cor.ci(x, keys = NULL, n.iter = 100, p = 0.05, poly = FALSE, method = "pearson")
cluster.cor
but wcluster.cor
or score.items
.Then, n.iter times, the data are recreated by sampling subjects (rows) with replacement and the correlations (and composite scales) are found again (and again and again). Mean and standard deviations of these values are calculated based upon the Fisher Z transform of the correlations. Summary statistics include the original correlations and their confidence intervals. For those who want the complete set of replications, those are available as an object in the resulting output.
Although particularly useful for SAPA (
make.keys
, cluster.cor
, and score.items
for forming synthetic correlation matrices from composites of item correlations. See also corr.test
for standard significance testing of correlation matrices. See also lowerCor
for finding and printing correlation matrices, as well as lowerMat
for displaying them.keys.list <-
list(agree=c("-A1","A2","A3","A4","A5"),conscientious=c("C1","C2","C2","-C4","-C5"),
extraversion=c("-E1","-E2","E3","E4","E5"))
keys <- make.keys(bfi[1:15],keys.list)
cor.ci(bfi[1:15],keys,n.iter=10)
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