the corpora). 
+> Our data draws mainly from urban (`r round(sum(bias_tab$recXcor[urban])*100)`% of recordings, `r round(sum(bias_tab$chiXcor[urban])*100)`% of the children, `r round(sum(urban)/length(urban)*100)`% of the corpora), English-speaking settings (`r round(sum(bias_tab$recXcor[english])*100)`% of recordings, `r round(sum(bias_tab$chiXcor[english])*100)`% of the children, `r round(sum(english)/length(english)*100)`% of the corpora), largely from North America (`r round(sum(bias_tab$recXcor[northam])*100)`% of recordings, `r round(sum(bias_tab$chiXcor[northam])*100)`% of the children, `r round(sum(northam)/length(northam)*100)`% of the corpora). 
 
 ## SM Y: Variability as a function of hardware
 

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+ 89 named destinations out of 1000 (max. 500000)
  30990 words of extra memory for PDF output out of 35830 (max. 10000000)
 

CODE/create-all-rs.R

@@ -17,7 +17,9 @@ mydat_lena = merge(mydat_lena,dist_contig_lena[,c("session_id","next_session")],
 #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 more 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
+#given those two numbers, with 5 draws we'd cover many combinations in winni, lucid, & trio; 
+# but we'll do more 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),]
@@ -49,7 +51,7 @@ mydat_aclew = merge(mydat_aclew,dist_contig_aclew[,c("session_id","next_session"
 nsamples=20
 
 all_rs=data.frame(matrix(NA,nrow=dim(df.icc.mixed)[1],ncol=(nsamples))) 
-# it should have as many rows as there are ICCs, namely number of metrics = 71
+# it should have as many rows as there are ICCs, namely number of metrics = 75
 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
@@ -62,7 +64,7 @@ all_iccs$metric<-df.icc.mixed$metric
 
 #dist_contig_lena[,c("child_id","age","age_dist_next_rec")]
 table(dist_contig_lena[,c("age_dist_next_rec")]) # FYI
-table(dist_contig_lena[,c("age")]) # FYI
+hist(as.numeric(unlist(dist_contig_lena[,c("age")]))) # FYI
 
 for(i in 1:nsamples){#i=1
   

CODE/sessionInfo.txt

@@ -23,20 +23,21 @@ other attached packages:
 [13] lme4_1.1-33           Matrix_1.5-4.1       
 
 loaded via a namespace (and not attached):
- [1] beeswarm_0.4.0    gtable_0.3.3      xfun_0.39         insight_0.19.2   
- [5] rstatix_0.7.2     lattice_0.21-8    vctrs_0.6.3       tools_4.3.0      
- [9] generics_0.1.3    parallel_4.3.0    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_4.3.0    textshaping_0.3.6 munsell_0.5.0    
-[21] mnormt_2.1.1      vipor_0.4.5       htmltools_0.5.5   yaml_2.3.7       
-[25] pillar_1.9.0      nloptr_2.0.3      MASS_7.3-60       boot_1.3-28.1    
-[29] abind_1.4-5       nlme_3.1-162      tidyselect_1.2.0  rvest_1.0.3      
-[33] digest_0.6.33     stringi_1.7.12    purrr_1.0.1       labeling_0.4.2   
-[37] splines_4.3.0     cowplot_1.1.1     fastmap_1.1.1     grid_4.3.0       
-[41] colorspace_2.1-0  cli_3.6.1         magrittr_2.0.3    utf8_1.2.3       
-[45] broom_1.0.5       withr_2.5.0       scales_1.2.1      backports_1.4.1  
-[49] rmarkdown_2.23    httr_1.4.6        gridExtra_2.3     ggsignif_0.6.4   
-[53] ragg_1.2.5        evaluate_0.21     knitr_1.43        viridisLite_0.4.2
-[57] mgcv_1.8-42       rlang_1.1.1       Rcpp_1.0.10       glue_1.6.2       
-[61] xml2_1.3.5        svglite_2.1.1     rstudioapi_0.15.0 minqa_1.2.5      
-[65] 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_0.19.2    rstatix_0.7.2     lattice_0.21-8    vctrs_0.6.3      
+ [9] tools_4.3.0       generics_0.1.3    parallel_4.3.0    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_4.3.0    textshaping_0.3.6
+[21] mnormt_2.1.1      munsell_0.5.0     vipor_0.4.5       htmltools_0.5.5  
+[25] sass_0.4.7        yaml_2.3.7        pillar_1.9.0      nloptr_2.0.3     
+[29] jquerylib_0.1.4   MASS_7.3-60       cachem_1.0.8      boot_1.3-28.1    
+[33] abind_1.4-5       nlme_3.1-162      tidyselect_1.2.0  rvest_1.0.3      
+[37] digest_0.6.33     stringi_1.7.12    purrr_1.0.1       labeling_0.4.2   
+[41] splines_4.3.0     cowplot_1.1.1     fastmap_1.1.1     grid_4.3.0       
+[45] colorspace_2.1-0  cli_3.6.1         magrittr_2.0.3    utf8_1.2.3       
+[49] broom_1.0.5       withr_2.5.0       scales_1.2.1      backports_1.4.1  
+[53] rmarkdown_2.23    httr_1.4.6        gridExtra_2.3     ggsignif_0.6.4   
+[57] ragg_1.2.5        evaluate_0.21     knitr_1.43        viridisLite_0.4.2
+[61] mgcv_1.8-42       rlang_1.1.1       Rcpp_1.0.10       glue_1.6.2       
+[65] xml2_1.3.5        svglite_2.1.1     rstudioapi_0.15.0 minqa_1.2.5      
+[69] jsonlite_1.8.7    R6_2.5.1          systemfonts_1.0.4

