LMBootstrapTest: Perform a test on the effects from the model
In FranceschiniS/LMWiRe: R Routines for Linear Model with Wide Response

Description Usage Arguments Details Value References Examples

Compute a partial model without the tested effects then estimate the residuals. Next, compute new outcomes from the predicted values of the partial model and sampled residuals. Finally, compute the Sum of Squares and the test statistic.

1	LMBootstrapTest(ResLMEffectMatrices, nboot = 100)

`ResLMEffectMatrices`	A list of 13 from `LMEffectMatrices`
`nboot`	A integer with the number of iterations to perform (by default: 100)

The function works as follow:

To be written again

A list with the following elements:

Fobs: A vector with the F statistics observed with the data
Fboot: A matrix with the F statistics observed with the bootstrap
Pvalues: A vector with the pvalue for every effect

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

 data('UCH')
 ResLMModelMatrix <- LMModelMatrix(formula=as.formula(UCH$formula),design=UCH$design)
 ResLMEffectMatrices <- LMEffectMatrices(ResLMModelMatrix = ResLMModelMatrix,outcomes=UCH$outcomes)

 result <- LMBootstrapTest(ResLMEffectMatrices = ResLMEffectMatrices,nboot=10)