optim.pls.cv {fastPLS}R Documentation

Cross-Validation with PLS-DA.

Description

This function performs a 10-fold cross validation on a given data set using Partial Least Squares (PLS) model. To assess the prediction ability of the model, a 10-fold cross-validation is conducted by generating splits with a ratio 1:9 of the data set. This is achieved by removing 10% of samples prior to any step of the statistical analysis, including PLS component selection and scaling. Best number of component for PLS was carried out by means of 10-fold cross-validation on the remaining 90% selecting the best Q2y value. Permutation testing was undertaken to estimate the classification/regression performance of predictors.

Usage


optim.pls.cv (Xdata,
              Ydata, 
              ncomp, 
              constrain=NULL,
              scaling = c("centering", "autoscaling","none"),
              method = c("plssvd", "simpls"),
              svd.method = c("irlba", "dc"),
              kfold=10)              

Arguments

Xdata

a matrix of independent variables or predictors.

Ydata

the responses. If Ydata is a numeric vector, a regression analysis will be performed. If Ydata is factor, a classification analysis will be performed.

ncomp

the number of latent components to be used for classification.

constrain

a vector of nrow(data) elements. Sample sharing a specific identifier or characteristics will be grouped together either in the training set or in the test set of cross-validation.

scaling

the scaling method to be used. Choices are "centering", "autoscaling", or "none" (by default = "centering"). A partial string sufficient to uniquely identify the choice is permitted.

method

the algorithm to be used to perform the PLS. Choices are "plssvd" or "simpls" (by default = "plssvd"). A partial string sufficient to uniquely identify the choice is permitted.

svd.method

the SVD method to be used to perform the PLS. Choices are "irlba" or "dc" (by default = "irlba"). A partial string sufficient to uniquely identify the choice is permitted.

kfold

number of cross-validations loops.

Value

The output of the result is a list with the following components:

B

the (p x m x length(ncomp)) array containing the regression coefficients. Each row corresponds to a predictor variable and each column to a response variable. The third dimension of the matrix B corresponds to the number of PLS components used to compute the regression coefficients. If ncomp has length 1, B is just a (p x m) matrix.

Ypred

the vector containing the predicted values of the response variables obtained by cross-validation.

Yfit

the vector containing the fitted values of the response variables.

P

the (p x max(ncomp)) matrix containing the X-loadings.

Q

the (m x max(ncomp)) matrix containing the Y-loadings.

T

the (ntrain x max(ncomp)) matrix containing the X-scores (latent components)

R

the (p x max(ncomp)) matrix containing the weights used to construct the latent components.

Q2Y

predicting power of model.

R2Y

proportion of variance in Y.

R2X

vector containg the explained variance of X by each PLS component.

txtQ2Y

a summary of the Q2y values.

txtR2Y

a summary of the R2y values.

Author(s)

Dupe Ojo, Alessia Vignoli, Stefano Cacciatore, Leonardo Tenori

See Also

pls,pls.double.cv

Examples


data(iris)
data=iris[,-5]
labels=iris[,5]
pp=optim.pls.cv(data,labels,2:4)
pp$optim_comp



[Package fastPLS version 0.2 Index]