j2 | R Documentation |
Estimates j2 model parameters as described in Zijlstra (2017) <doi:10.1080/0022250X.2017.1387858>.
j2(net, sender = NULL, receiver = NULL , density = NULL, reciprocity = NULL, burnin = NULL, sample = NULL, adapt= NULL, center = NULL, seed = NULL)
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. (2017). Regression of directed graphs on independent effects for density and reciprocity. The Journal of Mathematical Sociology 41 (4).
# 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: ## Not run: j2(Y,sender= ~ S, receiver = ~ REC, density = ~ D1 + D2, reciprocity= ~ R) ## End(Not run)
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