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@@ -55,7 +55,7 @@ The pdf version of this document was generated using an RMarkDown document, whic
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> Quoted text indicates sections that appear verbatim in the main manuscript.
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-In contrast, unmarked text are additional texts and analyses, as well as comments for the reader who is attempting to reproduce our analyses. Readers attempting to reproduce our analyses are asked to also read comments in chunks of code in the RMarkDown version of the document.
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+In contrast, unmarked text are additional texts and analyses, as well as comments for the reader who is attempting to reproduce our analyses. Readers attempting to reproduce our analyses are asked to also read comments in chunks of code in the RMarkDown version of the document, which are invisible in the pdf version.
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```{r recalc-all}
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@@ -266,7 +266,7 @@ ggarrange(bad, good,
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```{r gen-table}
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-#summary(all_rs)
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+#this code is a bit ugly-looking: 1:20 indicates the columns that contain the r coefficients for the 20 samples we drew
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rval_tab=cbind(apply(all_rs[,1:20],1,mean),apply(all_rs[,1:20],1,sd),all_rs[,c("data_set","metric")])
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colnames(rval_tab) <-c("m","sd","data_set","metric")
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rval_tab[,1:2]=round(rval_tab[,1:2],2)
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@@ -315,7 +315,7 @@ cor_t=t.test(rval_tab$m ~ rval_tab$data_set)
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```
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-> 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"]`).
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+> 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. Although the model was overall significant (F 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"]`).
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See table below for results of the Type 3 ANOVA.
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