knits but have not finished checking accuracy of numbers (did some bug fixes in var names)

CODE/SM.Rmd

@@ -80,7 +80,7 @@ if(RECALC) source("create-all-rs.R")
 
 ```{r readin}
 
-df.icc.simu <- read.csv("../output/df.icc.simu.csv")
+df.icc.simu <- read.csv("../output/df.icc.simu.csv") #365 rows because there are multiple rows per metric as a function of r
 
 mydat_aclew <- read.csv(paste0('../data_output/', 'aclew','_metrics_scaled.csv')) 
 mydat_aclew <- mydat_aclew[is.element(mydat_aclew$experiment, corpora),]
@@ -131,6 +131,7 @@ We used simstudy, a package created for such simulations, following the vignette
 
 plot(df.icc.simu$icc_child_id~df.icc.simu$myr,xlab="r used to simulate the data",ylab="Estimated ICC from simulated data")
 
+
 ```
 
 ## SM B: More information for benchmarking our results against previously reported reliability studies
@@ -198,18 +199,18 @@ nrecs=length(levels(mydat_aclew$session_id))
 # 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
 
 # remove missing data points altogether
-mydat_lena_nomissing <- mydat_lena[!is.na(mydat_lena$peak_wc_adu_ph) & !is.na(mydat_lena$voc_dur_och_ph),]
+mydat_lena_nomissing <- mydat_lena[!is.na(mydat_lena$peak_wc_adu) & !is.na(mydat_lena$voc_dur_och_ph),]
 
 #sample down to get 2 recs per child
 mysample = NULL
 for(thischild in levels(as.factor(mydat_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")]
+  onechidat <- mydat_lena_nomissing[mydat_lena_nomissing$child_id == thischild,c("child_id","experiment","age","child_id","peak_wc_adu","voc_dur_och_ph")]
   
   if(dim(onechidat)[1]>=2){
     selrows=sample(1:dim(onechidat)[1],2)
     mysample = rbind(mysample,
                      cbind(onechidat[selrows[1],],
-                           onechidat[selrows[2],c("child_id","peak_wc_adu_ph","voc_dur_och_ph")]
+                           onechidat[selrows[2],c("child_id","peak_wc_adu","voc_dur_och_ph")]
                      )
     )
   } 
@@ -220,19 +221,19 @@ colnames(mysample) <-
     "corpus",
     "age",
     "child_id1",
-    "peak_wc_adu_ph1",
+    "peak_wc_adu1",
     "voc_dur_och_ph1",
     "child_id2",
-    "peak_wc_adu_ph2",
+    "peak_wc_adu2",
     "voc_dur_och_ph2"
   )
 
 mysample$corpus = factor(mysample$corpus)
 
-mylimits=range(mysample[,c("peak_wc_adu_ph1","peak_wc_adu_ph2")])
+mylimits=range(mysample[,c("peak_wc_adu1","peak_wc_adu2")])
 
 bad <-
-  ggplot(mysample, aes(peak_wc_adu_ph1, peak_wc_adu_ph2)) + 
+  ggplot(mysample, aes(peak_wc_adu1, peak_wc_adu2)) + 
   geom_point(aes(colour = factor(corpus))) +
   geom_smooth(method = 'lm', formula = y ~ x)  +
   labs(color = "Corpus")  +  

CODE/create-all-rs.R

@@ -22,13 +22,16 @@ mydat_lena = merge(mydat_lena,dist_contig_lena[,c("session_id","next_session")],
 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$age),]
 #dim(mydat_aclew)
-# dim(dist_contig_aclew) #686 -- for some reason, we have 2 more eligible recs here... not sure why
+# dim(dist_contig_aclew) #686 --  we have 2 more eligible recs here:
 #length(dist_contig_aclew$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
+#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,]
@@ -45,10 +48,12 @@ 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)))
+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
 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
+# dim(all_rs)
 
 all_iccs=data.frame(matrix(NA,nrow=dim(df.icc.mixed)[1],ncol=(nsamples)))
 colnames(all_iccs[,1:nsamples])<-paste0("sample",1:nsamples)
@@ -66,7 +71,7 @@ for(i in 1:nsamples){#i=1
   #table(close_sessions$experiment) #these have to be similar
   #dim(close_sessions)
   
-  for(j in 1:dim(df.icc.mixed)[1]){# j=1
+  for(j in 1:dim(df.icc.mixed)[1]){# j=1 #this loops over all the metrics
     data_set=df.icc.mixed[j,"data_set"]
     metric=df.icc.mixed[j,"metric"]
     

CODE/sessionInfo.txt

@@ -1,6 +1,6 @@
 R version 4.3.0 (2023-04-21)
 Platform: aarch64-apple-darwin20 (64-bit)
-Running under: macOS Ventura 13.4.1
+Running under: macOS Ventura 13.6.1
 
 Matrix products: default
 BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 

data_output/all_rs.csv

@@ -1,366 +1,72 @@
 "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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