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.
spiec.easi(data, ...)# S3 method for phyloseq
spiec.easi(data, ...)
# S3 method for otu_table
spiec.easi(data, ...)
# S3 method for default
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, ...)
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
estimation method to use as a character string. Currently either 'glasso' or 'mb' (meinshausen-buhlmann's neighborhood selection)
character string specifying criterion/method for model selection. Accepts 'stars' [default], 'bstars' (Bounded StARS)
flag to show progress messages
flag to perform model selection. Choices are TRUE/FALSE/'batch'
list of further arguments to pulsar
or batch.pulsar
. See the documentation for pulsar.params
.
deprecated.
deprecated.
multi.spiec.easi