permut_mbplsda: Permutation testing of a multi-block partial least squares...

In packMBPLSDA: Multi-Block Partial Least Squares Discriminant Analysis

Permutation testing of a multi-block partial least squares discriminant model

Description

Function to perform permutation testing with 2-fold cross-validation for multi-block partial least squares discriminant analysis, in order to evaluate model validity and predictivity

Usage

permut_mbplsda(object, optdim, bloY, algo = c("max", "gravity", "threshold"), 
threshold = 0.5, nrepet = 100, npermut = 100, nbObsPermut = NULL, 
outputs = c("ER", "ConfMat", "AUC"), cpus = 1)

Arguments

object	an object created by mbplsda_nfX
optdim	integer indicating the (optimal) number of components of the multi-block partial least squares discriminant model
bloY	integer vector indicating the number of categories per variable of the Y-block.
algo	character vector indicating the method(s) of prediction to use (see details)
threshold	numeric indicating the threshold, between 0 and 1, to consider the categories are predicted with the threshold prediction method.
nrepet	integer indicating the number of repetitions
npermut	integer indicating the number of Y-block with switching observations
nbObsPermut	integer indicating the number of switching observations in all the modified Y-blocks
outputs	character vector indicating the wanted outputs (see details)
cpus	integer indicating the number of cpus to use when running the code in parallel

Details

Three different algorithms are available to predict the categories of observations. In the max, and respectively the threshold algorithms, numeric values are calculated from the matrix of explanatory variables and the regression coefficients. Then, the predicted categorie for each variable of the Y-block is the one which corresponds to the higher predicted value, respectively to the values higher than the indicated threshold. In the gravity algorithm, predicted scores of the observations on the components are calculated. Then, each observation is assigned to the observed category of which it is closest to the barycentre in the component space.

If nbObsPermut is not NULL, t-test are performed to compare mean cross-validated overall prediction error rates (or aera under ROC curve) evaluated on permuted Y-blocks, with the cross-validated overall prediction error rate (or aera under ROC curve) evaluated on the original Y-block.

Available outputs are Error Rates (ER), Confusion Matrix (ConfMat), Aera Under Curve (AUC).

