preprocessData(adata, numLoci, ploidy, dataType, dioecious, selfCompatible = NULL, mothersOnly = NULL, lociMin = 1, matMismatches = 0)2,
4, 6, or 8."genotype" or
"phenotype".dioecious=FALSE), can individuals self-fertilise? When
dioecious=FALSE, this argument may be left at its
default value of NULL - it will be set to FALSE by
preprocessData.dioecious=TRUE, then mothersOnly must be set to
either TRUE or FALSE. If dioecious=FALSE,
argument mothersOnly should be left at its default value of
NULL.numLoci-1,
being the maximum number of mismatching alleles between mother and
offspring that are allowed before the offspring is removed from the
dataset. The default value is 0. If an offspring has fewer
than matMismatches loci that mismatch with its mother, the
offending loci are set to contain no alleles.character.
inputData is used to load the allele data set
into R, then preprocessData will be called automatically on
the data frame before it is returned by inputData.
However, if the user loads their data into R by some means other
than inputData, then preprocessData MUST be
called on the data frame prior to using any other PolyPatEx
functions to analyse the allele data---preprocessData
performs a series of critical checks and preprocessing steps on
the data frame, without which other analysis functions in
PolyPatEx will fail.Note that inputData strips leading or trailing
spaces (whitespace) from each entry in the allele dataset as it is
read in. If you load your data by a means other than
inputData, you should ensure that you perform this
step yourself, as preprocessData will not carry out this
necessary step.
Note also that you should not use spaces in any of your allele codes - PolyPatEx functions use spaces to separate allele codes as they process the data - if allele codes already contains spaces, errors will occur in this processing. If you need a separator, I recommend using either ‘code.’ (a period) or ‘code_’ (an underscore) rather than a space.
preprocessData first performs a number of simple checks on
the format and validity of the data set. These checks look for
the presence of certain required columns and correct naming and
content of these columns. preprocessData will usually stop
with an error message should the data fail these basic checks.
Correct the indicated problem in the CSV file or R allele data
frame, then call inputData or preprocessData
again as appropriate. If you use a spreadsheet to edit the CSV
file, don't forget that you may also need to call
fixCSV on the CSV file, prior to calling
inputData again.
If the data is ‘genotypic’ data PolyPatEx requires that all $p$ alleles must be present in each allele set, where $p$ is the species' ploidy. If an allele set contains fewer than $p$ alleles, then it is reset to contain no alleles and is subsequently ignored by other PolyPatEx functions. ID and locus information is printed to the R terminal, to help the user locate these cases in their original dataset.
Further checks look for mismatches between progeny and their
mothers' allele sets at each locus---these are situations where a
progeny's allele set could not have arisen from any gamete that
the mother can provide. When no more than matMismatches
mismatching loci occurs in a mother-progeny pair, the offending
allele sets in the progeny are reset to contain no alleles (we
term these ‘missing’ allele sets). When mismatches occur
in more than matMismatches loci, the progeny is removed
entirely from the dataset. Information is printed to the R
terminal to assist the user in identifying the affected
individuals and loci---in particular, note that removal of several
(or all) of a single mother's progeny may indicate an error in the
mother's allele data, rather than in her progeny.
After the mother/progeny mismatch check above, a subsequent check
removes individuals from the dataset that have fewer than
lociMin non-missing allele sets remaining. The default
value for lociMin is 1---an individual must have at least
one non-missing locus to remain in the dataset. If any mothers
are removed from the dataset at this stage, all of her progeny are
removed also. Again, information about these removals is printed
to the R terminal.
Note that in the data frame that is returned by
preprocessData, the alleles in each allele set (i.e,
corresponding to each locus) will be sorted into alphanumeric
order---this sort order is necessary for the correct operation of
other PolyPatEx routines, and should not be altered.
PolyPatEx needs to know the characteristics of the dataset being
analysed. These are specified in the inputData or
preprocessData calls and are invisibly attached to the
allele data frame that is returned, for use by other PolyPatEx
functions. The required characteristics are:
numLoci: the number of loci in the dataset
ploidy: the ploidy ($p$) of the species
(currently allowed to be 4, 6, or 8. ploidy can
also be 2, provided dataType="genotype")
dataType: whether the data is genotypic (all $p$
alleles at each locus are observed) or phenotypic (only
the distinct allele states at a locus are observed -
alleles that appear more than once in the genotype of
a locus only appear once in the phenotype)
dioecious: whether the species is dioecious
or monoecious
selfCompatible: whether a monoecious species is
self compatible (i.e., whether an individual can
fertilise itself)
mothersOnly: whether a dioecious dataset should
retain only adult females that are mothers of progeny
in the dataset.
## If using inputData to input the allele dataset from CSV file,
## preprocessData() is applied automatically before the dataset is
## returned by inputData().
## Otherwise, if the allele dataset is created or loaded into R
## by other means, such preprocessData() must be applied before
## other PolyPatEx analysis routines are applied:
## Using the example dataset 'GF_Phenotype':
data(GF_Phenotype)
## Since we did not load this dataset using inputData(), we must
## first process it with preprocessData() before doing anything
## else:
pData <- preprocessData(GF_Phenotype,
numLoci=7,
ploidy=6,
dataType="phenotype",
dioecious=FALSE,
selfCompatible=FALSE)
head(pData) ## Checked and Cleaned version of GF_Phenotype
## pData is now ready to be passed to other PolyPatEx analysis
## functions...
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