packMBPLSDA (version 0.9.0)

plot_permut_mbplsda: Plot the results of the fonction permut_mbplsda in a pdf file

Description

Fonction to draw the results of the fonction permut_mbplsda (plot and regression line of cross validated prediction error rates, evaluated on the validation datasets, in function of the percent of modified Y-block values) in a pdf file

Usage

plot_permut_mbplsda(obj, filename = "PlotPermutationTest", 
MainPlot = "Permutation test results \n (subset of validation)")

Arguments

obj

object type list containing the results of the fonction permut_mbplsda

filename

a string of characters indicating the given pdf filename

MainPlot

a string of characters indicating the given main title

Value

no numeric result

Details

no details are needed

References

Westerhuis, J.A., Hoefsloot, H.C.J., Smit, S., Vis, D.J., Smilde, A.K., van Velzen, E.J.J., van Duijnhoven, J.P.M., van Dorsten, F.A. (2008). Assessment of PLSDA cross validation. Metabolomics, 4, 81-89.

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 permut_mbplsda packMBPLSDA-package

Examples

# NOT RUN {
data(status)
data(medical)
data(omics)
data(nutrition)
ktabX <- ktab.list.df(list(medical = medical[1:20,], omics = omics[1:20,]))
disjonctif <- (disjunctive(data.frame(status=status[1:20,], 
row.names = rownames(status)[1:20])))
dudiY   <- dudi.pca(disjonctif , center = FALSE, scale = FALSE, scannf = FALSE)
bloYobs <- 2
modelembplsQ <- mbplsda(dudiY, ktabX, scale = TRUE, option = "uniform", scannf = FALSE, nf = 1)
ncpopt <- 1
rtsPermut <- permut_mbplsda(modelembplsQ, nrepet = 30, npermut = 100, optdim = ncpopt, 
outputs = c("ER"), bloY=bloYobs, nbObsPermut = 10, cpus = 1, algo = c("max"))
plot_permut_mbplsda(rtsPermut,"plotPermut_nf1_30rep_100perm")
# }