Check parameter values and save as named vector.
SeqPrep(
GenoM = NULL,
LifeHistData = NULL,
nAgeClasses = 1,
MaxSibIter = 5,
Err = 1e-04,
MaxMismatchV = NULL,
Tfilter = -2,
Tassign = 0.5,
MaxSibshipSize = 100,
DummyPrefix = c("F", "M"),
Complexity = "full",
UseAge = "yes",
FindMaybeRel = TRUE,
CalcLLR = TRUE
)matrix with genotype data, size nInd x nSnp
Dataframe with 3 columns:
max. 30 characters long,
1 = females, 2 = males, other numbers = unknown,
(or hatching year) Zero and negative numbers are interpreted as missing values.
Number of age classes (= no. rows in AgePriors)
Maximum number of iterations of sibship clustering (up to 42).
Estimated genotyping error rate.
Maximum number of loci at which (1) a duplicate sample mismatches; (2) candidate parent and offspring are allowed to be opposite homozygotes; (3) parent-parent-offspring trios can have Mendelian errors.
Threshold log-likelihood ratio between a proposed relationship versus unrelated, to select candidate relatives. Typically a negative value, related to the fact that unconditional likelihoods are calculated during the filtering steps. More negative values may decrease non-assignment, but will increase computational time.
Minimum log-likelihood ratio required for acceptance of proposed relationship, relative to next most likely relationship. Higher values result in more conservative assignments.
Maximum number of offspring for a single individual (a generous safety margin is advised).
character vector of length 2 with prefixes for dummy dams (mothers) and sires (fathers); maximum 20 characters each.
Either "full" (default), "simp" (no explicit consideration of inbred relationships), "mono" (monogamous breeding system), or "herm" (hermaphrodites)
Identify pairs of non-assigned likely relatives after pedigree reconstruction. Can be time-consuming in large datasets.
Calculate log-likelihood ratios for all assignments. Can be time-consuming in large datasets.
A 1-row dataframe with parameter values
Please do not increasing the number of SNPs or individuals beyond the numbers present in the datasets, as this may cause R to crash.