optimalRep: Estimation of optimal sample size.

optimalRepR Documentation

Estimation of optimal sample size.

Description

Estimation of the optimal sample size when pilot multi-omic data sets are not available.

Usage

optimalRep(parameters, omicPower = 0.6, averagePower = 0.85, fdr = 0.05, cost = 1, equalSize = TRUE, max.size = 200)

Arguments

parameters

List with as many elements as omic data types. For each omic, each element of this list is another list containing the different parameters needed to compute power which, in this case, must be set by the user. See MultiPower for more details.

omicPower

The minimum power that must be achieved for each omic. It must be a vector with length equal to the number of omics. If it is a single number, this same number will be used for all the omics. By default, omicPower = 0.6.

averagePower

The minimum average power that must be globally achieved. By default, averagePower = 0.85.

fdr

False Discovery Rate level to be used. It is the significance level after multiple testing correction. By default, fdr = 0.05.

cost

The cost to generate a replicate (a sample) for each omic. It must be a vector with length equal to the number of omics. If it is a single number, this same number will be used for all the omics. This argument will only be used when a different sample size per omic is allowed. By default, cost = 1 (which means that all the omics will be assumed to have the same cost).

equalSize

If TRUE (default), the same optimal sample size will be estimated for all the omics. If FALSE, omics are allowed to have different sample sizes.

max.size

Maximum allowed sample size. By default, max.size = 30.

Author(s)

Sonia Tarazona; David Gómez-Cabrero

See Also

MultiPower

Examples

optimalSS = optimalRep(parameters = myparam, omicPower = 0.6,
averagePower = 0.8, fdr = 0.05, 
cost = 1, equalSize = TRUE, max.size = 30)
optimalSS$n  # optimal sample size

ConesaLab/MultiPower documentation built on April 16, 2023, 11:39 a.m.