Description Usage Arguments Details Value See Also Examples

View source: R/generate_data.R

This function generates data from the bipartite logitudinal influence network (BLIN) model *Y_t = A^T ∑_{k=1}^{lag} Y_{t-k} + ∑_{k=1}^{lag} Y_{t-k} B + X_t β + τ E_t*.

1 2 3 | ```
generate_blin(S, L, tmax, lag = 1, tau = 1, sigmaY = 1, muAB = 0,
sigmaAB = 1, rankA = S, rankB = L, use_cov = TRUE, seed = NA,
sparse = NA)
``` |

`S` |
Dimension of A. |

`L` |
Dimension of B. |

`tmax` |
Number of observations of relational data. |

`lag` |
Autoregressive lag in model, defaults to 1. |

`tau` |
Optional error standard deviatiom, defaults to 1. |

`sigmaY` |
Optional standard deviation of entries in |

`muAB` |
Optional mean of entries in decomposition of matrices |

`sigmaAB` |
Optional standard deviation of entries in decomposition matrices of |

`rankA` |
Rank of influence network matrix |

`rankB` |
Optional rank of influence network matrix |

`use_cov` |
Optional logical used to indicate whether to include |

`seed` |
Optional numeric to set seed before generating, defaults to NA (no seed set). |

`sparse` |
Optional degree of sparsity in A and B, i.e. |

This function generates a continuous bipartite longitudinal relational data set from the BLIN model,
*Y_t = A^T ∑_{k=1}^{lag} Y_{t-k} + ∑_{k=1}^{lag} Y_{t-k} B + X_t β + τ E_t*, where * \{ Y_t \}_t * is a set of *S \times L* matrices representing the bipartite relational data at each observation *t*.
The set *\{X_t \}_t* is a set of *S \times L \times p* arrays describing the influence of the
coefficient vector *beta*. Finally, each matrix *E_t* consists of iid standard normal random variables.

The matrices *A* and *B* are square matrices respesenting the influence networks among *S* senders and *L* receivers, respectively. The matrix *A* has decomposition *A = UV^T*, where each of *U* and *V* is an *S \times {rankA}* matrix of iid standard normal random variables with mean `muAB`

and standard deviation `sigmaAB`

.
Similarly, the matrix *B* has decomposition *B = WZ^T*, where each of *W* and *Z* is an *L \times {rankB}* matrix of iid standard normal random variables with standard deviation `sigmaAB`

and mean `muAB`

for *W* and mean `-muAB`

for *Z*.
Lastly, the covariate array *X_t* has 3 covariates: the first is an intercept, the second consists of iid Bernoulli random variables, and the third consists of iid standard normal random variables. All coefficients are *β_i = 0* for *i = 1,2,3*.

`fit` |
An |

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