PEER.Sim

<p><code>PEER.Sim</code> contains simulated observations from 100 subjects, each
observed at 4 distinct timepoints. At each timepoint bumpy predictor
profile is generated randomly and the scalar response variable is generated
considering a time-varying regression function and subject intercept.
Accompanying the functional predictor and scalar response are the subject
ID numbers and time of measurements.</p>

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

PEER.Sim function

<p>The data frame <code>PEER.Sim</code> is made up of subject ID
number(<code>id</code>), subject-specific time of measurement (<code>t</code>),
functional predictor profile (<code>W.1-W.100</code>) and scalar response
(<code>Y</code>)</p>

Format

Simulated longitudinal data with functional predictor and scalar response,
and structural information associated with predictor function — PEER.Sim

Simulated longitudinal data with functional predictor and scalar response,
and structural information associated with predictor function

PEER.Sim: Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function

Description

Arguments

Format

Details

References