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.
optim.pls.cv (Xdata,
Ydata,
ncomp,
constrain=NULL,
scaling = c("centering", "autoscaling","none"),
method = c("plssvd", "simpls"),
svd.method = c("irlba", "dc"),
kfold=10)The output of the result is a list with the following components:
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.
the vector containing the predicted values of the response variables obtained by cross-validation.
the vector containing the fitted values of the response variables.
the (p x max(ncomp)) matrix containing the X-loadings.
the (m x max(ncomp)) matrix containing the Y-loadings.
the (ntrain x max(ncomp)) matrix containing the X-scores (latent components)
the (p x max(ncomp)) matrix containing the weights used to construct the latent components.
predicting power of model.
proportion of variance in Y.
vector containg the explained variance of X by each PLS component.
a summary of the Q2y values.
a summary of the R2y values.
a matrix of independent variables or predictors.
the responses. If Ydata is a numeric vector, a regression analysis will be performed. If Ydata is factor, a classification analysis will be performed.
the number of latent components to be used for classification.
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.
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.
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.
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.
number of cross-validations loops.
Dupe Ojo, Alessia Vignoli, Stefano Cacciatore, Leonardo Tenori
pls,pls.double.cv
# \donttest{
data(iris)
data=iris[,-5]
labels=iris[,5]
pp=optim.pls.cv(data,labels,2:4)
pp$optim_comp
# }
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