packMBPLSDA (version 0.9.0)

plot_boot_mbplsda: Plot the results of the fonction boot_mbplsda in a pdf file

Description

Fonction to draw the results of the fonction boot_mbplsda (2-fold cross-validated parameter values) in a pdf file

Usage

plot_boot_mbplsda(obj, filename = "PlotBootstrapMbplsda", propbestvar = 0.5)

Arguments

obj

object type list containing the results of the fonction boot_mbplsda

filename

a string of characters indicating the given pdf filename

propbestvar

numeric value between 0 and 1, indicating the pourcentage of variables with the best VIPc values to plot

Value

no numeric result

Details

no details are needed

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 boot_mbplsda packMBPLSDA-package

Examples

# NOT RUN {
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)
plot_boot_mbplsda(resboot,"plotBoot_nf1_30rep", propbestvar=0.20)
# }