boot_mbplsda {packMBPLSDA}

bootstraped simulations for multi-block partial least squares discriminant analysis

Description

Function to perform bootstraped simulations for multi-block partial least squares discriminant analysis, in order to get confidence intervals for regression coefficients, variable loadings, variable and block importances.

Usage

boot_mbplsda(object, nrepet = 199, optdim, cpus = 1, ...)

Arguments

object

an object created by mbplsda

nrepet

integer indicating the number of repetitions

optdim

integer indicating the optimal number of global components to be introduced in the model

cpus

integer indicating the number of cpus to use when running the code in parallel

...

other arguments to be passed to methods

Details

no details are needed

Value

XYcoef

mean, standard deviation, quantiles (0.025;0.975), 95% confidence interval, median for regression coefficients

faX

mean, standard deviation, quantiles (0.025;0.975), 95% confidence interval, median for variable loadings

vipc

mean, standard deviation, quantiles (0.025;0.975), 95% confidence interval, median for cumulated variable importances

bipc

mean, standard deviation, quantiles (0.025;0.975), 95% confidence interval, median for cumulated block importances

Note

at least 30 bootstrap repetitions may be recommended, more than 100 beeing preferable

Author(s)

Marion Brandolini-Bunlon (<[e-mail]>) and Stephanie Bougeard (<[e-mail]>)

References

Efron, B., Tibshirani, R.J. (1994). An Introduction to the Bootstrap. Chapman and Hall-CRC Monographs on Statistics and Applied Probability, Norwell, Massachusetts, United States.

Brandolini-Bunlon, M., Petera, M., Gaudreau, P., Comte, B., Bougeard, S., Pujos-Guillot, E.(2019). A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology. Presented at 12emes Journees Scientifiques RFMF, Clermont-Ferrand, FRA(05-21-2019 - 05-23-2019).

Brandolini-Bunlon, M., Petera, M., Gaudreau, P., Comte, B., Bougeard, S., Pujos-Guillot, E.(2019). Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134

Brandolini-Bunlon, M., Petera, M., Gaudreau, P., Comte, B., Bougeard, S., Pujos-Guillot, E.(2020). A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology. Presented at Chimiometrie 2020, Liege, BEL(01-27-2020 - 01-29-2020).

See Also

mbplsda plot_boot_mbplsda packMBPLSDA-package

Examples

data(status)
data(medical)
data(omics)
data(nutrition)
ktabX <- ktab.list.df(list(medical = medical, nutrition = nutrition, omics = omics))
disjonctif <- (disjunctive(status))
dudiY   <- dudi.pca(disjonctif , center = FALSE, scale = FALSE, scannf = FALSE)
ncpopt <- 1
modelembplsQ <- mbplsda(dudiY, ktabX, scale = TRUE, option = "uniform", scannf = FALSE, nf = 2)
resboot <- boot_mbplsda(modelembplsQ, optdim = ncpopt, nrepet = 30, cpus=1)