Tests the significance of model effects by bootstrap.

Tests the significance of the effects from the model using bootstrap. This function is based on the outputs of lmpEffectMatrices. Tests on combined effects are also provided.

Usage

lmpBootstrapTests(
  resLmpEffectMatrices,
  nboot = 100,
  nCores = 2,
  verbose = FALSE
)

Arguments

resLmpEffectMatrices

A list of 12 from lmpEffectMatrices.

nboot

An integer with the number of bootstrap sample to be drawn.

nCores

The number of cores to use for parallel execution.

verbose

If TRUE, will display a message with the duration of execution.

Value

A list with the following elements:

f.obs

A vector of size F (number of effects in the model) with the F statistics for each model term calculated on the initial data.

f.boot

b × F matrix with the F statistics calculated on the bootstrap samples.

p.values

A vector of size F with the p-value for each model effect.

resultsTable

A 2 × F matrix with the p-value and the percentage of variance for each model effect.

References

Thiel M.,Feraud B. and Govaerts B. (2017) ASCA+ and APCA+: Extensions of ASCA and APCA in the analysis of unbalanced multifactorial designs, Journal of Chemometrics

Thiel, M., Benaiche, N., Martin, M., Franceschini, S., Van Oirbeek, R., & Govaerts, B. (2023) limpca: an R package for the linear modeling of high dimensional designed data based on ASCA/APCA family of methods, Journal of Chemometrics

Examples

data("UCH")
resLmpModelMatrix <- lmpModelMatrix(UCH)
resLmpEffectMatrices <- lmpEffectMatrices(resLmpModelMatrix = resLmpModelMatrix)

res <- lmpBootstrapTests(
  resLmpEffectMatrices = resLmpEffectMatrices,
  nboot = 10, nCores = 2, verbose = TRUE
)
#> 7.29387855529785