dyads-package {dyads}

dyads

Description

Package for Dyadic Network Analysis.

Details

Package:	dyads
Type:	Package
Title:	Dyadic Network Analysis
Version:	1.2.1
Date:	2022-08-16
Author:	Bonne J.H. Zijlstra <[e-mail]>
Maintainer:	Bonne J.H. Zijlstra <[e-mail]>
Depends:	R (>= 3.0.0)
Imports:	stats, CholWishart, MASS, RcppZiggurat, Rfast, mvtnorm
Suggests:	plyr
Description:	Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>, the (multilevel) b2 model.
License:	GPL (>= 2)

Index of help topics:

b2ML                    MCMC estimates for the (multilevel) b2 model
dyads-package           dyads
j2                      MCMC estimates for the j2 model
p2                      MCMC estimates for the p2 model
p2ML                    MCMC estimates for the (multilevel) p2 model

Includes functions for estimation of the (multilevel) p2 model (van Duijn, Snijders and Zijlstra (2004) <doi:10.1046/j.0039-0402.2003.00258.x>), more specifically the adaptive random walk algorithm (Zijlstra, van Duijn and Snijders (2009) <doi:10.1348/000711007X255336>), for the estimation of the j2 model (Zijlstra (2017) <doi:10.1080/0022250X.2017.1387858>), and for their bidirectional counterpart, b2.

Author(s)

Bonne J.H. Zijlstra Maintainer: 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. Zijlstra, B.J.H. (2017). Regression of directed graphs on independent effects for density and reciprocity. Journal of Mathematical Sociology, 41(4), 185-192.

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)