Description Usage Arguments Details Value Author(s) Examples
Identify the potential fathers for each progeny.
| 1 | potentialFatherIDs(dataset, mismatches = 0, VLTMin = 1)
 | 
| dataset | list: a list structure previously output from
 | 
| mismatches | integer: the maximum allowed number of mismatching loci between candidate and progeny, before the candidate is rejected as a potential father. Default value is 0 - i.e., no mismatches allowed. | 
| VLTMin | integer: the minimum number of ‘valid’ loci (loci at which a valid progeny-candidate comparison was possible) required for a candidate to be considered as a potential father. Default value is 1. | 
Given the output from genotPPE or
phenotPPE, potentialFatherIDs returns, for
each progeny, the IDs of candidates that are identified as
potential fathers.
To decide whether a given candidate is a potential father to a
given progeny, potentialFatherIDs uses the quantities
FLCount (the number of loci at which a candidate can provide a
gamete compatible with the progeny) and VLTotal (the number of
loci at which a valid comparison was possible - ‘valid’
loci) that are returned by genotPPE or
phenotPPE.
For a candidate to be identified as a potential father of a progeny, there are two criteria to be met:
VLTotal >= max(VLTMin,mismatches+1),
FLCount >= VLTotal-mismatches.
Here, VLTmin and mismatches are user-specified
parameters. VLTmin allows the user to ensure that a
candidate is only considered for potential fatherhood if a
sufficient number of valid loci were available for comparison.
mismatches allows the user to specify a maximum number of
allowed mismatching loci between progeny and candidate, before
the candidate is rejected as a potential father.  Hence the user
may wish to relax the condition that ALL valid loci must match for
a candidate to be regarded as a potential father to a progeny.
A data frame, containing the columns Progeny (ID)
Mother (ID), potentialFather (ID or None)
FLCount and VLTotal (the FLCount and
VLTotal values for the given potential father).
Alexander Zwart (alec.zwart at csiro.au)
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## Using the example dataset 'FR_Genotype':
data(FR_Genotype)
## Since we did not load this dataset using inputData(), we must
## first process it with preprocessData() before doing anything
## else:
gData <- preprocessData(FR_Genotype,
                        numLoci=7,
                        ploidy=4,
                        dataType="genotype",
                        dioecious=TRUE,
                        mothersOnly=TRUE)
head(gData)  ## Checked and Cleaned version of FR_Genotype
gPPE <- genotPPE(gData)  ## Perform the exclusion analyses
## Obtain IDs of potential fathers of each seedling, allowing a
## single allele mismatch:
pFI <- potentialFatherIDs(gPPE,mismatches=1,VLTMin=2)
## pFC can be viewed or writted to file via, e.g. write.csv()
 | 
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