classificationData

<p>Two groups of Gaussian processes with different mean values</p>

datasets

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data
to solve problems of estimating unobserved segments of a partially observed functional data sample,
functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample.
Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria.
The package allows the implementation of the methodology and the replication of the results in El<c3><ad>as, A., Jim<c3><a9>nez, R. and Yukich, J. (2020) <arXiv:2007.16059>.

Antonio El<c3><ad>as

localFDA

Localization Processes for Functional Data Analysis

Raul Jim<c3><a9>nez

Joe Yukich

classificationData function

<p>A matrix with n = 100 functions by columns and t = 200 evaluation points by row.
The first 50 are G1 and second 50 curves are G2 that differs in the mean value.</p>

classificationData: Two groups of Gaussian processes with different mean values

Description

Usage

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Examples