p2: MCMC estimates for the p2 model

In dyads: Dyadic Network Analysis

MCMC estimates for the p2 model

Description

Estimates p2 model parameters with the adaptive random walk algorithm as described in Zijlstra, Van Duijn and Snijders (2009) <doi: 10.1348/000711007X255336>.

Usage

p2(net, sender = NULL, receiver = NULL, density = NULL, reciprocity = NULL, 
burnin = NULL, sample = NULL, adapt = NULL, seed = NULL)

Arguments

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

Value

Returns a matrix with MCMC means, standard deviations, quantiles and estimated effective sample sizes for p2 parameters.

Author(s)

Bonne J.H. Zijlstra [e-mail]

References

Zijlstra, B.J.H., Duijn, M.A.J. van, and Snijders, T.A.B. (2009). MCMC estimation for the $p_2$ network regression model with crossed random effects. British Journal of Mathematical and Statistical Psychology, 62, 143-166.

Examples

# create a very small network with covariates for illustrative purposes
S <- c(1,0,1,0,1,1,0,1,0,1)
REC <- (S*-1)+1
D1 <- matrix(c(0,1,0,1,0,1,0,1,0,1,
              0,0,0,1,0,1,0,1,0,1,
              1,1,0,0,1,0,0,0,0,0,
              1,1,1,0,1,0,0,0,0,1,
              1,0,1,0,0,1,1,0,1,0,
              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,
              1,0,1,1,1,0,0,0,0,0), 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(0,1,1,1,1,1,0,0,1,1,
              0,0,0,1,1,1,0,0,1,0,
              1,1,0,1,1,1,0,0,1,1,
              1,1,1,0,1,1,0,1,1,0,
              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 p2 model
p2(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: 
p2(Y,sender= ~ S, receiver =  ~ REC, density = ~ D1+ D2, reciprocity= ~ R)

## End(Not run)

dyads documentation built on Aug. 17, 2022, 9:06 a.m.