allelematch (version 2.5.4)

amAlleleFreq: Determine allele frequencies

Description

Determines the allele frequencies for each locus in a multilocus genetic dataset by first removing missing observations. It requires an amDataset object and a map vector relating each column of the dataset to a genetic locus.

Usage

amAlleleFreq(
		amDatasetFocal, 
		multilocusMap = NULL
		)
		
	# S3 method for amAlleleFreq
print(x, ...)

Value

An amAlleleFreq object

Arguments

amDatasetFocal

An amDataset object

multilocusMap

Optionally, a vector of integers or strings giving the mappings onto loci for all genotype columns in amDatasetFocal.
When omitted, columns are assumed to be paired (i.e., diploid loci with alleles in adjacent columns).
See details.

x

An amDataset object.

...

Additional arguments to summary

Author

Paul Galpern ()

Details

This function is called by amUnique.

multilocusMap is often not required, as amDataset objects will typically consist of paired columns of genotypes, where each pair is a separate locus. In cases where this is not the case (e.g., gender is given in only one column), a map vector must be specified.

Example: amDataset consists of gender followed by 4 diploid loci in paired columns
multilocusMap = c(1, 2, 2, 3, 3, 4, 4, 5, 5)
or equally
multilocusMap=c("GENDER", "LOC1", "LOC1", "LOC2", "LOC2", "LOC3", "LOC4", "LOC4")

References

For more information and for a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at <doi:10.1111/j.1755-0998.2012.03137.x>.

See Also

amUnique

Examples

	if (FALSE) {
	data("amExample5")
	
	## Produce amDataset object
	myDataset1 <- 
		amDataset(
			amExample5, 
			missingCode = "-99", 
			indexColumn = 1, 
			metaDataColumn = 2
			)
	
	## Usage
	myAlleleFreq <- 
		amAlleleFreq(
			myDataset1, 
			multilocusMap = c(1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 
			11, 11)
			)
	
	## Produce amDataset object, but remove gender column
	myDataset2 <- 
		amDataset(
			amExample5, 
			missingCode = "-99", 
			indexColumn = 1,
			metaDataColumn = 2, 
			ignoreColumn = "gender"
			)
	
	## Because all columns are paired, usage is simpler
	myAlleleFreq <- amAlleleFreq(myDataset2)
	}