naiveVariation: Naive (Empirical) Variation

In countprop: Calculate Model-Based Metrics of Proportionality on Count-Based Compositional Data

Naive (Empirical) Variation

Description

Naive (empirical) estimates of proportionality metrics using only the observed counts.

Usage

naiveVariation(
  counts,
  pseudo.count = 0,
  type = c("standard", "phi", "phis", "rho", "logp"),
  impute.zeros = TRUE,
  ...
)

Arguments

counts	Matrix of counts; samples are rows and features are columns
pseudo.count	Positive count to be added to all elements of count matrix.
type	Type of variation metric to be calculated: standard, phi, phis (a symmetric version of phi), rho, or logp (the variance-covariance matrix of log-transformed proportions)
impute.zeros	If TRUE, then cmultRepl() from the zCompositions package is used to impute zero values in the counts matrix.
...	Optional arguments passed to zero-imputation function cmultRepl()

Value

An estimate of the requested metric of proportionality.

Examples

#' data(singlecell)

naiveVariation(singlecell)
naiveVariation(singlecell, type="phi")
naiveVariation(singlecell, type="rho")

countprop documentation built on Aug. 18, 2023, 9:06 a.m.