| plot_permut_mbplsda {packMBPLSDA} | R Documentation |
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 |
Details
no details are needed
Value
no numeric result
Author(s)
Marion Brandolini-Bunlon (<marion.brandolini-bunlon@inra.fr>) and Stephanie Bougeard (<stephanie.bougeard@anses.fr>)
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
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")