data(fake.bc)
fake.bc <- calc.genoprob(fake.bc, step=5)
# genome scan by Haley-Knott regression
out <- scanone(fake.bc, method="hk")
# permutation tests
## Not run: operm <- scanone(fake.bc, method="hk", n.perm=1000)
# peaks for all chromosomes
summary(out)
# results with LOD >= 3
summary(out, threshold=3)
# the same, but also showing the p-values
summary(out, threshold=3, perms=operm, pvalues=TRUE)
# results with LOD meeting the 0.05 threshold from the permutation results
summary(out, perms=operm, alpha=0.05)
# the same, also showing the p-values
summary(out, perms=operm, alpha=0.05, pvalues=TRUE)
##### summary with multiple phenotype results
out2 <- scanone(fake.bc, pheno.col=1:2, method="hk")
# permutations
## Not run: operm2 <- scanone(fake.bc, pheno.col=1:2, method="hk", n.perm=1000)
# results with LOD >= 2 for the 1st phenotype and >= 1 for the 2nd phenotype
# using format="allpheno"
summary(out2, thr=c(2, 1), format="allpheno")
# The same with format="allpeaks"
summary(out2, thr=c(2, 1), format="allpeaks")
# The same with p-values
summary(out2, thr=c(2, 1), format="allpeaks", perms=operm2, pvalues=TRUE)
# results with LOD meeting the 0.05 significance level by the permutations
# using format="allpheno"
summary(out2, format="allpheno", perms=operm2, alpha=0.05)
# The same with p-values
summary(out2, format="allpheno", perms=operm2, alpha=0.05, pvalues=TRUE)
# The same with format="allpeaks"
summary(out2, format="allpeaks", perms=operm2, alpha=0.05, pvalues=TRUE)
# format="tabByCol"
summary(out2, format="tabByCol", perms=operm2, alpha=0.05, pvalues=TRUE)
# format="tabByChr", but using bayes intervals
summary(out2, format="tabByChr", perms=operm2, alpha=0.05, pvalues=TRUE,
ci.function="bayesint")
# format="tabByChr", but using 99% bayes intervals
summary(out2, format="tabByChr", perms=operm2, alpha=0.05, pvalues=TRUE,
ci.function="bayesint", prob=0.99)
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