# R/rZ_frn_fc.R In amen: Additive and Multiplicative Effects Models for Networks and Relational Data

#### Documented in rZ_frn_fc

```#' Simulate Z given fixed rank nomination data
#'
#' Simulates a random latent matrix Z given its expectation, dyadic correlation
#' and fixed rank nomination data
#'
#' simulates Z under the constraints (1) Y[i,j]>Y[i,k] => Z[i,j]>Z[i,k] , (2)
#' Y[i,j]>0 => Z[i,j]>0 , (3) Y[i,j]=0 & odobs[i]<odmax[i] => Z[i,j]<0
#'
#' @usage rZ_frn_fc(Z, EZ, rho, Y, YL, odmax, odobs)
#' @param Z a square matrix, the current value of Z
#' @param EZ expected value of Z
#' @param Y square matrix of ranked nomination data
#' @param YL list of ranked individuals, from least to most preferred in each
#' row
#' @param odmax a scalar or vector giving the maximum number of nominations for
#' each individual
#' @param odobs observed outdegree
#' @return a square matrix, the new value of Z
#' @author Peter Hoff
#' @export rZ_frn_fc
rZ_frn_fc <-
function(Z,EZ,rho,Y,YL,odmax,odobs)
{
# simulates Z under the contraints
# (1)  Y[i,j]>Y[i,k]                => Z[i,j]>Z[i,k]  (same as rank)
# (2)  Y[i,j]>0                     => Z[i,j]>0
# (3)  Y[i,j]=0 & odobs[i]<odmax[i] => Z[i,j]<0

sz<-sqrt(1-rho^2)
ut<-upper.tri(Z)
lt<-lower.tri(Z)
rws<-outer(1:nrow(Z),rep(1,nrow(Z)))

Y[is.na(Y)]<- -1

for(y in sample(c( (-1):ncol(YL))) )
{
if(y<2)
{
if(y<=0){lbm<- rep(-Inf,nrow(Z))}
if(y==1){lbm<-pmax(0,apply(Z - (Y!=0)*(Inf^(Y!=0)),1,max,na.rm=TRUE)) }
}
if(y>=2) {lbm<-Z[cbind(1:nrow(Z),YL[,y-1])] }

if(y== -1) { ubm<-rep(Inf,nrow(Z))}
if(y<ncol(YL) & y>=0)  { ubm<- Z[ cbind(1:nrow(Z), YL[,y+1] )]  }
if(y==0) { ubm[odobs<odmax]<- 0 }
if(y==ncol(YL)) { ubm<- rep(Inf,nrow(Z)) }
ubm[is.na(ubm)]<-Inf ; lbm[is.na(lbm)]<- -Inf

for(k in sample(1:2))
{
if(k==1) {
up<- ut & Y==y
rwb<-rws[up]
lb<-lbm[rwb] ; ub<-ubm[rwb]
ez<- EZ[up] + rho*( t(Z)[up]  - t(EZ)[up] )
Z[up]<-ez+sz*qnorm(runif(sum(up),pnorm((lb-ez)/sz),pnorm((ub-ez)/sz)))
}
if(k==2)  {
up<- lt & Y==y
rwb<-rws[up]
lb<-lbm[rwb] ; ub<-ubm[rwb]
ez<- EZ[up] + rho*( t(Z)[up]  - t(EZ)[up] )
Z[up]<-ez+sz*qnorm(runif(sum(up),pnorm((lb-ez)/sz),pnorm((ub-ez)/sz)))
}
}
}

diag(Z)<-rnorm(nrow(Z),diag(EZ),1)
Z
}
```

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amen documentation built on May 29, 2017, 2:24 p.m.