pred_mbplsda {packMBPLSDA}

Observed parameters and predicted categories from a multi-block partial least squares discriminant model

Description

Fonction to perform categories predictions from a multi-block partial least squares discriminant model.

Usage

pred_mbplsda(object, optdim , threshold = 0.5, bloY, 
algo = c("max", "gravity", "threshold"))

Arguments

object

an object created by mbplsda

optdim

integer indicating the (optimal) number of components of the multi-block partial least squares discriminant model

threshold

numeric indicating the threshold, between 0 and 1, to consider the categories are predicted with the threshold prediction method.

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)

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.

Value

XYcoef

list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset

VIPc

cumulated variable importances for a given number of dimensions

BIPc

cumulated block importances for a given number of dimensions

faX

matrix containing the global variable loadings associated with the global explanatory dataset

lX

matrix of the global components associated with the whole explanatory dataset(scores of the individuals)

ConfMat.ErrorRate

confidence matrix and prediction error rate per category

ErrorRate.global

confidence matrix and prediction error rate, per Y-block variable and overall

PredY.max

predictions and accuracy of predictions with the "max" algorithm

PredY.gravity

predictions and accuracy of predictions with the "gravity" algorithm

PredY.threshold

predictions and accuracy of predictions with the "threshold" algorithm

AUC

aera under ROC cuve value and 95% confidence interval, per category, per Y-block variable and overall

Author(s)

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

References

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_pred_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)
bloYobs <- 2
ncpopt <- 1
modelembplsQ <- mbplsda(dudiY, ktabX, scale = TRUE, option = "uniform", scannf = FALSE, nf = 2)
predictions <- pred_mbplsda(modelembplsQ, optdim = ncpopt, threshold = 0.5, bloY=bloYobs, 
algo = c("max", "gravity", "threshold"))