sample_pspline_coef_2d

Samples from tensor product P-spline prior with additive penalties:
P = (1/wiggliness) * (P_s x I_t + I_s x P_t) + I

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

sample_pspline_coef_2d function

<dl><dt>k_s</dt>
<dd>Basis dimension for first margin.</dd>
<dt>k_t</dt>
<dd>Basis dimension for second margin.</dd>
<dt>wiggliness</dt>
<dd>Controls smoothness.</dd></dl>

Arguments

Sample coefficients from 2D tensor P-spline prior — sample_pspline_coef_2d

<dl>

<dt>k_s</dt>
<dd>Basis dimension for first margin.</dd>


<dt>k_t</dt>
<dd>Basis dimension for second margin.</dd>


<dt>wiggliness</dt>
<dd>Controls smoothness.</dd>

</dl>

Sample coefficients from 2D tensor P-spline prior

sample_pspline_coef_2d: Sample coefficients from 2D tensor P-spline prior

Description

Usage

Value

Arguments