sasfunclust-package: Sparse and smooth functional data clustering

In sasfunclust: Sparse and Smooth Functional Clustering

Description

Implements the sparse and smooth functional clustering (SaS-Funclust) method (Centofanti et al. (2021) <arXiv:2103.15224>) that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of domain.

Details

Package:	sasfunclust
Type:	Package
Version:	1.0.0
Date:	2021-04-01
License:	GPL-3

Author(s)

Fabio Centofanti, Antonio Lepore, Biagio Palumbo

References

Centofanti, F., Lepore, A., & Palumbo, B. (2021). Sparse and Smooth Functional Data Clustering. arXiv preprint arXiv:2103.15224.

See Also

sasfclust, sasfclust_cv

Examples

## Not run: 

n_i=20
train<-simulate_data("Scenario I",n_i=n_i,sd = 1,sd2_basis = 0.5^2)
lambda_s_seq=10^seq(-4,-3)
lambda_l_seq=10^seq(-1,0)
G_seq=2
mod_cv<-sasfclust_cv(X=train$X,grid=train$grid,G_seq=G_seq,
lambda_l_seq = lambda_l_seq,lambda_s_seq =lambda_s_seq,maxit = 5,K_fold = 2,q=10)
plot(mod_cv)

mod<-sasfclust(X=train$X,grid=train$grid,G_seq=mod_cv$G_opt,
lambda_l = mod_cv$lambda_l_opt,lambda_s_seq =mod_cv$lambda_s_opt,maxit = 5,q=10)

print(aa$clus$classes)
plot(mod)

## End(Not run)

sasfunclust documentation built on April 2, 2021, 5:06 p.m.