computePower: Power estimation

In powerPLS: Power Analysis for PLS Classification

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)

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.

Value

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

Author(s)

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

## Not run: 
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')

## End(Not run)

powerPLS documentation built on April 4, 2025, 1:57 a.m.