mcmcre: Performs NBDA with individual level random effects

In spatialnbda: Performs spatial NBDA in a Bayesian context

Description

The incorporation of random effects accounts for heterogeneity contributed by individual aptitudes of the individuals concerned. The baseline rate is then scaled by the random effects.

Usage

mcmcre(formatteddata, its, pilot_tuner1, pilot_tuner2, start1, start2)

Arguments

formatteddata	Formatted data using the function FormatData
its	Number of iterations
pilot_tuner1	Tuner for the social parameter
pilot_tuner2	Tuner for the asocial parameter
start1	Start value for the social parameter
start2	Start value for the asocial parameter

Value

The output is a list that contains: (i) The siumulated values for each parameter (ii) The posterior summaries each random effect parameter, (iii) The posterior summaries for the social and asocial parameters Trace plots for the social and asocial parameters are provided together with a density and acf plot for the social parameter.

Examples

# library(calibrate)
# loading the x and y spatial coordinates to construct the spatially derived 
#social network

data(Xx)
data(Yy)

X <- cbind(Xx,Yy)
plot(X[,1],X[,2],pch=16,cex=1,xlim=c(0,1),ylim=c(0,1),xlab="x",
ylab="y",main="",cex.axis=2,cex.lab=2)

areas = calculate.areas(X[,1], X[,2], rep(0.2,length(X[,1])), 1000)
spatialareas = areas
len = length(X[,1])

spatialnetwork = matrix(0,nrow=len,ncol=len)
for(i in 1:len){
  for(j in i:len){ 
    template = spatialareas[[i]][j]
    spatialnetwork[i,j] = spatialnetwork[j,i] = template
    #spatialareas[[i]]=NULL
    
  }
  
}

# loading the times and ids to plot the diffusion times and run nbda

data(Times)
data(Ids)
numdiff = 10

plot_colors = colors()[c(12,28,31,32,34,37,41,47,59,62,146,176,258,117,154,625,563,376,113,556)]

for(i in 1:numdiff){
  a = (i-1) * (len)
  b = a + (len)
  startindex = a + 1
  endindex = b
  plot(Times[startindex:endindex,1],c(1:len),type="o",lwd=4,col=plot_colors[i],ylab="Solver index",
  main="",xlab="Time(s)",yaxt='n',ylim=c(0,len),xlim=range(Times))
  #textxy(c(1:len), Times[startindex:endindex,1], Ids[startindex:endindex,1],cex = .8,col="red")
  par(new=TRUE)
}
par(new=TRUE)
plot( Times[1:len,1],c(1:len),type="o",lwd=4,col=plot_colors[1],ylab="",main="",xlab="",
     ylim=c(0,len),xlim=range(Times))



Diffusions = rep(1,len)
for(i in 2:numdiff){
  addon = rep(i,len)
  Diffusions = c(Diffusions,addon)

}
Groups = rep(1,length(Times[,1]))
Events = c(1:length(Times[,1]))
space = rep(1,length(Times[,1]))
spatialnetwork = 1*spatialnetwork
shape = FormatData(Times[,1],spatialnetwork,Ids[,1],Groups,Diffusions,Events,spatialnetwork)

# running nbda to obtain posterior estimates of the social and 
# baseline rate parameters

#ptm <- proc.time()
#mcmc(shape,10000,0.05,0.05,-3,-5)
#proc.time() - ptm


# running nbda to obtain posterior estimates of the
# social, baseline rate and random effect parameters

#ptm <- proc.time()
#mcmcre(shape,10000,0.05,0.05,-3,-5)
#proc.time() - ptm

spatialnbda documentation built on May 2, 2019, 8:54 a.m.