Predict.matrix.pcre.random.effect

mgcv-style constructor for prediction of PC-basis functional random effects

Methods for regression for functional
data, including function-on-scalar, scalar-on-function, and
function-on-function regression. Some of the functions are applicable to
image data.

Julia Wrobel

refund

Regression with Functional Data

Jeff Goldsmith

Fabian Scheipl

Lei Huang

Chongzhi Di

Jonathan Gellar

Jaroslaw Harezlak

Bruce Swihart

Luo Xiao

Ciprian Crainiceanu

Yakuan Chen

Sonja Greven

Lan Huo

So Young Park

Ana-Maria Staicu

Erjia Cui

Ruonan Li

Zheyuan Li

Predict.matrix.pcre.random.effect function

<dl><dt>object</dt>
<dd>a smooth specification object, see <code><a href="/link/smooth.construct?package=refund&version=0.1-37" data-mini-rdoc="refund::smooth.construct">smooth.construct</a></code></dd>
<dt>data</dt>
<dd>see <code><a href="/link/smooth.construct?package=refund&version=0.1-37" data-mini-rdoc="refund::smooth.construct">smooth.construct</a></code></dd></dl>

Arguments

Fabian Scheipl; adapted from 'Predict.matrix.random.effect' by S.N. Wood.

Author

mgcv-style constructor for prediction of PC-basis functional random effects — Predict.matrix.pcre.random.effect

<dl>

<dt>object</dt>
<dd>a smooth specification object, see <code><a href='https://rdrr.io/pkg/mgcv/man/smooth.construct.html'>smooth.construct</a></code></dd>


<dt>data</dt>
<dd>see <code><a href='https://rdrr.io/pkg/mgcv/man/smooth.construct.html'>smooth.construct</a></code></dd>

</dl>

mgcv-style constructor for prediction of PC-basis functional random effects

Predict.matrix.pcre.random.effect: mgcv-style constructor for prediction of PC-basis functional random effects

Description

Usage

Value

Arguments

Author