cclasso: CCLasso: Correlation inference of Composition data through...

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

CCLasso: Correlation inference of Composition data through Lasso method

Description

Implementation of the CCLasso approach (Fang et al., 2015), which is published on GitHub (Fang, 2016). The function is extended by a progress message.

Usage

cclasso(
  x,
  counts = F,
  pseudo = 0.5,
  sig = NULL,
  lams = 10^(seq(0, -8, by = -0.01)),
  K = 3,
  kmax = 5000,
  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.
sig	numeric matrix giving an initial covariance matrix. If NULL (default), diag(rep(1, p)) is used.
lams	numeric vector specifying the tuning parameter sequences. Default is 10^(seq(0, -8, by = -0.01)).
K	numeric value (integer) giving the folds of crossvalidation. Defaults to 3.
kmax	numeric value (integer) specifying the maximum iteration for augmented lagrangian method. Default is 5000.
verbose	logical indicating whether a progress indicator is shown (TRUE by default).

Value

A list containing the following elements:

cov.w	Covariance estimation
cor.w	Correlation estimation
lam	Final tuning parameter

Author(s)

Fang Huaying, Peking University (R code)
Stefanie Peschel (documentation)

References

\insertRef

fang2015cclassoNetCoMi

\insertReffang2016cclassoGithubNetCoMi

stefpeschel/NetCoMi documentation built on Nov. 12, 2024, 7:12 a.m.