compute_pls_cv_metrics

Internal function to compute CV metrics for each component number.

internal

Provides tools for analyzing the relationship between direct
prices (based on labor values) and prices of production using Bayesian
generalized linear models, panel data methods, partial least squares
regression, canonical correlation analysis, and panel vector
autoregression. Includes functions for model comparison, out-of-sample
validation, and structural break detection. Here, methods use raw accounting data with explicit temporal structure, following Gomez Julian (2023) <doi:10.17605/OSF.IO/7J8KF>
and standard econometric techniques for panel data analysis.

José Mauricio Gómez Julián

valueprhr

Value-Price Analysis with Bayesian and Panel Data Methods

Jose Mauricio Gomez Julian

compute_pls_cv_metrics function

<dl><dt>Y_actual</dt>
<dd>Actual Y matrix.</dd>
<dt>cv_predictions</dt>
<dd>3D array of CV predictions [obs, vars, components].</dd>
<dt>ncomp</dt>
<dd>Number of components fitted.</dd></dl>

Arguments

Compute PLS Cross-Validation Metrics — compute_pls_cv_metrics

<dl>

<dt>Y_actual</dt>
<dd>Actual Y matrix.</dd>


<dt>cv_predictions</dt>
<dd>3D array of CV predictions [obs, vars, components].</dd>


<dt>ncomp</dt>
<dd>Number of components fitted.</dd>

</dl>

compute_pls_cv_metrics: Compute PLS Cross-Validation Metrics

Description

Usage

Value

Arguments