Description Usage Arguments Value References See Also Examples

View source: R/simulateData2.R

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

1 | ```
simulateData2(n = 100, p = 100, t = 30)
``` |

`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. |

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 |

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).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# 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)
``` |

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