data_output/all_rs.csv

@@ -1,376 +1,76 @@
 "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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-0.583500863342014,0.56341739451768,0.586839250414721,0.530343063630888,0.554115872095784,0.544047669288385,0.640942137904335,0.600910963391631,0.52406476423949,0.521060017304864,0.565862866633694,0.515956300954213,0.610912919563559,0.586671495118455,0.573345756475579,0.649623281769695,0.532932093993799,0.540731922992143,0.589930713232514,0.567334897257489,"aclew","avg_cry_voc_dur_chi"
-0.555009670908681,0.583586399095823,0.491305475613682,0.502663957163792,0.505588369564365,0.540648934813805,0.574892671390481,0.613027554316833,0.545039958910585,0.562042078447267,0.483960519625005,0.56292834430224,0.53756987199686,0.506960007842416,0.594164858507186,0.581320093442512,0.568360784084447,0.459811495892549,0.500742946339456,0.470961674146913,"aclew","avg_non_can_voc_dur_chi"
-0.603108847110179,0.652438364589894,0.619188686590074,0.642399089320062,0.65842882365645,0.628077666552184,0.676565438233796,0.641155463242649,0.605347114856744,0.596709990276701,0.546027881007189,0.621518081630193,0.614427187645252,0.68553115316339,0.57921358173119,0.679541104475961,0.581186408402128,0.581900816147946,0.615389914174077,0.59510584204849,"aclew","avg_voc_dur_chi"
-0.437061331612535,0.546607098247521,0.373498857446771,0.418780777620025,0.486583168204571,0.485526468828453,0.500248332630137,0.383019986665134,0.5237449469787,0.506165076286803,0.513812608121882,0.520073768973325,0.502833972171299,0.586500874848478,0.418710411881669,0.470733909117366,0.458622236437926,0.525768583846386,0.411751557541578,0.542386526688872,"aclew","avg_voc_dur_fem"
-0.34205690363917,0.442880406754636,0.519796391490302,0.491433083689546,0.468568416952827,0.483287423617567,0.449701427795879,0.489106714820486,0.504526586974027,0.515805466309508,0.395366833465495,0.527638511180824,0.472939278568,0.487421918701635,0.40619054421642,0.502733863055106,0.577722507790708,0.549443195706567,0.534917485612386,0.488881280235185,"aclew","avg_voc_dur_mal"
-0.690350214729103,0.650071285856013,0.648790251628807,0.685747671342945,0.660204518452614,0.645513680034275,0.601379781603351,0.690301974076448,0.653708191150906,0.698600635959409,0.658003344709924,0.637830920558018,0.672432844305826,0.689311378727803,0.692969204891956,0.68083328495866,0.646643851678661,0.66071668576338,0.725300571699441,0.694753701435535,"aclew","avg_voc_dur_och"
-0.801098123910249,0.779013352988971,0.739309279186926,0.812264614582686,0.820490640570279,0.787869329120326,0.804578695429789,0.795196648952323,0.78160177755542,0.796058787084905,0.782002152392633,0.799422224312673,0.792343953913779,0.809537297193832,0.819508962954372,0.791813922131436,0.805976073193809,0.758760593855271,0.824498821784712,0.817715434497524,"aclew","can_voc_chi_ph"
-0.762470229493643,0.760000245822926,0.699918368487636,0.7814471448864,0.769120228379514,0.745673874127857,0.76819292089,0.780994466353667,0.735448414393642,0.749778059301535,0.691253280716873,0.726817985520017,0.770013451343508,0.800660838581383,0.783022074590453,0.777375639660172,0.754319097245703,0.740281325010194,0.780828790264366,0.757591807390448,"aclew","can_voc_dur_chi_ph"
-0.760253363427766,0.666910812388086,0.659362662581621,0.710705453102384,0.744097775127932,0.742399802864205,0.730580644306645,0.666224433531127,0.691724237102415,0.690060352389976,0.63162373491638,0.627339066869848,0.643657411593276,0.709963336506257,0.723311200331181,0.706396216335111,0.646481580854562,0.71157195184044,0.740942673920095,0.685458825354419,"aclew","cp_dur"
