powerPLS (version 0.2.1)

computePower: Power estimation

Description

Estimates power for a given sample size, type I error level and number of score components.

Usage

computePower(X, Y, A, n, seed = 123,
Nsim = 100, nperm = 200, alpha = 0.05,
scaling = 'auto-scaling', test = 'R2',
Y.prob = FALSE, eps = 0.01, post.transformation = TRUE,
fast = FALSE, transformation = 'clr', ncores = NULL)

Value

Returns a matrix of estimated power for each number of components and tests selected.

Arguments

X

Data matrix where columns represent the \(p\) variables and rows the \(n\) observations.

Y

Data matrix where columns represent the two classes and rows the \(n\) observations.

A

Number of score components

n

Sample size

seed

Seed value

Nsim

Number of simulations

nperm

Number of permutations

alpha

Type I error level

scaling

Type of scaling, one of c('auto-scaling', 'pareto-scaling', 'mean-centering'). Default to 'auto-scaling'

test

Type of test statistic, one of c('score', 'mcc', 'R2'). Default to 'R2'.

Y.prob

Boolean value. Default FALSE. IF TRUE Y is a probability vector

eps

Default 0.01. eps is used when Y.prob = FALSE to transform Y in a probability vector.

post.transformation

Boolean value. TRUE if you want to apply post transformation. Default to TRUE

fast

Use the function fk_density from the FKSUM R package for kernel density estimation. Default to FALSE.

transformation

Transformation used to map Y in probability data vector. The options are 'ilr' and 'clr'.

ncores

Number of cores, default NULL.

Author

Angela Andreella

References

For the general framework of power analysis for PLS-based methods see:

Andreella, A., Fino, L., Scarpa, B., & Stocchero, M. (2024). Towards a power analysis for PLS-based methods. arXiv preprint https://arxiv.org/abs/2403.10289.

Examples

if (FALSE) {
datas <- simulatePilotData(nvar = 10, clus.size = c(5,5),m = 6,nvar_rel = 5,A = 2)
out <- computePower(X = datas$X, Y = datas$Y, A = 3, n = 20, test = 'R2')
}