gcoda: gCoda: conditional dependence network inference for...

In stefpeschel/NetCoMi: Network Construction and Comparison for Microbiome Data

gCoda: conditional dependence network inference for compositional data

Description

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
)

Arguments

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).

Value

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

Author(s)

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

References

\insertRef

fang2016gcodaGithubNetCoMi

\insertReffang2017gcodaNetCoMi

stefpeschel/NetCoMi documentation built on Feb. 4, 2024, 8:20 a.m.