source and/or in ID passing the threshold set by the supplied
criteria.
find_hits(data=data, ID= 'LOCUS', source=NULL, values=list(start=11, stop=21), var.cuts=FALSE, low.cut=NULL, high.cut=NULL, cutoff=0.05, Z=NULL, ...)source.data) to be tested
to see if they are a hit.data that are to be used to
determine if an observation is a putative hit.find_hits returns a dataframe containing putative hits and data for
other individuals in their group.
cdf.pval function or
data with Zscores. Suggestions for using pvalue data are given below.
The whole data object can be used, including if there are additional
descriptors. ID refers to the identifier for individuals. Does not
need to be unique. source is optional and contains a list of
identifiers to be test for putative hits. If there are multiple individuals
with the same ID (ex, in the same test group) then over half of them
need to meet the criteria to be a putative hit. values indicates the
columns containing values to evaluate, with start = the position of the first
column and stop = the position of the last column.
If you wish to use a different cutoff for each column, then set
var.cuts = TRUE and supply lists for both low.cut and
high.cut that correspond to the largest value to be considered a hit
on the low side (ex low abundance) and the smallest value to be considered
a hit on the high side (ex high abundance), respectively. Alternatively,
cutoff is used for data coming out of cdf.pval.
cutoff=0.05 then values <=0.025 and="" values="">= 0.975 will be
considered putative hits. If Zscores are provided (or other criteria where
values >= abs(x) are considered a hit), then Z should be used to
define a cutoff.
data are subsetted based on the column (ID) either by all
levels (e.g. group A, group B) or by source, if provided. Each column
in values (e.g. assay) is evaluated to see if any individuals in that
column meet the criteria for a putative hit. If more than half of the
individuals meet the criteria to be a putative hit for that column, all the
individuals belonging to that level are put into the output data frame. If
not, then the remaining columns are evaluated or it moves to the next level.
Individual responses that are low or high are evaluated separately.
#See the sweave document in the corresponding paper for examples
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