poppr (version 2.3.0)

clonecorrect: Remove potential bias caused by cloned genotypes in genind or genclone object.

Description

This function removes any duplicated multilocus genotypes from any specified population strata.

Usage

clonecorrect(pop, strata = 1, combine = FALSE, keep = 1)

Arguments

pop

a genind, genclone, or snpclone object

strata

a hierarchical formula or numeric vector. This will define the columns of the data frame in the strata slot to use.

combine

logical. When set to TRUE, the strata will be combined to create a new population for the clone-corrected genind or genclone object.

keep

integer. When combine is set to FALSE, you can use this flag to choose the levels of your population strata. For example: if your clone correction strata is set to "Pop", "Subpop", and "Year", and you want to analyze your populations with respect to year, you can set keep = c(1,3).

Value

a clone corrected genclone, snpclone, or genind object.

Details

This function will clone correct based on the stratification provided. To clone correct indiscriminately of population structure, set strata = NA.

Examples

# 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)

Run the code above in your browser using DataCamp Workspace