ngr.data.generator.bsplines

Generating random curves from B-Splines
n,nknots,norder,p,domain=c(0,1),snr,betaind

Implementation of commonly used penalized functional linear regression models, including the Smooth and Locally Sparse (SLoS) method by Lin et al. (2016) <doi:10.1080/10618600.2016.1195273>, Nested Group bridge Regression (NGR) method by Guan et al. (2020) <doi:10.1080/10618600.2020.1713797>, Functional Linear Regression That's interpretable (FLIRTI) by James et al. (2009) <doi:10.1214/08-AOS641>, and the Penalized B-spline regression method.

Haolun Shi

PFLR

Estimating Penalized Functional Linear Regression

Tianyu Guan

Rob Cameron

Zhenhua Lin

ngr.data.generator.bsplines function

<dl><dt>n</dt>
<dd>Number of curves</dd>
<dt>nknots</dt>
<dd>Number of knots</dd>
<dt>norder</dt>
<dd>Degree</dd>
<dt>p</dt>
<dd>Number of time points</dd>
<dt>domain</dt>
<dd>Domain of time</dd>
<dt>snr</dt>
<dd>Signal to noise ratio</dd>
<dt>betaind</dt>
<dd>Numeric index for function</dd></dl>

Arguments

Generating random curves from B-Splines — ngr.data.generator.bsplines

<dl>

<dt>n</dt>
<dd>Number of curves</dd>


<dt>nknots</dt>
<dd>Number of knots</dd>


<dt>norder</dt>
<dd>Degree</dd>


<dt>p</dt>
<dd>Number of time points</dd>


<dt>domain</dt>
<dd>Domain of time</dd>


<dt>snr</dt>
<dd>Signal to noise ratio</dd>


<dt>betaind</dt>
<dd>Numeric index for function</dd>

</dl>

ngr.data.generator.bsplines: Generating random curves from B-Splines

Description

Usage

Value

Arguments