# NOT RUN {
# LOAD A. euteiches data set
data(Aeut)
# Redefine it as a genclone object
Aeut <- as.genclone(Aeut)
strata(Aeut) <- other(Aeut)$population_hierarchy[-1]
# Check the number of multilocus genotypes
mlg(Aeut)
popNames(Aeut)
# Clone correct at the population level.
Aeut.pop <- clonecorrect(Aeut, strata = ~Pop)
mlg(Aeut.pop)
popNames(Aeut.pop)
# }
# NOT RUN {
# Clone correct at the subpopulation level with respect to population and
# combine.
Aeut.subpop <- clonecorrect(Aeut, strata = ~Pop/Subpop, combine=TRUE)
mlg(Aeut.subpop)
popNames(Aeut.subpop)
# Do the same, but set to the population level.
Aeut.subpop2 <- clonecorrect(Aeut, strata = ~Pop/Subpop, keep=1)
mlg(Aeut.subpop2)
popNames(Aeut.subpop2)
# LOAD H3N2 dataset
data(H3N2)
strata(H3N2) <- other(H3N2)$x
# Extract only the individuals located in China
country <- clonecorrect(H3N2, strata = ~country)
# How many isolates did we have from China before clone correction?
sum(strata(H3N2, ~country) == "China") # 155
# How many unique isolates from China after clone correction?
sum(strata(country, ~country) == "China") # 79
# Something a little more complicated. (This could take a few minutes on
# slower computers)
# setting the hierarchy to be Country > Year > Month
c.y.m <- clonecorrect(H3N2, strata = ~year/month/country)
# How many isolates in the original data set?
nInd(H3N2) # 1903
# How many after we clone corrected for country, year, and month?
nInd(c.y.m) # 1190
# }
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