Fit Dirichlet-Multinomial models to count data.

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

Usage

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

Arguments

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.

Details

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

Value

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.

References

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.

Author

Martin Morgan mailto:mtmorgan.xyz@gmail.com

Examples

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

fit[[which.min(lplc)]]
#> class: DMN 
#> k: 4 
#> samples x taxa: 278 x 130 
#> Laplace: 38781.1 BIC: 40425.31 AIC: 39476.69