pplscv_cpp {resemble}R Documentation

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

Description

For internal use only!.

Usage

pplscv_cpp(X, Y, scale, method, 
                  mindices, pindices, 
                  minF, ncomp, 
                  newX, 
                  maxiter, tol, 
                  waplsgrid)

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 'wapls1' 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 samples 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 samples to be used for predicting at each iteration.

minF

an integer indicating the number of minimum pls components (if the method = 'pls').

ncomp

an integer indicating the number of pls components.

newX

a matrix of one row corresponding to the sample to be predicted (if the method = 'wapls1').

maxiter

maximum number of iterations.

tol

limit for convergence of the algorithm in the nipals algorithm.

waplsgrid

the grid on which the search for the best combination of minimum and maximum pls factors of 'wapls1' is based on in case method = 'completewapls1p'.

Value

a list containing the following one-row matrices:

Author(s)

Leonardo Ramirez-Lopez


[Package resemble version 1.2.2 Index]