gCoda: conditional dependence network inference for compositional data

A parallelized implementation of the gCoda approach (Fang et al., 2017), published on GitHub (Fang, 2016).

Usage

gcoda(
  x,
  counts = F,
  pseudo = 0.5,
  lambda.min.ratio = 1e-04,
  nlambda = 15,
  ebic.gamma = 0.5,
  cores = 1L,
  verbose = TRUE
)

x: numeric matrix (nxp) with samples in rows and OTUs/taxa in columns.
counts: logical indicating whether x constains counts or fractions. Defaults to FALSE meaning that x contains fractions so that rows sum up to 1.
pseudo: numeric value giving a pseudo count, which is added to all counts if counts = TRUE. Default is 0.5.
lambda.min.ratio: numeric value specifying lambda(max) / lambda(min). Defaults to 1e-4.
nlambda: numberic value (integer) giving the of tuning parameters. Defaults to 15.
ebic.gamma: numeric value specifying the gamma value of EBIC. Defaults to 0.5.
cores: integer indicating the number of CPU cores used for computation. Defaults to 1L. For cores > 1L, foreach is used for parallel execution.
verbose: logical indicating whether a progress indicator is shown (TRUE by default).

A list containing the following elements:

`lambda`	lambda sequence for compuation of EBIC score
`nloglik`	negative log likelihood for lambda sequence
`df`	number of edges for lambda sequence
`path`	sparse pattern for lambda sequence
`icov`	inverse covariance matrix for lambda sequence
`ebic.score`	EBIC score for lambda sequence
`refit`	sparse pattern with best EBIC score
`opt.icov`	inverse covariance matrix with best EBIC score
`opt.lambda`	lambda with best EBIC score

fang2016gcodaGithubNetCoMi

fang2017gcodaNetCoMi

Fang Huaying, Peking University (R-Code and documentation)
Stefanie Peschel (Parts of the documentation; Parallelization)