Package 'dyads' reference manual

Title:	Dyadic Network Analysis
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.
Authors:	Bonne J.H. Zijlstra <[email protected]>
Maintainer:	Bonne J.H. Zijlstra <[email protected]>
License:	GPL (>= 2)
Version:	1.2.1
Built:	2024-12-06 06:47:59 UTC
Source:	CRAN

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 <[email protected]>
Maintainer:	Bonne J.H. Zijlstra <[email protected]>
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)
NeedsCompilation:	no
Packaged:	2022-08-17 07:33:31 UTC; b.j.h.zijlstra
Repository:	CRAN
Date/Publication:	2022-08-17 07:50:02 UTC
Config/pak/sysreqs:	make

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 <[email protected]>

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)
# 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)

MCMC estimates for the (multilevel) b2 model

Description

Estimates the (multilevel) b2 model parameters, which is the bidirectional counterpart of the multilevel p2 model as described in Zijlstra, Van Duijn and Snijders (2006) <doi: 10.1027/1614-2241.2.1.42>.

Usage

b2ML(nets, actor = NULL, density = NULL, adapt = NULL, burnin = NULL, center = NULL, 
separate= NULL, densVar = NULL, seed = NULL) 
b2ML(nets, actor = NULL, density = NULL, adapt = NULL, burnin = NULL, center = NULL, 
separate= NULL, densVar = NULL, seed = NULL)

Arguments

`nets`	List with n dichotomous symmetric dependent networks.
`actor`	Optional matrix with a stacked actor covariate, corresponding to the n networks. Multiple actor covariates can be added as a formula object, see example below
`density`	Optional matrix with symmetric a stacked density covariate, with dimensions similar to the n dependent networks. Multiple density covariates can be added as a formula object, see example below
`adapt`	Optional number of adaptive sequenses (default is 100).
`burnin`	Optional specification of number of burn-in iterations (default is 5000).
`center`	Optional argument for centering predictors (default is TRUE).
`separate`	Optional argument for estimating separate coefficients for the n dependent networks (default is FALSE).
`densVar`	Optional argument for estimating densty variance at the network level (default is TRUE).
`seed`	Optonal specification of random seed (delfault is 1).

Value

Returns a fitted model of class b2ML, to be opened with the function summary().

Author(s)

Bonne J.H. Zijlstra [email protected]

References

Zijlstra, B. J., Van Duijn, M. A., & Snijders, T. A. (2006). The Multilevel p2 Model A random effects model for the analysis of multiple social networks. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2(1), 42.

Examples


# create two very small networks with covariates for illustrative purposes
Y1 <- matrix(c( 0,1,1,1,1,1,1,1,1,0,
                1,0,1,0,1,1,1,1,1,1,
                1,1,0,0,1,1,1,1,0,1,
                1,0,0,0,1,0,0,1,0,0,
                1,1,1,1,0,1,1,0,1,1,
                1,1,1,0,1,0,1,0,1,1,
                1,1,1,0,1,1,0,1,1,1,
                1,1,1,1,0,0,1,0,0,1,
                1,1,0,0,1,1,1,0,0,1,
                0,1,1,0,1,1,1,1,1,0), ncol=10) 
Y2 <- matrix(c( 0,0,1,0,1,1,0,1,0,0,
                0,0,0,0,0,0,0,1,1,0,
                1,0,0,1,0,1,0,1,0,0,
                0,0,1,0,0,0,1,1,0,0,
                1,0,0,0,0,0,1,1,0,0,
                1,0,1,0,0,0,1,1,0,0,
                0,0,0,1,1,1,0,1,0,0,
                1,1,1,1,1,1,1,0,0,1,
                0,1,0,0,0,0,0,0,0,0,
                0,0,0,0,0,0,0,1,0,0), ncol=10) 
Y <- list(Y1, Y2)                
Aa1 <- c(1,0,1,0,1,1,0,1,0,1)
Aa2 <- c(1,0,0,1,0,0,1,1,0,1)
Aa <- list(Aa1, Aa2)
Aat <- do.call(plyr::rbind.fill.matrix, Aa)
Ab1 <- c(0,0,0,0,0,0,0,0,0,0)
Ab2 <- c(1,1,1,1,1,1,1,1,1,1)
Ab <- list(Ab1, Ab2)
Abt <- do.call(plyr::rbind.fill.matrix, Ab)
Da1 <- abs(matrix(rep(Aa1,10), byrow = FALSE, ncol= 10) -
            matrix(rep(Aa1,10), byrow = TRUE, ncol= 10))
Da2 <- abs(matrix(rep(Aa2,10), byrow = FALSE, ncol= 10) -
            matrix(rep(Aa2,10), byrow = TRUE, ncol= 10))
Da <- list(Da1, Da2)
Dat <- do.call(plyr::rbind.fill.matrix, Da)

