check.analytical

This function calculates the finite-difference approximation of the GCV/UBRE gradient for the fitted 
 model and compares it with the analytical gradient.

models

regression

smooth

Generalized additive models under shape
constraints on the component functions of the linear predictor.
Models can include multiple shape-constrained (univariate
and bivariate) and unconstrained terms. Routines of the
package 'mgcv' are used to set up the model matrix, print,
and plot the results. Multiple smoothing parameter
estimation by the Generalized Cross Validation or similar.
See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7>
for an overview. A broad selection of shape-constrained
smoothers, linear functionals of smooths with shape constraints,
and Gaussian models with AR1 residuals.

Natalya Pya

scam

Shape Constrained Additive Models

check.analytical function

<dl> 
 <dt>object</dt>
<dd>A fitted <code>scam</code> object.</dd>
 <dt>data</dt>
<dd>An original data frame or list containing the model response variable and covariates.</dd>
 <dt>del</dt>
<dd>A positive scalar (default is 1e-6) giving an increment for finite difference approximation.</dd>
 <dt>control</dt>
<dd>Control option list as returned by <code>scam.control</code>.</dd></dl>

Arguments

Natalya Pya &lt;nat.pya@gmail.com&gt;

Author

Checking the analytical gradient of the GCV/UBRE score — check.analytical

<dl>

 
 <dt>object</dt>
<dd>A fitted <code>scam</code> object.</dd>


 <dt>data</dt>
<dd>An original data frame or list containing the model response variable and covariates.</dd>


 <dt>del</dt>
<dd>A positive scalar (default is 1e-6) giving an increment for finite difference approximation.</dd>
 

 <dt>control</dt>
<dd>Control option list as returned by <code>scam.control</code>.</dd>

</dl>

Natalya Pya &lt;nat.pya@gmail.com&gt;

check.analytical: Checking the analytical gradient of the GCV/UBRE score

Description

Usage

Value

Arguments

Author

See Also