-0.796244432152419,0.718035738635239,0.705536247486034,0.773229185022203,0.768593897882942,0.762030442673198,0.766239741502578,0.745872331851968,0.738039954800821,0.736774027262459,0.681928804752833,0.69781818772845,0.690954105153625,0.764113431894709,0.777501523635639,0.743782449988914,0.720859995015874,0.757112440790421,0.782459884762755,0.752529698025885,"aclew","cp_n"
-0.495030803907562,0.493315671118634,0.466908677080292,0.544546150456564,0.592464845528805,0.526870822130468,0.507121518862983,0.465219236256029,0.537984208662909,0.559433926026323,0.44642297425869,0.511674082665402,0.447847970067897,0.45140142326667,0.587207879262884,0.536446404542855,0.523138874003261,0.399751909172375,0.491596140198644,0.499115356934284,"aclew","cry_voc_chi_ph"
-0.522852457133244,0.563606605189523,0.536065093058066,0.518244689312383,0.591430063293195,0.576739790110789,0.57348840350449,0.480528183779994,0.543601067235463,0.60459359415663,0.483354523868317,0.549601762469355,0.520502294280392,0.495831121913933,0.585586584740731,0.551534940291076,0.533525741661617,0.473438780827435,0.522459572580735,0.535520993513013,"aclew","cry_voc_dur_chi_ph"
-0.556364657277924,0.569096775906363,0.518896018970686,0.483206378579904,0.412584940143778,0.452978952178015,0.529967640673194,0.502266717148032,0.519869179157158,0.53625436658587,0.443245200564825,0.569927420967691,0.483783413517478,0.482646495691115,0.480663513770527,0.475504710413362,0.479923456658723,0.536136441604141,0.435041465795627,0.51177224193138,"aclew","lp_dur"
-0.515688788752308,0.542309052409672,0.490944675718267,0.454168071377254,0.464492494033396,0.420163560392504,0.501343153428419,0.473810449400848,0.509315367811411,0.555283112318298,0.486729108925141,0.538496557852592,0.50761075754214,0.495027485615702,0.465471667315357,0.461759357009416,0.504340523506622,0.443538629718593,0.349014803171965,0.480482004931781,"aclew","lp_n"
-0.626252117648963,0.729607495583924,0.648718616414106,0.700763811661396,0.678249383989521,0.650267874674998,0.723975370868268,0.659406573068626,0.642934155204726,0.708603793271601,0.658954796928821,0.621566388916718,0.599396049864291,0.655880138872611,0.704927667753009,0.633099860445213,0.684112902936648,0.661367703728398,0.703569764750846,0.672777715490825,"aclew","non_can_voc_chi_ph"
-0.606643263833061,0.720250644994242,0.615356375929576,0.674467104444859,0.642668718051216,0.620213743052357,0.70695019700991,0.660871545870006,0.612727805012909,0.676839045197003,0.624730665276679,0.593197917635005,0.586591288373325,0.628277433179362,0.632578512513045,0.633013823411453,0.663079910731587,0.657986525776276,0.677824118397784,0.649788629643789,"aclew","non_can_voc_dur_chi_ph"
-0.603631829210714,0.589403017293206,0.583489686779984,0.557454998084553,0.5622341291018,0.570325326234475,0.680143810031648,0.492108700494507,0.598945078291263,0.593056921755887,0.474954664335555,0.488994168739298,0.573399275237268,0.628100573723797,0.578371591100778,0.504220487657207,0.406347606893138,0.541085068591046,0.59534763443567,0.570178293761996,"aclew","pc_adu_ph"
-0.59139149232511,0.645371698265858,0.53487390073359,0.608837514909207,0.591343959011758,0.602251499086039,0.636294334166652,0.502163725649431,0.544056168874796,0.602650441700476,0.504852930528011,0.472918712607011,0.634230978489406,0.619748345115937,0.617933639180531,0.555215074753585,0.538485680309712,0.607191897941813,0.505716158922985,0.555343137259266,"aclew","pc_fem_ph"
