predict.margint

This function returns the fitted values given the covariates of the original sample under an additive model using a classical or robust marginal integration procedure estimator 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

predict.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

Fitted values for objects of class margint — predict.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>

predict.margint: Fitted values for objects of class `margint`

Description

Usage

Value

Arguments

Author