dfilter: Filtering datasets for subpopulations with low sample sizes

In ohtadstats: Tomoka Ohta D Statistics

Description

Simplifies the process of eliminating subpopulations with low sample sizes.

Usage

dfilter(data, minsample)

Arguments

data	Matrix containing genotype data with individuals as rows and loci as columns. Genotypes should be coded as 0 (homozygous), 1 (heterozygous), or 2 (homozygous). Rownames must be subpopulation names and column names should be marker names.
minsample	An integer representing the smallest number of individuals a subpopulation must contain to be included in analysis.

Value

filtered_data The original dataset minus the subpopulations that fail to meet the sample size threshold.

Examples

test <- matrix(round(runif(400,1,2)), nrow = 100)
rownames(test) <- c(rep(c('A','B','C'),each=25), rep(c('D','E'), each=5), rep('F', 15))
dim(test)

#The 'D' and 'E' subpopulations have only five members each and should be removed
filtered_test <- dfilter(test,12)

dim(filtered_test)	# New dataset is reduced by 10 rows (five for 'D' and five for 'E')

ohtadstats documentation built on Nov. 15, 2019, 5:11 p.m.