# Crisp-set data.
findOutcomes(d.educate)
findOutcomes(d.educate, con = 0.75, cov = 0.75)
x <- configTable(d.performance[,1:8], frequency = d.performance$frequency)
findOutcomes(x, con = .7, cov = .7) # too computationally demanding
# Approximate by passing approx = TRUE to conCovOpt().
findOutcomes(x, con = .7, cov = .7, approx = TRUE)
# Approximate by passing a case cutoff to configTable().
findOutcomes(x, con = .7, cov = .7, case.cutoff = 10)
# A causal chain.
target1 <- "(A + B <-> C)*(C + D <-> E)"
dat1 <- selectCases(target1)
findOutcomes(dat1)
# A causal cycle.
target2 <- "(A + Y1 <-> B)*(B + Y2 <-> A)*(A + Y3 <-> C)"
dat2 <- selectCases(target2, full.ct(target2))
findOutcomes(dat2)
# Multi-value data.
findOutcomes(d.pban) # no possible outcomes at con = cov = 1
findOutcomes(d.pban, con = 0.8)
findOutcomes(d.pban, con = 0.8, cov= 0.8)
# Fuzzy-set data.
findOutcomes(d.jobsecurity) # no possible outcomes at con = cov = 1
findOutcomes(d.jobsecurity, con = 0.86)
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