deviance.margint

This function returns the deviance of the fitted additive model using one of the three
classical or robust marginal integration estimators, as computed with <code>margint.cl</code> or
<code>margint.rob</code>.

Three robust marginal integration procedures for additive models based on local
polynomial kernel smoothers. As a preliminary estimator of the multivariate
function for the marginal integration procedure, a first approach uses local
constant M-estimators, a second one uses local polynomials of order 1 over all the
components of covariates, and the third one uses M-estimators based on local
polynomials but only in the direction of interest. For this last approach,
estimators of the derivatives of the additive functions can be obtained. All three
procedures can compute predictions for points outside the training set if desired.
See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.

Alejandra Martinez

rmargint

Robust Marginal Integration Procedures

Matias Salibian-Barrera

deviance.margint function

<dl><dt>object</dt>
<dd>an object of class <code>margint</code>, a result of a call to <code>margint.cl</code> or <code>margint.rob</code>.</dd>
<dt>...</dt>
<dd>additional other arguments. Currently ignored.</dd></dl>

Arguments

Alejandra Mercedes Martinez <a href="/link/ale_m_martinez%40hotmail.com?package=rmargint&version=2.0.3" data-mini-rdoc="rmargint::ale_m_martinez@hotmail.com">ale_m_martinez@hotmail.com</a>

Author

Deviance for objects of class margint — deviance.margint

<dl>

<dt>object</dt>
<dd>an object of class <code>margint</code>, a result of a call to <code>margint.cl</code> or <code>margint.rob</code>.</dd>


<dt>...</dt>
<dd>additional other arguments. Currently ignored.</dd>

</dl>

Alejandra Mercedes Martinez <a href='mailto:ale_m_martinez@hotmail.com'>ale_m_martinez@hotmail.com</a>

deviance.margint: Deviance for objects of class `margint`

Description

Usage

Value

Arguments

Author