# NOT RUN {
# plot correlation matrix using circles
sjt.corr(mydf)
# Data from the EUROFAMCARE sample dataset
library(sjmisc)
data(efc)
# retrieve variable and value labels
varlabs <- get_label(efc)
# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c83cop2")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c88cop7")
# create data frame with COPE-index scale
mydf <- data.frame(efc[, c(start:end)])
colnames(mydf) <- varlabs[c(start:end)]
# we have high correlations here, because all items
# belong to one factor. See example from "sjp.pca".
sjt.corr(mydf, p.numeric = TRUE)
# auto-detection of labels, only lower triangle
sjt.corr(efc[, c(start:end)], triangle = "lower")
# auto-detection of labels, only lower triangle, all correlation
# values smaller than 0.3 are not shown in the table
sjt.corr(efc[, c(start:end)], triangle = "lower", val.rm = 0.3)
# auto-detection of labels, only lower triangle, all correlation
# values smaller than 0.3 are printed in blue
sjt.corr(efc[, c(start:end)], triangle = "lower",val.rm = 0.3,
CSS = list(css.valueremove = 'color:blue;'))
# }
# NOT RUN {
# }
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