Remove potential bias caused by cloned genotypes in genind or genclone object.
This function removes any duplicated multilocus genotypes from any specified population strata.
a hierarchical formula or numeric vector. This will define the columns of the data frame in the strata slot to use.
This function will clone correct based on the stratification
provided. To clone correct indiscriminately of population structure, set
strata = NA.
a clone corrected
Zhian N. Kamvar
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# 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 ## End(Not run)
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