crossv_GeDS

<code>crossv_GeDS</code> performs k-fold cross-validation for tuning the relevant
parameters of the <code>NGeDS</code>, <code>GGeDS</code>, <code>NGeDSgam</code>, and
<code>NGeDSboost</code> models.

Spline Regression, Generalized Additive Models, and Component-wise Gradient
Boosting, utilizing Geometrically Designed (GeD) Splines. GeDS regression is a
non-parametric method inspired by geometric principles, for fitting spline regression
models with variable knots in one or two independent variables. It efficiently estimates
the number of knots and their positions, as well as the spline order, assuming the
response variable follows a distribution from the exponential family. GeDS models
integrate the broader category of Generalized (Non-)Linear Models, offering a flexible
approach to modeling complex relationships. A description of the method can be found in
Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
<doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation
includes Generalized Additive Models (GAM) and Functional Gradient Boosting (FGB),
enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of
Dimitrova et al. (2024).

Emilio Guillen

GeDS

Geometrically Designed Spline Regression

Andrea Lattuada

crossv_GeDS function

<dl><dt>formula</dt>
<dd>a description of the structure of the model structure,
including the dependent and independent variables.</dd>
<dt>data</dt>
<dd>a data frame containing the variables referenced in the formula.</dd>
<dt>model_fun</dt>
<dd>the GeDS model to be fitted, that is, <code>NGeDS</code>,
<code>GGeDS</code>, <code>NGeDSgam</code> or <code>NGeDSboost</code>.</dd>
<dt>parameters</dt>
<dd>to tune via cross-validation. These are: <code>beta</code>, <code>phi</code> and
<code>q</code> in the case of <code>NGeDS</code>, <code>GGeDS</code> and <code>NGeDSgam</code>. In
addition, for <code>NGeDSboost</code>, <code>int.knots_init</code> and <code>shrinkage</code>
can also be tuned. Default values are <code>int.knots_init_grid = c(0, 1, 2)</code>,
<code>shrinkage_grid = c(0.1, 0.5, 1)</code>, <code>beta_grid = c(0.3, 0.5, 0.7)</code>,
<code>phi_grid = c(0.9, 0.95, 0.99)</code>, <code>q_grid = c(2, 3))</code>.</dd></dl>

Arguments

k-fold cross-validation — crossv_GeDS

<dl>

<dt>formula</dt>
<dd>a description of the structure of the model structure,
including the dependent and independent variables.</dd>


<dt>data</dt>
<dd>a data frame containing the variables referenced in the formula.</dd>


<dt>model_fun</dt>
<dd>the GeDS model to be fitted, that is, <code>NGeDS</code>,
<code>GGeDS</code>, <code>NGeDSgam</code> or <code>NGeDSboost</code>.</dd>


<dt>parameters</dt>
<dd>to tune via cross-validation. These are: <code>beta</code>, <code>phi</code> and
<code>q</code> in the case of <code>NGeDS</code>, <code>GGeDS</code> and <code>NGeDSgam</code>. In
addition, for <code>NGeDSboost</code>, <code>int.knots_init</code> and <code>shrinkage</code>
can also be tuned. Default values are <code>int.knots_init_grid = c(0, 1, 2)</code>,
<code>shrinkage_grid = c(0.1, 0.5, 1)</code>, <code>beta_grid = c(0.3, 0.5, 0.7)</code>,
<code>phi_grid = c(0.9, 0.95, 0.99)</code>, <code>q_grid = c(2, 3))</code>.</dd>

</dl>

crossv_GeDS: k-fold cross-validation

Description

Value

Arguments