Modeling a binary network outcome

Load the library:

library(amen)

Set up the data:

data(lazegalaw)

Y<-lazegalaw$Y[,,2]
Xn<-lazegalaw$X[,c(2,4,5,6)]
Xd<-lazegalaw$Y[,,-2]
Xd<-array( c(Xd,outer(Xn[,4],Xn[,4],"==")),dim=dim(Xd)+c(0,0,1))
dimnames(Xd)[[3]]<-c("advice","cowork","samepractice")

dimnames(Xd)[[3]]

## [1] "advice"       "cowork"       "samepractice"

dimnames(Xn)[[2]]

## [1] "female"    "seniority" "age"       "practice"

plot the network with “practice” denoted by plotting color:

netplot(lazegalaw$Y[,,2],ncol=Xn[,4])

fitSRRM<-ame(Y, Xd=Xd, Xr=Xn, Xc=Xn, family="bin")

summary(fitSRRM) 

## 
## Regression coefficients:
##                    pmean   psd z-stat p-val
## intercept         -0.248 0.479 -0.518 0.605
## female.row        -0.024 0.132 -0.179 0.858
## seniority.row     -0.001 0.010 -0.127 0.899
## age.row           -0.016 0.009 -1.898 0.058
## practice.row      -0.138 0.112 -1.234 0.217
## female.col        -0.057 0.121 -0.475 0.635
## seniority.col      0.017 0.009  1.895 0.058
## age.col           -0.008 0.008 -0.981 0.326
## practice.col      -0.200 0.103 -1.934 0.053
## advice.dyad       -0.096 0.083 -1.155 0.248
## cowork.dyad        1.144 0.065 17.566 0.000
## samepractice.dyad  0.449 0.055  8.113 0.000
## 
## Variance parameters:
##     pmean   psd
## va  0.161 0.037
## cab 0.014 0.021
## vb  0.125 0.029
## rho 0.085 0.054
## ve  1.000 0.000

fitAME<-ame(Y, Xd=Xd, Xr=Xn, Xc=Xn, R=3, family="bin")

summary(fitAME) 

## 
## Regression coefficients:
##                    pmean   psd z-stat p-val
## intercept         -0.857 0.691 -1.240 0.215
## female.row        -0.120 0.187 -0.641 0.522
## seniority.row     -0.002 0.015 -0.117 0.907
## age.row           -0.023 0.013 -1.751 0.080
## practice.row      -0.073 0.167 -0.437 0.662
## female.col        -0.107 0.169 -0.635 0.526
## seniority.col      0.010 0.013  0.737 0.461
## age.col           -0.006 0.011 -0.531 0.596
## practice.col      -0.085 0.142 -0.600 0.548
## advice.dyad       -0.132 0.109 -1.212 0.226
## cowork.dyad        1.459 0.093 15.769 0.000
## samepractice.dyad  0.566 0.083  6.835 0.000
## 
## Variance parameters:
##     pmean   psd
## va  0.297 0.077
## cab 0.025 0.041
## vb  0.175 0.051
## rho 0.154 0.094
## ve  1.000 0.000