Nothing
DistSimfix <-
function(posx, NumProcess=2,type='Poisson',
lambdaMarg=NULL, #parameter for the 'Poisson' type
lambdaParent=NULL, lambdaNumP=NULL, dist='normal', sigmaC=1,minC=-1,maxC=1, #parameters for the 'Cluster' type
PA=FALSE,info=FALSE,fixed.seed=1,...)
{
#DistSimfix generates the second and following processes, and given the first process
#calculates de sets of close points and the corresponding mean distance
#for each point X of the first process. The differencewith DistSim is that here the seed is fixed
if (length(sigmaC)==1) sigmaC<-c(sigmaC, sigmaC)
if (length(minC)==1) minC<-c(minC, minC)
if (length(maxC)==1) Max<-c(maxC, maxC)
if (length(lambdaNumP)==1) lambdaNumP<-c(lambdaNumP, lambdaNumP)
if (is.vector(lambdaParent)==TRUE) lambdaParent<-cbind(lambdaParent, lambdaParent)
if(type=='Poisson')
{
posyNH<-simNHPc(lambda=lambdaMarg[,1],fixed.seed=fixed.seed)$posNH
if (NumProcess==3) poszNH<-simNHPc(lambda=lambdaMarg[,2],
fixed.seed=(fixed.seed+100000))$posNH
else poszNH=NULL
}
if(type=='PoissonCluster')
{
Tf<-length(lambdaParent)
posTy<-simNHPc(lambda=lambdaParent[,1],fixed.seed=fixed.seed)$posNH
posyNH<-GenSons(posTy, lambdaNumP=lambdaNumP[1], dist=dist,sigmaC=sigmaC[1],
minC=minC[1], maxC=maxC[1], Tf=Tf,fixed.seed=fixed.seed)
if (NumProcess==3){
posTz<-simNHPc(lambda=lambdaParent[,2],fixed.seed=(fixed.seed+100000))$posNH
poszNH<-GenSons(posTz,lambdaNumP=lambdaNumP[2], dist=dist,sigmaC=sigmaC[2],
minC=minC[2], maxC=maxC[2], Tf=Tf,fixed.seed=(fixed.seed+100000))
} else poszNH=NULL
}
DistTri<-DistObs(posx=posx,posy=posyNH,posz=poszNH, info=info,
PA=PA,procName=c('ObsX','SimY','SimZ'),...)
return(DistTri)
}
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