aIc.singular {aIc}

R Documentation

aIc.singular tests for singular data. This is expected to be true if the transform is behaving rationally in compositional datasets and also true in the case of datasets with more features than samples.

Description

aIc.singular tests for singular data. This is expected to be true if the transform is behaving rationally in compositional datasets and also true in the case of datasets with more features than samples.

Usage

aIc.singular(
  data,
  norm.method = "prop",
  zero.remove = 0.95,
  zero.method = "prior",
  log = FALSE,
  group = NULL
)

Arguments

data	can be any dataframe or matrix with samples by column
norm.method	can be prop, clr, RLE, TMM, TMMwsp
zero.remove	is a value. Filter data to remove features that are 0 across at least that proportion of samples: default 0.95
zero.method	can be any of NULL, prior, GBM or CZM. NULL will not impute or change 0 values, GBM (preferred) and CZM are from the zCompositions R package, and prior will simply add 0.5 to all counts.
log	is a logical. log transform the RLE or TMM outputs, default=FALSE
group	is a vector containing group information. Required for clr, RLE, TMM, lvha, and iqlr based normalizations.

Value

Returns a list with a yes/no binary decision in is.singular and the covariance matrix in cov.matrix

Author(s)

Greg Gloor

Examples

data(selex)
group = c(rep('N', 7), rep('S', 7))
x <- aIc.singular(selex, group=group, norm.method='clr', zero.method='prior')

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