data_sim {SFPL}

Rank data simulation

Description

Simulates (partial) rank data for multiple groups together with object variables.

Usage

data_sim(m, M, n, p, K, delta, eta)

Arguments

m

Length of the partial ranking for each observation.

M

Total number of objects.

n

Number of observations (rankers) per group.

p

Number of object variables.

K

Number of groups.

delta

Approximate fraction of different coefficients across the \beta^{(k)}.

eta

Approximate fraction of sparse coefficients in \beta^{(k)} for all k.

Value

y

A list consisting of K matrices with each matrix containing (partial) rankings across n observations for group k.

x

A M \times p matrix containing the values for the p objects variables across the M objects.

beta

A p \times K matrix containing the true value of \beta, which was used to generate y.

Author(s)

Sjoerd Hermes
Maintainer: Sjoerd Hermes [e-mail]

References

1. Hermes, S., van Heerwaarden, J., and Behrouzi, P. (2024). Joint Learning from Heterogeneous Rank Data. arXiv preprint, arXiv:2407.10846

Examples

data_sim(3, 10, 50, 5, 2, 0.25, 0.25)