spiec.easi: SPIEC-EASI pipeline

In zdk123/SpiecEasi: Sparse Inverse Covariance for Ecological Statistical Inference

SPIEC-EASI pipeline

Description

Run the whole SPIEC-EASI pipeline, from data transformation, sparse inverse covariance estimation and model selection. Inputs are a non-normalized OTU table and pipeline options.

Usage

spiec.easi(data, ...)

## S3 method for class 'phyloseq'
spiec.easi(data, ...)

## S3 method for class 'otu_table'
spiec.easi(data, ...)

## Default S3 method:
spiec.easi(
  data,
  method = "glasso",
  sel.criterion = "stars",
  verbose = TRUE,
  pulsar.select = TRUE,
  pulsar.params = list(),
  icov.select = pulsar.select,
  icov.select.params = pulsar.params,
  lambda.log = TRUE,
  ...
)

Arguments

data	For a matrix, non-normalized count OTU/data table with samples on rows and features/OTUs in columns. Can also by phyloseq or otu_table object.
...	further arguments to sparseiCov / huge
method	estimation method to use as a character string. Currently either 'glasso' or 'mb' (meinshausen-buhlmann's neighborhood selection)
sel.criterion	character string specifying criterion/method for model selection. Accepts 'stars' [default], 'bstars' (Bounded StARS)
verbose	flag to show progress messages
pulsar.select	flag to perform model selection. Choices are TRUE/FALSE/'batch'
pulsar.params	list of further arguments to pulsar or batch.pulsar. See the documentation for pulsar.params.
icov.select	deprecated.
icov.select.params	deprecated.
lambda.log	should values of lambda be distributed logarithmically (TRUE) or linearly ()FALSE) between lamba.min and lambda.max?

See Also

multi.spiec.easi

zdk123/SpiecEasi documentation built on Oct. 20, 2023, 6:49 a.m.