Original marker data for hybrid tiger salamander larvae collected from Bluestone Quarry pond.

Description

Each row is an individual, each column is a marker. This is an example type="allele.count". Genotypes are 0 (homozygous for native California Tiger Salamander allele), 1 (heterozygous), or 2 (homozygous for introduced Barred Tiger Salamander allele). There are NA's.

Usage

data(Bluestone)

Format

A data frame with 41 observations on 64 markers.

Source

Fitzpatrick, B. M., J. R. Johnson, D. K. Kump, H. B. Shaffer, J. J. Smith, and S. R. Voss. 2009. Rapid fixation of non-native alleles revealed by genome-wide SNP analysis of hybrid tiger salamanders. BMC Evolutionary Biology 9:176. http://www.biomedcentral.com/1471-2148/9/176

Examples

	## 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)

Results