pffr_build_call

internal

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

pffr_build_call function

<dl><dt>call</dt>
<dd>Original pffr call.</dd>
<dt>algorithm</dt>
<dd>Algorithm symbol.</dd>
<dt>new_formula</dt>
<dd>Transformed formula.</dd>
<dt>pffr_data</dt>
<dd>Data frame for mgcv.</dd>
<dt>dots</dt>
<dd>Validated dots.</dd>
<dt>use_ar</dt>
<dd>Whether AR(1) errors requested.</dd>
<dt>nobs</dt>
<dd>Number of observations.</dd>
<dt>nyindex</dt>
<dd>Number of y-index points.</dd>
<dt>obs_indices</dt>
<dd>Observation indices.</dd></dl>

Arguments

Build and configure the mgcv call — pffr_build_call

<dl>

<dt>call</dt>
<dd>Original pffr call.</dd>


<dt>algorithm</dt>
<dd>Algorithm symbol.</dd>


<dt>new_formula</dt>
<dd>Transformed formula.</dd>


<dt>pffr_data</dt>
<dd>Data frame for mgcv.</dd>


<dt>dots</dt>
<dd>Validated dots.</dd>


<dt>use_ar</dt>
<dd>Whether AR(1) errors requested.</dd>


<dt>nobs</dt>
<dd>Number of observations.</dd>


<dt>nyindex</dt>
<dd>Number of y-index points.</dd>


<dt>obs_indices</dt>
<dd>Observation indices.</dd>

</dl>

Build and configure the mgcv call

pffr_build_call: Build and configure the mgcv call

Description

Usage

Value

Arguments