ccor

Inputs raw data representing two curves, applies penalized B-spline
smoothing to the two curves, and computes the curve correlation between
them via a call to <code>cor.ct</code>.

Implements a basis function or functional data analysis framework
for several techniques of multivariate analysis in continuous-time
setting. Specifically, we introduced continuous-time analogues of
several classical techniques of multivariate analysis, such as
principal component analysis, canonical correlation analysis,
Fisher linear discriminant analysis, K-means clustering, and so
on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025)
"Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

Biplab Paul

ctmva

Continuous-Time Multivariate Analysis

Philip Tzvi Reiss

Noemi Foa

Dror Arbiv

ccor function

<dl><dt>y</dt>
<dd>either a vector or a two-column matrix, without missing values; see Details</dd>
<dt>time</dt>
<dd>a vector of time points</dd>
<dt>curve</dt>
<dd>curve indicator; see Details</dd>
<dt>k</dt>
<dd>number of B-spline basis functions</dd>
<dt>min.overlap</dt>
<dd>minimum overlap of the two curves' time ranges</dd>
<dt>min.n</dt>
<dd>minimum number of observations per curve</dd></dl>

Arguments

Philip Tzvi Reiss &lt;reiss@stat.haifa.ac.il&gt;, Noemi Foa, Dror Arbiv and Biplab Paul &lt;paul.biplab497@gmail.com&gt;

Author

Curve correlation — ccor

<dl>

<dt>y</dt>
<dd>either a vector or a two-column matrix, without missing values; see Details</dd>


<dt>time</dt>
<dd>a vector of time points</dd>


<dt>curve</dt>
<dd>curve indicator; see Details</dd>


<dt>k</dt>
<dd>number of B-spline basis functions</dd>


<dt>min.overlap</dt>
<dd>minimum overlap of the two curves' time ranges</dd>


<dt>min.n</dt>
<dd>minimum number of observations per curve</dd>

</dl>

ccor: Curve correlation

Description

Usage

Value

Arguments

Author

Details

See Also

Examples