-0.531033629283407,0.463842094476255,0.497435361386951,0.561005370850983,0.464803856793662,0.572869413007277,0.58532951270304,0.492838455515492,0.600794166892415,0.441302245978755,0.448343664919459,0.503443631378163,0.523000407610729,0.553108947457866,0.463166867942332,0.458249936465154,0.428231903601725,0.599019989374238,0.568229174789154,0.479634677664313,"aclew","pc_mal_ph"
-0.655520697218532,0.645393211705228,0.653142749931534,0.683795727648503,0.630409221101938,0.663659264132379,0.624744272906907,0.691971067322469,0.601760190265013,0.689912919003522,0.645533089162996,0.639818035176769,0.623261300987304,0.661852786671964,0.662873468765367,0.689883562057284,0.640633926131661,0.63826108154645,0.678909879006097,0.612686438653602,"aclew","peak_simple_CTC"
-0.632554970688185,0.594326347144652,0.612528448465804,0.668294316271852,0.607134998381571,0.661961790828059,0.632377353348013,0.666914121713413,0.561309213630403,0.643947995938596,0.616113065811954,0.651742583718507,0.596016580558977,0.586952341583998,0.624470252303504,0.649329965614379,0.645318346140761,0.584838017287346,0.677998715975494,0.550460650131994,"aclew","peak_voc_chi"
-0.417507160385446,0.451192269010943,0.329610048324672,0.465424411794533,0.362201802584972,0.343343561916756,0.414278559585331,0.38141275458602,0.341467510318819,0.478787476496903,0.35057490746508,0.413082158874747,0.403622265222556,0.526778907734679,0.432913933100724,0.435874753570261,0.345459734842609,0.414604374232076,0.328751741303804,0.338300003212761,"aclew","peak_voc_fem"
-0.359160380927808,0.328173986530003,0.334327734854978,0.435899455291533,0.361931027596591,0.428326570759005,0.454612022521376,0.308179822682014,0.371699574216582,0.418186781887256,0.20746729438132,0.366060774306298,0.386020681654592,0.359339119142218,0.258609176185606,0.294047358003687,0.270812206024625,0.369480608631135,0.439775885443058,0.434664021414253,"aclew","peak_voc_mal"
-0.726526225729312,0.691275398374798,0.642146368864242,0.682446838457602,0.660646842232706,0.661933743909577,0.672614760761172,0.647860071486746,0.656819195575261,0.715226361282776,0.657018383009679,0.692561503658954,0.660546687891358,0.665838948042883,0.678081586519312,0.731793957331001,0.693424809282188,0.622180055989578,0.699988567233267,0.687176858495227,"aclew","peak_voc_och"
-0.273874030533774,0.276798705968398,0.290267988439095,0.2245158888768,0.122737494800811,0.17642219257172,0.369507877963337,0.101341108789153,0.318291669946921,0.305476400663953,0.229790904568284,0.271946708241364,0.276068621784893,0.302757853594027,0.211876715636762,0.200807213514723,0.163717529280084,0.251237904460042,0.21445767310465,0.244305748874026,"aclew","peak_wc_adu"
-0.289289866081809,0.42687880717693,0.237923650366628,0.31142985573753,0.265647504509225,0.264279382108978,0.417707015235797,0.221082689005034,0.176398389624276,0.284270430129077,0.244601295143861,0.261288769629833,0.277113086504087,0.342867527155415,0.298436534846134,0.35951894404585,0.294466755066619,0.328145414840984,0.176842648382157,0.179236373197996,"aclew","peak_wc_fem"
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input/el1000-metrics

@@ -1 +1 @@
-Subproject commit 87f92d4d62cff98d2226532644ffcd715411de20
+Subproject commit c61d982bf9c7b88394959b6fa1efb3aba58aeb0f

input/laac-metrics

@@ -1 +1 @@
-Subproject commit a19af3e152363beac5128bd2b3337da325908461
+Subproject commit 431b54f0e59b56290fdabe59a5ec626545733be6