# estimate b2 model for two networks
M1 <- b2ML(Y,actor= ~ Aat + Abt, density = ~ Dat, adapt = 10, burnin = 100, densVar = FALSE)
summary(M1)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M1 <- b2ML(Y,actor= ~ Aat + Abt, density = ~ Dat, densVar = FALSE)
summary(M1)

## End(Not run)

# estimate b2 model for a single network
M2 <- b2ML(list(Y[[1]]),actor= ~ Aat[1:10,], density = ~ Dat[1:10,], adapt = 10, burnin = 100, 
densVar = FALSE)
summary(M2)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M2 <- b2ML(list(Y[[1]]),actor= ~ Aat[1:10,], density = ~ Dat[1:10,], densVar = FALSE)
summary(M2)

## End(Not run)
# create two very small networks with covariates for illustrative purposes
Y1 <- matrix(c( 0,1,1,1,1,1,1,1,1,0,
                1,0,1,0,1,1,1,1,1,1,
                1,1,0,0,1,1,1,1,0,1,
                1,0,0,0,1,0,0,1,0,0,
                1,1,1,1,0,1,1,0,1,1,
                1,1,1,0,1,0,1,0,1,1,
                1,1,1,0,1,1,0,1,1,1,
                1,1,1,1,0,0,1,0,0,1,
                1,1,0,0,1,1,1,0,0,1,
                0,1,1,0,1,1,1,1,1,0), ncol=10) 
Y2 <- matrix(c( 0,0,1,0,1,1,0,1,0,0,
                0,0,0,0,0,0,0,1,1,0,
                1,0,0,1,0,1,0,1,0,0,
                0,0,1,0,0,0,1,1,0,0,
                1,0,0,0,0,0,1,1,0,0,
                1,0,1,0,0,0,1,1,0,0,
                0,0,0,1,1,1,0,1,0,0,
                1,1,1,1,1,1,1,0,0,1,
                0,1,0,0,0,0,0,0,0,0,
                0,0,0,0,0,0,0,1,0,0), ncol=10) 
Y <- list(Y1, Y2)                
Aa1 <- c(1,0,1,0,1,1,0,1,0,1)
Aa2 <- c(1,0,0,1,0,0,1,1,0,1)
Aa <- list(Aa1, Aa2)
Aat <- do.call(plyr::rbind.fill.matrix, Aa)
Ab1 <- c(0,0,0,0,0,0,0,0,0,0)
Ab2 <- c(1,1,1,1,1,1,1,1,1,1)
Ab <- list(Ab1, Ab2)
Abt <- do.call(plyr::rbind.fill.matrix, Ab)
Da1 <- abs(matrix(rep(Aa1,10), byrow = FALSE, ncol= 10) -
            matrix(rep(Aa1,10), byrow = TRUE, ncol= 10))
Da2 <- abs(matrix(rep(Aa2,10), byrow = FALSE, ncol= 10) -
            matrix(rep(Aa2,10), byrow = TRUE, ncol= 10))
Da <- list(Da1, Da2)
Dat <- do.call(plyr::rbind.fill.matrix, Da)

# estimate b2 model for two networks
M1 <- b2ML(Y,actor= ~ Aat + Abt, density = ~ Dat, adapt = 10, burnin = 100, densVar = FALSE)
summary(M1)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M1 <- b2ML(Y,actor= ~ Aat + Abt, density = ~ Dat, densVar = FALSE)
summary(M1)

## End(Not run)

# estimate b2 model for a single network
M2 <- b2ML(list(Y[[1]]),actor= ~ Aat[1:10,], density = ~ Dat[1:10,], adapt = 10, burnin = 100, 
densVar = FALSE)
summary(M2)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M2 <- b2ML(list(Y[[1]]),actor= ~ Aat[1:10,], density = ~ Dat[1:10,], densVar = FALSE)
summary(M2)

## End(Not run)

MCMC estimates for the j2 model

Description

Estimates j2 model parameters as described in Zijlstra (2017) <doi:10.1080/0022250X.2017.1387858>.

