opls_cv_cpp: Internal Cpp function for performing leave-group-out...

In resemble: Memory-Based Learning in Spectral Chemometrics

Internal Cpp function for performing leave-group-out cross-validations for pls regression

Description

For internal use only!.

Usage

opls_cv_cpp(X, Y, scale, method, 
                  mindices, pindices, 
                  min_component, ncomp, 
                  new_x, 
                  maxiter, tol, 
                  wapls_grid, 
                  algorithm, 
                  statistics = TRUE)

Arguments

X	a matrix of predictor variables.
Y	a matrix of a single response variable.
scale	a logical indicating whether the matrix of predictors (X) must be scaled.
method	the method used for regression. One of the following options: 'pls' or 'wapls' or 'completewapls1p'.
mindices	a matrix with n rows and m columns where m is equivalent to the number of resampling iterations. The elements of each column indicate the indices of the observations to be used for modeling at each iteration.
pindices	a matrix with k rows and m columns where m is equivalent to the number of resampling iterations. The elements of each column indicate the indices of the observations to be used for predicting at each iteration.
min_component	an integer indicating the number of minimum pls components (if the method = 'pls').
ncomp	an integer indicating the number of pls components.
new_x	a matrix of one row corresponding to the observation to be predicted (if the method = 'wapls').
maxiter	maximum number of iterations.
tol	limit for convergence of the algorithm in the nipals algorithm.
wapls_grid	the grid on which the search for the best combination of minimum and maximum pls factors of 'wapls' is based on in case method = 'completewapls1p'.
algorithm	either pls ('pls') or modified pls ('mpls'). See get_weigths function.
statistics	a logical value indicating whether the precision and accuracy statistics are to be returned, otherwise the predictions for each validation segment are retrieved.

Value

if statistics = true a list containing the following one-row matrices:

• rmse_seg: the RMSEs.

• st_rmse_seg: the standardized RMSEs.

• rsq_seg: the coefficients of determination.

if statistics = false a list containing the following one-row matrices:

• predictions: the predictions of each of the validation segments in pindices. Each column in pindices contains the validation indices of a segment.

• st_rmse_seg: the standardized RMSEs.

• rsq_seg: the coefficients of determination.

If method = "wapls", data of the pls weights are output in this list(compweights).

If method = "completewapls1", data of all the combination of components passed in wapls_grid are output in this list(complete_compweights).

Author(s)

Leonardo Ramirez-Lopez

resemble documentation built on Nov. 5, 2025, 7:08 p.m.