Simulate Genotyping Errors
MkGenoErrors.RdGenerate errors and missing values in a (simulated) genotype matrix.
Usage
MkGenoErrors(
SGeno,
CallRate = 0.99,
SnpError = 5e-04,
ErrorFV = function(E) c((E/2)^2, E - (E/2)^2, E/2),
ErrorFM = NULL,
Error.shape = 0.5,
CallRate.shape = 1,
WithLog = FALSE
)Arguments
- SGeno
matrix with genotype data in Sequoia's format: 1 row per individual, 1 column per SNP, and genotypes coded as 0/1/2.
- CallRate
either a single number for the mean call rate (genotyping success), OR a vector with the call rate at each SNP, OR a named vector with the call rate for each individual. In the third case, ParMis is ignored, and individuals in the pedigree (as id or as parent) not included in this vector are presumed non-genotyped.
- SnpError
either a single value which will be combined with
ErrorFV, or a length 3 vector with probabilities (observed given actual) hom|other hom, het|hom, and hom|het; OR a vector or 3XnSnp matrix with the genotyping error rate(s) for each SNP.- ErrorFV
function taking the error rate (scalar) as argument and returning a length 3 vector with hom->other hom, hom->het, het->hom. May be an 'ErrFlavour', e.g. 'version2.9'.
- ErrorFM
function taking the error rate (scalar) as argument and returning a 3x3 matrix with probabilities that actual genotype i (rows) is observed as genotype j (columns). See below for details. To use, set
ErrorFV = NULL- Error.shape
first shape parameter (alpha) of beta-distribution of per-SNP error rates. A higher value results in a flatter distribution.
- CallRate.shape
as Error.shape, for per-SNP call rates.
- WithLog
Include dataframe in output with which datapoints have been edited, with columns id - SNP - actual (original, input) - observed (edited, output).