simulateData2: Simulate bivariate data for funLBM

In funLBM: Model-Based Co-Clustering of Functional Data

Simulate bivariate data for funLBM

Description

Simulate bivariate data according to the funLBM model with K=4 groups for rows and L=3 groups for columns.

Usage

simulateData2(n = 100, p = 100, t = 30)

Arguments

n	The number of rows (individuals) of the simulated data array,
p	The number of columns (functional variables) of the simulated data array,
t	The number of measures for the functions of the simulated data array.

Value

The resulting object contains:

data1	data array of size n x p x t for first variable
data2	data array of size n x p x t for second variable
row_clust	Group memberships of rows
col_clust	Group memberships of columns

References

C. Bouveyron, L. Bozzi, J. Jacques and F.-X. Jollois, The Functional Latent Block Model for the Co-Clustering of Electricity Consumption Curves, Journal of the Royal Statistical Society, Series C, 2018 (https://doi.org/10.1111/rssc.12260).

See Also

funLBM

Examples

# Simulate data and co-clustering
set.seed(12345)
X = simulateData2(n = 50, p = 50, t = 15)


# Co-clustering with funLBM
out = funLBM(list(X$data1,X$data2),K=4,L=3)

# Visualization of results
plot(out,type='blocks')
plot(out,type='proportions')
plot(out,type='means')

# Evaluating clustering results
ari(out$col_clust,X$col_clust)
ari(out$row_clust,X$row_clust)

funLBM documentation built on April 11, 2022, 5:06 p.m.