Description Usage Arguments Value Author(s) References Examples

Estimates j2 model parameters as described in Zijlstra (in press) <doi:10.1080/0022250X.2017.1387858>.

1 2 |

`net` |
Directed dichotomous n*n network (digraph). |

`sender` |
Optional sender covariates of lenght n. |

`receiver` |
Optinal receiver covariates of length n. |

`density` |
Optional density covariates of dimensions n*n. |

`reciprocity` |
Optional symmetric reciprocity covariates of dimensions n*n. |

`burnin` |
Optional specification of number of burn-in iterations (default is 10000). |

`sample` |
Optional specification of number of MCMC samples (default is 40000). |

`adapt` |
Optional number of adaptive sequenses (default is 100). |

`center` |
Optional boolean argument for centering predictors (default is TRUE). |

`seed` |
Optonal specification of random seed (delfault is 1). |

Returns a matrix with MCMC means, standard deviations, quantiles and effective sample sizes for j2 parameters.

Bonne J.H. Zijlstra b.j.h.zijlstra@uva.nl

Zijlstra, B.J.H. (in press). Regression of directed graphs on independent effects for density and reciprocity. *Journal of Mathematical Sociology*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
# create a very small network with covariates for illustrative purposes
S <- c(1,0,1,0,1,1,0,1,0,1)
REC <- c(0,0,1,1,0,0,1,1,0,0)
D1 <- matrix(c(0,1,0,1,0,1,0,1,0,0,
0,0,1,1,0,1,0,1,0,1,
1,1,0,0,1,0,0,0,0,0,
1,1,1,0,1,0,0,0,0,1,
1,0,1,0,0,1,1,0,1,1,
0,0,0,0,0,0,1,1,1,1,
0,0,0,0,0,1,0,1,0,1,
1,0,0,0,0,1,1,0,1,1,
0,1,0,1,0,1,0,1,0,0,
0,0,1,1,1,0,0,0,0,0), ncol=10)
D2 <- abs(matrix(rep(S,10), byrow = FALSE, ncol= 10) -
matrix(rep(REC,10), byrow = TRUE, ncol= 10))
R <- D1*t(D1)
Y <- matrix(c(0,0,1,1,1,1,0,0,1,1,
0,0,0,1,1,1,0,0,1,0,
1,1,0,1,1,1,0,0,1,1,
0,1,1,0,1,1,0,1,1,0,
1,1,1,1,0,1,1,0,1,1,
0,1,1,1,1,0,1,1,1,0,
1,0,1,0,1,1,0,1,0,1,
0,1,1,1,0,1,1,0,1,1,
1,0,1,0,1,0,1,1,0,1,
1,1,1,0,0,1,1,1,1,0), ncol=10)
# estimate j2 model
j2(Y,sender= ~ S, receiver = ~ REC, density = ~ D1 + D2, reciprocity= ~ R,
burnin = 100, sample = 400, adapt = 10)
# notice: burn-in, sample size and number of adaptive sequenses are
# much smaller than recommended to keep computation time low.
# recommended code:
j2(Y,sender= ~ S, receiver = ~ REC, density = ~ D1 + D2, reciprocity= ~ R)
``` |

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