# NOT RUN {
#data("CPAI537") # Chinese personality assessment inventory (N = 537)
# # Example 1: Oblique geomin rotation with 10 random starts
# res1 <- efaMR(CPAI537, factors = 5, fm = 'ml',
# rtype = 'oblique', rotation = 'geomin',
# geomin.delta = .01, nstart = 10)
# res1
# summary(res1)
# res1$MultipleSolutions
# res1$Comparisons
# In practice, we recommend nstart = 100 or more (Hattori, Zhang, & Preacher, 2018).
# Example 2: Oblique geomin rotation (no random starts)
# compared against CF-varimax and CF-quartimax rotation solutions
# res2 <- efaMR(CPAI537, factors = 5, fm = 'ml',
# rtype = 'oblique', rotation = 'geomin',
# additionalRC = c('CF-varimax', 'CF-quartimax'),
# geomin.delta = .01, nstart = 1)
# res2$MultipleSolutions
# res2$Comparisons
# Example 3: Obtaining multiple solutions from the unrotated factor loading matrix as input
# res3 <- efa(CPAI537, factors = 5, fm = 'ml',
# rtype = 'oblique', rotation = 'geomin')
# set.seed(2017)
# res3MR <- efaMR(input.A = res3$unrotated, rtype = 'oblique',
# rotation = 'geomin', geomin.delta = .01)
# res3MR$MultipleSolutions
# res3MR$Comparisons
# }
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