dmn: Fit Dirichlet-Multinomial models to count data.
In DirichletMultinomial: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Fit Dirichlet-Multinomial models to a sample x taxon count matrix.

1	dmn(count, k, verbose = FALSE, seed = runif(1, 0, .Machine$integer.max))

`count`	`matrix()` of sample x taxon counts.
`k`	`integer(1)`, the number of Dirichlet components to fit.
`verbose`	`logical(1)` indicating whether progress in fit should be reported.
`seed`	`numeric(1)` random number seed.

This implements Dirichlet-multinomial mixture models describe in the package help page, DirichletMultinomial-package.

An object of class dmn, with elements (elements are usually retrieved via functions defined in the package, not directly).

`GoodnessOfFit`	NLE, LogDet, Laplace, AIC, and BIC criteria assessing goodness-of-fit.
`Group`	`matrix` of dimension samples x `k`, providing the Dirichlet parameter vectors.
`Mixture`	Weight `numeric()` of length `k`, with relative weight of each component.
`Fit`	Lower `matrix()` of dimension taxa x `k` with 95% lower bounds on Dirichlet component vector estimates. Estimate `matrix()` of dimension taxa x `k` with Dirichlet component vector estimates. Upper `matrix()` of dimension taxa x `k` with 95% upper bounds on Dirichlet component vector estimates.

Martin Morgan mailto:mtmorgan@fhcrc.org

Holmes I, Harris K, Quince C, 2012 Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE 7(2): e30126. doi:10.1371/journal.pone.0030126.

DirichletMultinomial-package, vignette("DirichletMultinomial")

data(fit)
## k = 1:7; full example in vignette
lplc <- sapply(fit, laplace)
plot(lplc, type="b")
fit[[which.min(lplc)]]

Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min


Attaching package: ‘S4Vectors’

The following object is masked from ‘package:base’:

    expand.grid

Loading required package: IRanges
class: DMN 
k: 4 
samples x taxa: 278 x 130 
Laplace: 38781.1 BIC: 40425.31 AIC: 39476.69