# Analysis of crisp-set data.
cna.educate <- cna(d.educate)
cna.educate
# Print only complex solution formulas.
print(cna.educate, what = "c")
# Print only atomic solution formulas.
print(cna.educate, what = "a")
# Print only minimally sufficient conditions.
print(cna.educate, what = "m")
# Print only the configuration table.
print(cna.educate, what = "t")
# Print solutions with spaces before and after "*".
options(spaces = c("<->", "->", "*" ))
cna(d.educate, details = c("e", "f", "PAcon", "PACcov"))
# Restore the default of the option "spaces".
options(spaces = c("<->", "->", "+"))
# Analysis of multi-value data.
cna.pban <- cna(d.pban, outcome = "PB=1", cov = .95, maxstep = c(6, 6, 10),
what = "all")
cna.pban
# Print only the atomic solution formulas.
print(cna.pban, what = "a", nsolutions = "all")
# Do not print the specification of the evaluation measures.
print(cna.pban, what = "a", nsolutions = "all", printMeasures = FALSE)
# Print further details.
print(cna.pban, nsolutions = "all", details = c("AACcon", "AAcov", "ex", "fa"))
# Print more digits.
print(cna.pban, nsolutions = "all", digits = 6)
# Print the configuration table with the "cases" column.
print(cna.pban, what = "t", show.cases = TRUE, printMeasures = FALSE)
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