poppr (version 1.0.5)

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

Description

This function removes any duplicated multi locus genotypes from any specified population hierarchy.

Usage

clonecorrect(pop, hier = c(1), dfname = "population_hierarchy",
  combine = FALSE, keep = 1)

Arguments

pop

a genind object

hier

a numeric or character list. This is the list of vectors within a data frame (specified in dfname) in the 'other' slot of the genind object. The list should indicate the pop

dfname

a character string. This is the name of the data frame or list containing the vectors of the population hierarchy within the other slot of the genind object.

combine

logical. When set to TRUE, the heirarchy will be combined to create a new population for the genind object.

keep

integer. When combine is set to FALSE, you can use this flag to choose the levels of your population hierarchy. For example: if your clone correction hierarchy is set to "Pop", "Subpop", and "Year", and you w

Value

• a clone corrected genind object.

Examples

# LOAD A. euteiches data set
data(Aeut)

# Check the number of multilocus genotypes
mlg(Aeut)
Aeut$pop.names

# Clone correct at the population level.
Aeut.pop <- clonecorrect(Aeut, hier="Pop")
mlg(Aeut.pop)
Aeut.pop$pop.names

# Clone correct at the subpopulation level with respect to population and
# combine.
Aeut.subpop <- clonecorrect(Aeut, hier=c("Pop", "Subpop"), combine=TRUE)
mlg(Aeut.subpop)
Aeut.subpop$pop.names

# Do the same, but set to the population level.
Aeut.subpop2 <- clonecorrect(Aeut, hier=c("Pop", "Subpop"), keep=1)
mlg(Aeut.subpop2)
Aeut.subpop2$pop.names

# LOAD H3N2 dataset
data(H3N2)

# Extract only the individuals located in China
country <- clonecorrect(H3N2, hier=c("country"), dfname="x")

# How many isolates did we have from China before clone correction?
length(which(other(H3N2)$x$country=="China")) # 155

# How many unique isolates from China after clone correction?
length(which(other(country)$x$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, hier=c("year","month","country"), dfname="x")

# How many isolates in the original data set?
length(other(H3N2)$x$country) # 1903

# How many after we clone corrected for country, year, and month?
length(other(c.y.m)$x$country) # 1190