## Not run:
# data(Bluestone)
# BS.fit <- Cline.fit(Bluestone[,1:12], model = c("logit.logistic", "Barton"))
# Cline.plot(BS.fit)
#
# # # parental allele frequencies (assumed diagnostic)
# BS.P <- data.frame(Locus=names(Bluestone),Allele="BTS",P1=1,P2=0)
#
# # # estimate ancestry and heterozygosity
# BS.est <-HIest(Bluestone,BS.P,type="allele.count")
#
# # shortcut for diagnostic markers and allele count data:
# BS.est <- HIC(Bluestone)
#
# # # calculate likelihoods for early generation hybrid classes
# BS.class <- HIclass(Bluestone,BS.P,type="allele.count")
#
# # # compare classification with maximum likelihood estimates
# BS.test <- HItest(BS.class,BS.est)
#
# table(BS.test$c1)
# # # all 41 are TRUE, meaning the best classification is at least 2 log-likelihood units
# # # better than the next best
#
# table(BS.test$c2)
# # # 2 are TRUE, meaning the MLE S and H are within 2 log-likelihood units of the best
# # # classification, i.e., the simple classification is rejected in all but 2 cases
#
# table(BS.test$Best.class,BS.test$c2)
# # # individuals were classified as F2-like (class 3) or backcross to CTS (class 4),
# # # but only two of the F2's were credible
#
# BS.test[BS.test$c2,]
# # # in only one case was the F2 classification a better fit (based on AIC) than the
# # # continuous model.
# ## End(Not run)
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