# Mean Pairwise Distance Matrix Given a `genambig` object, `meandistance.matrix` produces a symmetrical matrix of pairwise distances between samples, averaged across all loci. An array of all distances prior to averaging may also be produced. ```r meandistance.matrix(object, samples = Samples(object), loci = Loci(object), all.distances=FALSE, distmetric = Bruvo.distance, progress = TRUE, ...) meandistance.matrix2(object, samples = Samples(object), loci = Loci(object), freq = simpleFreq(object, samples, loci), self = 0, all.distances = FALSE, distmetric = Bruvo.distance, progress = TRUE, ...) ``` ## Arguments - `object`: A `genambig` object containing the genotypes to be analyzed. If `distmetric = Bruvo.distance`, the `Usatnts` slot should be filled in. For `meandistance.matrix2`, `Ploidies` and `PopInfo` are also required. - `samples`: A character vector of samples to be analyzed. These should be all or a subset of the sample names used in `object`. - `loci`: A character vector of loci to be analyzed. These should be all or a subset of the loci names used in `object`. - `freq`: A data frame of allele frequencies such as that produced by `simpleFreq` or `deSilvaFreq`. - `self`: A number ranging from 0 to 1, indicating the rate of selfing. - `all.distances`: If `FALSE`, only the mean distance matrix will be returned. If `TRUE`, a list will be returned containing an array of all distances by locus and sample as well as the mean distance matrix. - `distmetric`: The function to be used to calculate distances between genotypes. `Bruvo.distance`, `Lynch.distance`, or a distance function written by the user. - `progress`: If `TRUE`, loci and samples will be printed to the console as distances are calculated, so that the user can monitor the progress of the computation. - `...`: Additional arguments (such as `maxl`, `add`, and `loss`) to pass to `distmetric`. ## Returns A symmetrical matrix containing pairwise distances between all samples, averaged across all loci. Row and column names of the matrix will be the sample names provided in the `samples` argument. If `all.distances=TRUE`, a list will be produced containing the above matrix as well as a three-dimensional array containing all distances by locus and sample. The array is the first item in the list, and the mean matrix is the second. ## Details Each distance for the three-dimensional array is calculated only once, to save computation time. Since the array (and resulting mean matrix) is symmetrical, the distance is written to two positions in the array at once. `meandistance.matrix` uses ambiguous genotypes exactly as they are, whereas `meandistance.matrix2` uses `genotypeProbs` to calculate all possible unambiguous genotypes and their probabilities under random mating or partial selfing. The distance between each possible pair of unambiguous genotypes for the two samples is calculated with `distmetric` and weighted by the product of the probabilities of the two gentoypes. As you might expect, `meandistance.matrix2` takes longer to process a given `"genambig"` object than `meandistance.matrix` does. Additionally, the distance between two identical ambiguous genotypes will be zero when calculated with `meandistance.matrix`, and greater than zero when calculated with `meandistance.matrix2`, due to potential differences in copy number of the alleles. When `Bruvo.distance` is used, `meandistance.matrix2` exaggerates distances between individuals of different ploidy as compared to `meandistance.matrix`. The use of `Bruvo2.distance` with `meandistance.matrix2` allows individuals with different ploidies to have similar inter-individual distances to those between individuals of the same ploidy. In general, it will be desirable to use `Bruvo.distance` with `meandistance.matrix` for complex datasets with high ploidy levels, or `Bruvo.distance2` with `meandistance.matrix2` for hexaploid or lower datasets (based on how long it takes my personal computer to perform these calculations) where changes in ploidy are due to genome doubling or genome loss. If all individuals have the same ploidy, `Bruvo.distance` and `Bruvo2.distance` will give identical results regardless of whether `meandistance.matrix` or `meandistance.matrix2` is used. `meandistance.matrix2` does not allow a genotype to have more alleles than the ploidy of the individual (as listed in the `Ploidies` slot). Additionally, if `self` is greater than zero, each population may only have one ploidy at each locus. ## See Also `Bruvo.distance`, `Bruvo2.distance`, `Lynch.distance`, `meandist.from.array`, `GENLIST` ## Examples ```r # create a list of genotype data mygendata <- new("genambig", samples = c("ind1","ind2","ind3","ind4"), loci = c("locus1","locus2","locus3","locus4")) Genotypes(mygendata) <- array(list(c(124,128,138),c(122,130,140,142),c(122,132,136),c(122,134,140), c(203,212,218),c(197,206,221),c(215),c(200,218), c(140,144,148,150),c(-9),c(146,150),c(152,154,158), c(233,236,280),c(-9),c(-9),c(-9))) Usatnts(mygendata) <- c(2,3,2,1) # make index vectors of data to use myloci <- c("locus1","locus2","locus3") mysamples <- c("ind1","ind2","ind4") # calculate array and matrix mymat <- meandistance.matrix(mygendata, mysamples, myloci, all.distances=TRUE) # view the results mymat[[1]]["locus1",,] mymat[[1]]["locus2",,] mymat[[1]]["locus3",,] mymat[[2]] # add addtional info needed for meandistance.matrix2 mygendata <- reformatPloidies(mygendata, output="one") Ploidies(mygendata) <- 4 PopInfo(mygendata) <- c(1,1,1,1) # calculate distances taking allele freqs into account mymat2 <- meandistance.matrix2(mygendata, mysamples, myloci) mymat2 # now do the same under selfing mymat3 <- meandistance.matrix2(mygendata, mysamples, myloci, self=0.3) mymat3 ``` ## Author(s) Lindsay V. Clark

meandistance.matrix function