Naive (empirical) estimates of proportionality metrics using only the
observed counts.
Usage
naiveVariation(
counts,
pseudo.count = 0,
type = c("standard", "phi", "phis", "rho"),
lr = c("alr", "clr"),
impute.zeros = TRUE,
...
)
Value
An estimate of the requested metric of proportionality.
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)
lr
Which scale to calculate the proportionality metric on, either alr or clr.
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()