gaussian_process_cv

a matrix of a single response variable.

a matrix with <code>n</code> rows and <code>m</code> columns where <code>m</code> 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.

mindices

a matrix with <code>k</code> rows and <code>m</code> columns where <code>m</code> 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.

pindices

a logical indicating whether both the predictors 
and the response variable must be scaled to zero mean and unit variance.

scale

a logical value indicating whether the precision and 
accuracy statistics are to be returned, otherwise the predictions for each 
validation segment are retrieved.

statistics

an integer indicating the number of pls components.

ncomp

internal

Functions for dissimilarity analysis and memory-based learning
(MBL, a.k.a local modeling) in complex spectral data sets.
Most of these functions are based on the methods presented in
Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.

Leonardo RamirezLopez

resemble

Memory-Based Learning in Spectral Chemometrics

gaussian_process_cv function

<dl><dt>X</dt>
<dd>a matrix of predictor variables.</dd>
<dt>Y</dt>
<dd>a matrix of a single response variable.</dd>
<dt>mindices</dt>
<dd>a matrix with <code>n</code> rows and <code>m</code> columns where <code>m</code> 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.</dd>
<dt>pindices</dt>
<dd>a matrix with <code>k</code> rows and <code>m</code> columns where <code>m</code> 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.</dd>
<dt>scale</dt>
<dd>a logical indicating whether both the predictors 
and the response variable must be scaled to zero mean and unit variance.</dd>
<dt>statistics</dt>
<dd>a logical value indicating whether the precision and 
accuracy statistics are to be returned, otherwise the predictions for each 
validation segment are retrieved.</dd>
<dt>ncomp</dt>
<dd>an integer indicating the number of pls components.</dd></dl>

Arguments

Author

Internal Cpp function for performing leave-group-out cross 
validations for gaussian process — gaussian_process_cv

<dl>

<dt>X</dt>
<dd>a matrix of predictor variables.</dd>


<dt>Y</dt>
<dd>a matrix of a single response variable.</dd>


<dt>mindices</dt>
<dd>a matrix with <code>n</code> rows and <code>m</code> columns where <code>m</code> 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.</dd>


<dt>pindices</dt>
<dd>a matrix with <code>k</code> rows and <code>m</code> columns where <code>m</code> 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.</dd>


<dt>scale</dt>
<dd>a logical indicating whether both the predictors 
and the response variable must be scaled to zero mean and unit variance.</dd>


<dt>statistics</dt>
<dd>a logical value indicating whether the precision and 
accuracy statistics are to be returned, otherwise the predictions for each 
validation segment are retrieved.</dd>


<dt>ncomp</dt>
<dd>an integer indicating the number of pls components.</dd>

</dl>

gaussian_process_cv: Internal Cpp function for performing leave-group-out cross validations for gaussian process

Description

Usage

Value

Arguments

Author