gaussian_process_cv

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></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>

</dl>

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

Description

Usage

Value

Arguments

Author