Usage

j2(net, sender = NULL, receiver = NULL , density = NULL, reciprocity = NULL, 
burnin = NULL, sample = NULL, adapt= NULL, center = NULL, seed = NULL) 
j2(net, sender = NULL, receiver = NULL , density = NULL, reciprocity = NULL, 
burnin = NULL, sample = NULL, adapt= NULL, center = 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).
`center`	Optional boolean argument for centering predictors (default is TRUE).
`seed`	Optonal specification of random seed (delfault is 1).

Value

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

Author(s)

Bonne J.H. Zijlstra [email protected]

References

Zijlstra, B.J.H. (2017). Regression of directed graphs on independent effects for density and reciprocity. The Journal of Mathematical Sociology 41 (4).

Examples


# 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)
# 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)

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)
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 [email protected]

References

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)
# 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)

MCMC estimates for the (multilevel) p2 model

Description

Estimates the (multilevel) p2 model parameters,as described in Zijlstra, Van Duijn and Snijders (2006) <doi: 10.1027/1614-2241.2.1.42>.

Usage

p2ML(nets, sender = NULL, receiver = NULL, density =~ 1, reciprocity =~ 1, 
adapt = NULL, burnin = NULL, center = NULL, separate= NULL, seed = NULL) 

p2ML(nets, sender = NULL, receiver = NULL, density =~ 1, reciprocity =~ 1, 
adapt = NULL, burnin = NULL, center = NULL, separate= NULL, seed = NULL)

Arguments

`nets`	List with n dichotomous dependent directed networks.
`sender`	Optional matrix with a stacked actor-level sender covariate, corresponding to the n networks. Multiple sender covariates can be added as a formula object, see example below
`receiver`	Optional matrix with a stacked actor-level receiver covariate, corresponding to the n networks. Multiple receiver covariates can be added as a formula object
`density`	Optional stacked matrix with a density covariate, with dimensions similar to the n dependent networks. Multiple density covariates can be added as a formula object, see example below
`reciprocity`	Optional stacked matrix with a symmetric reciprocity covariate, with dimensions similar to the n dependent networks. Multiple reciprocity covariates can be added as a formula object
`adapt`	Optional number of adaptive sequenses (default is 125).
`burnin`	Optional specification of number of burn-in iterations (default is 2500).
`center`	Optional argument for centering predictors (default is TRUE).
`separate`	Optional argument for estimating separate coefficients for the n dependent networks (default is FALSE).
`seed`	Optonal specification of random seed (delfault is 1).

Value

Returns a fitted model of class 2ML, to be opened with the function summary().

Author(s)

Bonne J.H. Zijlstra [email protected]

References

Examples


# create two very small networks with covariates for illustrative purposes
Y1 <- 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)
Y2 <- matrix(c(0,0,1,0,1,0,0,1,0,0,
               0,0,0,0,0,0,0,1,1,0,
               0,0,0,1,0,1,0,1,0,1,
               0,0,1,0,0,0,1,1,0,0,
               1,0,0,1,0,0,1,0,0,1,
               0,0,1,0,0,0,1,1,0,0,
               0,1,0,0,1,0,0,0,0,0,
               1,0,1,0,1,1,1,0,0,1,
               0,1,0,1,0,0,0,0,0,0,
               0,1,0,1,0,0,0,1,0,0), ncol=10) 
Y <- list(Y1, Y2)                
Sa1 <- c(1,0,1,0,1,1,0,1,0,1)
Sa2 <- c(1,0,0,1,0,0,1,1,0,1)
Sa <- list(Sa1, Sa2)
Sat <- Rat <-  do.call(plyr::rbind.fill.matrix, Sa)
Sb1 <- c(0,1,1,0,1,0,1,0,1,0)
Sb2 <- c(1,0,1,0,0,1,0,1,0,1)
Sb <- list(Sb1, Sb2)
Sbt <- do.call(plyr::rbind.fill.matrix, Sb)
Da1 <- abs(matrix(rep(Sa1,10), byrow = FALSE, ncol= 10) -
              matrix(rep(Sa1,10), byrow = TRUE, ncol= 10))
Da2 <- abs(matrix(rep(Sa2,10), byrow = FALSE, ncol= 10) -
              matrix(rep(Sa2,10), byrow = TRUE, ncol= 10))
Da <- list(Da1, Da2)
Dat <- do.call(plyr::rbind.fill.matrix, Da)

