Tools for Working With Pairwise Distance Arrays
Description
meandist.from.array
produces a mean distance matrix from an array of
pairwise distances by locus, such as that produced by
meandistance.matrix
when all.distances=TRUE
. find.na.dist
finds missing distances in such an array, and
find.na.dist.not.missing
finds missing distances that aren't the
result of missing genotypes.
Usage
1 2 3 4 5 6 7 8  meandist.from.array(distarray, samples = dimnames(distarray)[[2]],
loci = dimnames(distarray)[[1]])
find.na.dist(distarray, samples = dimnames(distarray)[[2]],
loci = dimnames(distarray)[[1]])
find.na.dist.not.missing(object, distarray,
samples = dimnames(distarray)[[2]], loci = dimnames(distarray)[[1]])

Arguments
distarray 
A threedimensional array of pairwise distances between samples, by
locus. Loci are represented in the first dimension, and samples are
represented in the second and third dimensions. Dimensions are named
accordingly. Such an array is the first element of the list produced by

samples 
Character vector. Samples to analyze. 
loci 
Character vector. Loci to analyze. 
object 
A 
Details
find.na.dist.not.missing
is primarily intended to locate distances
that were not calculated by Bruvo.distance
because both genotypes
had too many alleles (more than maxl
). The user may wish to
estimate these distances manually and fill them into the array, then
recalculate the mean matrix using meandist.from.array
.
Value
meandist.from.array
returns a matrix, with both rows and columns
named by samples, of distances averaged across loci.
find.na.dist
and find.na.dist.not.missing
both return data
frames with three columns: Locus, Sample1, and Sample2. Each row
represents the index in the array of an element containing NA.
Author(s)
Lindsay V. Clark
See Also
meandistance.matrix
, Bruvo.distance
,
find.missing.gen
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  # set up the genotype data
samples < paste("ind", 1:4, sep="")
samples
loci < paste("loc", 1:3, sep="")
loci
testgen < new("genambig", samples=samples, loci=loci)
Genotypes(testgen, loci="loc1") < list(c(9), c(102,104),
c(100,106,108,110,114),
c(102,104,106,110,112))
Genotypes(testgen, loci="loc2") < list(c(77,79,83), c(79,85), c(9),
c(83,85,87,91))
Genotypes(testgen, loci="loc3") < list(c(122,128), c(124,126,128,132),
c(120,126), c(124,128,130))
Usatnts(testgen) < c(2,2,2)
# look up which samples*loci have missing genotypes
find.missing.gen(testgen)
# get the threedimensional distance array and the mean of the array
gendist < meandistance.matrix(testgen, distmetric=Bruvo.distance,
maxl=4, all.distances=TRUE)
# look at the distances for loc1, where there is missing data and long genotypes
gendist[[1]]["loc1",,]
# look up all missing distances in the array
find.na.dist(gendist[[1]])
# look up just the missing distances that don't result from missing genotypes
find.na.dist.not.missing(testgen, gendist[[1]])
# Copy the array to edit the new copy
newDistArray < gendist[[1]]
# calculate the distances that were NA from genotype lengths exceeding maxl
# (in reality, if this were too computationally intensive you might estimate
# it manually instead)
subDist < Bruvo.distance(c(100,106,108,110,114), c(102,104,106,110,112))
subDist
# insert this distance into the correct positions
newDistArray["loc1","ind3","ind4"] < subDist
newDistArray["loc1","ind4","ind3"] < subDist
# calculate the new mean distance matrix
newMeanMatrix < meandist.from.array(newDistArray)
# look at the difference between this matrix and the original.
newMeanMatrix
gendist[[2]]
