# NOT RUN {
#use the msq data set that shows the structure of energetic and tense arousal
small.msq <- psychTools::msq[ c("active", "energetic", "vigorous", "wakeful",
"wide.awake", "full.of.pep", "lively", "sleepy", "tired", "drowsy","intense",
"jittery", "fearful", "tense", "clutched.up", "quiet", "still", "placid",
"calm", "at.rest") ]
small.R <- cor(small.msq,use="pairwise")
keys.list <- list(
EA = c("active", "energetic", "vigorous", "wakeful", "wide.awake", "full.of.pep",
"lively", "-sleepy", "-tired", "-drowsy"),
TA =c("intense", "jittery", "fearful", "tense", "clutched.up", "-quiet", "-still",
"-placid", "-calm", "-at.rest") ,
high.EA = c("active", "energetic", "vigorous", "wakeful", "wide.awake", "full.of.pep",
"lively"),
low.EA =c("sleepy", "tired", "drowsy"),
lowTA= c("quiet", "still", "placid", "calm", "at.rest"),
highTA = c("intense", "jittery", "fearful", "tense", "clutched.up")
)
keys <- make.keys(small.R,keys.list)
adjusted.scales <- scoreOverlap(keys.list,small.R)
#compare with unadjusted
confounded.scales <- cluster.cor(keys,small.R)
summary(adjusted.scales)
#note that the EA and high and low EA and TA and high and low TA
# scale correlations are confounded
summary(confounded.scales)
bfi.stats <- statsBy(bfi,group="education",cors=TRUE ,cor="cov")
#specify to find covariances
bfi.plus.keys <- c(bfi.keys,gender="gender",age ="age")
bfi.by <- scoreBy(bfi.plus.keys,bfi.stats)
bfi.by$var #to show the variances of each scale by groupl
round(bfi.by$cor.mat,2) #the correlations by group
bfi.by$alpha
# }
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