Value

RV.YYpermut.values	RV coefficient between Y-block and each Y-block with permuted values
cor.YYpermut.values	correlation coefficient between categories in the Y-block and each Y-block with permuted values
prctGlob.Ychange.values	overall percentage of modified values in each Y-block with permuted values
prct.Ychange.values	percentage per category of modified values in each Y-block with permuted values
descrYperm	statistical description of RV.YYpermut, cor.YYpermut, prctGlob.Ychange, prct.Ychange
TruePosC.max, TruePosC.gravity, TruePosC.threshold	statistical description of cross-validated percentages of true positive observations per category, evaluated on calibration datasets, with the different algorithms (TruePosC.max for "max", TruePosC.gravity for "gravity", TruePosC.threshold for "threshold"), for each Y-block with permuted values
TruePosV.max, TruePosV.gravity, TruePosV.threshold	statistical description of cross-validated percentages of true positive observations per category, evaluated on validation datasets, with the different algorithms (TruePosV.max for "max", TruePosV.gravity for "gravity", TruePosV.threshold for "threshold"), for each Y-block with permuted values
TrueNegC.max, TrueNegC.gravity, TrueNegC.threshold	statistical description of cross-validated percentages of true negative observations per category, evaluated on calibration datasets, with the different algorithms (TrueNegC.max for "max", TrueNegC.gravity for "gravity", TrueNegC.threshold for "threshold"), for each Y-block with permuted values
TrueNegV.max, TrueNegV.gravity, TrueNegV.threshold	statistical description of cross-validated percentages of true negative observations per category, evaluated on validation datasets, with the different algorithms (TrueNegV.max for "max", TrueNegV.gravity for "gravity", TrueNegV.threshold for "threshold"), for each Y-block with permuted values
FalsePosC.max, FalsePosC.gravity, FalsePosC.threshold	statistical description of cross-validated percentages of false positive observations per category, evaluated on calibration datasets, with the different algorithms (FalsePosC.max for "max", FalsePosC.gravity for "gravity", FalsePosC.threshold for "threshold"), for each Y-block with permuted values
FalsePosV.max, FalsePosV.gravity, FalsePosV.threshold	statistical description of cross-validated percentages of false positive observations per category, evaluated on validation datasets, with the different algorithms (FalsePosV.max for "max", FalsePosV.gravity for "gravity", FalsePosV.threshold for "threshold"), for each Y-block with permuted values
FalseNegC.max, FalseNegC.gravity, FalseNegC.threshold	statistical description of cross-validated percentages of false negative observations per category, evaluated on calibration datasets, with the different algorithms (FalseNegC.max for "max", FalseNegC.gravity for "gravity", FalseNegC.threshold for "threshold"), for each Y-block with permuted values
FalseNegV.max, FalseNegV.gravity, FalseNegV.threshold	statistical description of cross-validated percentages of false negative observations per category, evaluated on validation datasets, with the different algorithms (FalseNegV.max for "max", FalseNegV.gravity for "gravity", FalseNegV.threshold for "threshold"), for each Y-block with permuted values
ErrorRateC.max, ErrorRateC.gravity, ErrorRateC.threshold	statistical description of cross-validated prediction error rates per category, evaluated on calibration datasets, with the different algorithms (ErrorRateC.max for "max", ErrorRateC.gravity for "gravity", ErrorRateC.threshold for "threshold"), for each Y-block with permuted values
ErrorRateV.max, ErrorRateV.gravity, ErrorRateV.threshold	statistical description of cross-validated prediction error rates per category, evaluated on validation datasets, with the different algorithms (ErrorRateV.max for "max", ErrorRateV.gravity for "gravity", ErrorRateV.threshold for "threshold"), for each Y-block with permuted values
ErrorRateCglobal.max, ErrorRateCglobal.gravity, ErrorRateCglobal.threshold	statistical description of cross-validated overall prediction error rates, evaluated on calibration datasets, with the different algorithms (ErrorRateCglobal.max for "max", ErrorRateCglobal.gravity for "gravity", ErrorRateCglobal.threshold for "threshold"), for each Y-block with permuted values
ErrorRateVglobal.max, ErrorRateVglobal.gravity, ErrorRateVglobal.threshold	statistical description of cross-validated overall prediction error rates, evaluated on validation datasets, with the different algorithms (ErrorRateVglobal.max for "max", ErrorRateVglobal.gravity for "gravity", ErrorRateVglobal.threshold for "threshold"), for each Y-block with permuted values
AUCc	if all Y-block variables are binary, statistical description of cross-validated aera under ROC curve values per category, evaluated on the validation datasets, for each Y-block with permuted values
AUCv	if all Y-block variables are binary, statistical description of cross-validated aera under ROC curve values per category, evaluated on the validation datasets, for each Y-block with permuted values
AUCc.global	if all Y-block variables are binary, statistical description of cross-validated overall aera under ROC curve values, evaluated on the validation datasets, for each Y-block with permuted values
AUCv.global	if all Y-block variables are binary, statistical description of cross-validated overall aera under ROC curve values, evaluated on the validation datasets, for each Y-block with permuted values
reg.GlobalRes_prctYchange	results of linear regression of overall prediction error rates, and overall aera under ROC curve, onto percentages of modified values in Y-block
ttestMeanERv	if nbObsPermut is not NULL, results of the t-test comparing mean cross-validated overall prediction error rates (and eventually aera under ROC curve) evaluated on permuted Y-blocks, with the cross-validated overall prediction error rate (and eventually aera under ROC curve) evaluated on the original Y-block

Note

at least 30 cross-validation repetitions and 100 Y-block with switching observations may be recommended

Author(s)

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

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 plot_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
ncpopt <- 1
modelembplsQ <- mbplsda(dudiY, ktabX, scale = TRUE, option = "uniform", 
scannf = FALSE, nf = 1)
rtsPermut <- permut_mbplsda(modelembplsQ, nrepet = 30, npermut = 100, optdim = ncpopt, 
outputs = c("ER"), bloY = bloYobs, nbObsPermut = 10, cpus=1, algo = c("max"))

packMBPLSDA documentation built on June 20, 2022, 5:08 p.m.