# estimate p2 model for two networks
M1 <- p2ML(Y, sender= ~ Sat + Sbt,  receiver= ~ Rat, density = ~ Dat, adapt = 10, burnin = 100)
summary(M1)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M1 <- p2ML(Y,sender= ~ Sat + Sbt,  receiver= ~ Rat, density = ~ Dat)
summary(M1)

## End(Not run)

# estimate p2 model for a single network
M2 <- p2ML(list(Y[[1]]),sender= ~ Sat[1:10,] + Sbt[1:10,],  receiver= ~ Rat[1:10,],
density = ~ Dat[1:10,], adapt = 10, burnin = 100)
summary(M2)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M2 <- p2ML(list(Y[[1]]),sender= ~ Sat[1:10,] + Sbt[1:10,],  receiver= ~ Rat[1:10,], 
density = ~ Dat[1:10,])
summary(M2)

## End(Not run)
# create two very small networks with covariates for illustrative purposes
Y1 <- 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)
Y2 <- matrix(c(0,0,1,0,1,0,0,1,0,0,
               0,0,0,0,0,0,0,1,1,0,
               0,0,0,1,0,1,0,1,0,1,
               0,0,1,0,0,0,1,1,0,0,
               1,0,0,1,0,0,1,0,0,1,
               0,0,1,0,0,0,1,1,0,0,
               0,1,0,0,1,0,0,0,0,0,
               1,0,1,0,1,1,1,0,0,1,
               0,1,0,1,0,0,0,0,0,0,
               0,1,0,1,0,0,0,1,0,0), ncol=10) 
Y <- list(Y1, Y2)                
Sa1 <- c(1,0,1,0,1,1,0,1,0,1)
Sa2 <- c(1,0,0,1,0,0,1,1,0,1)
Sa <- list(Sa1, Sa2)
Sat <- Rat <-  do.call(plyr::rbind.fill.matrix, Sa)
Sb1 <- c(0,1,1,0,1,0,1,0,1,0)
Sb2 <- c(1,0,1,0,0,1,0,1,0,1)
Sb <- list(Sb1, Sb2)
Sbt <- do.call(plyr::rbind.fill.matrix, Sb)
Da1 <- abs(matrix(rep(Sa1,10), byrow = FALSE, ncol= 10) -
              matrix(rep(Sa1,10), byrow = TRUE, ncol= 10))
Da2 <- abs(matrix(rep(Sa2,10), byrow = FALSE, ncol= 10) -
              matrix(rep(Sa2,10), byrow = TRUE, ncol= 10))
Da <- list(Da1, Da2)
Dat <- do.call(plyr::rbind.fill.matrix, Da)

# estimate p2 model for two networks
M1 <- p2ML(Y, sender= ~ Sat + Sbt,  receiver= ~ Rat, density = ~ Dat, adapt = 10, burnin = 100)
summary(M1)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M1 <- p2ML(Y,sender= ~ Sat + Sbt,  receiver= ~ Rat, density = ~ Dat)
summary(M1)

## End(Not run)

# estimate p2 model for a single network
M2 <- p2ML(list(Y[[1]]),sender= ~ Sat[1:10,] + Sbt[1:10,],  receiver= ~ Rat[1:10,],
density = ~ Dat[1:10,], adapt = 10, burnin = 100)
summary(M2)
# Notice: burn-in, and number of adaptive sequenses are 
# much smaller than recommended to keep computation time low.
# recommended code: 
## Not run: 
M2 <- p2ML(list(Y[[1]]),sender= ~ Sat[1:10,] + Sbt[1:10,],  receiver= ~ Rat[1:10,], 
density = ~ Dat[1:10,])
summary(M2)

## End(Not run)

Package 'dyads'

Help Index

dyads

Description

Details

Author(s)

References

Examples

MCMC estimates for the (multilevel) b2 model

Description

Usage

Arguments

Value

Author(s)

References

Examples

MCMC estimates for the j2 model

Description

Usage

Arguments

Value

Author(s)

References

Examples

MCMC estimates for the p2 model

Description

Usage

Arguments

Value

Author(s)

References

Examples

MCMC estimates for the (multilevel) p2 model

Description

Usage

Arguments

Value

Author(s)

References

Examples