Description Usage Arguments Details Value Examples
An RJMCMC algorithm is used to achieve model discrimination between the null model which contains only the baseline rate parameter and the full model which contains both the baseline rate and social parameters.
1 | rjmcmc(formatteddata, its, pilot_tuner1, pilot_tuner2, start1, start2, start3, p1, p2)
|
formatteddata |
Formatted data using the FormatData function. |
its |
Number of iterations |
pilot_tuner1 |
Tuner for proposal distribution for the social parameter. |
pilot_tuner2 |
Tuner for the proposal distribution for the baseline rate parameter. |
start1 |
Start value for the social parameter |
start2 |
Start value for the baseline rate parameter |
start3 |
Start model |
p1 |
Unifor prior variance tuner for the baseline rate |
p2 |
Uniform prior variance tuner for the social parameter |
It is important to check that the chains have mixed which using this function. A rough way would be to view the trace plots printed.
The output is a table with the number of iterations for which the Markov chain spent in each visited model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | #Example 1
data(timearray)
data(idarray)
data(socialx)
data(socialy)
Times = timearray[,1]
Ids = idarray[,1]
lenh = length(Times)
Groups = rep(1,length(Times))
Events = c(1:length(Times))
socialites = matrix(1,nrow=lenh,ncol=lenh)
x = socialx
y = socialy
plot(x[,1],y[,1],xlab="x",ylab="y",cex=2,pch=16,main="Point pattern of nest positions")
areas = calculate.areas(x[,1],y[,1],rep(0.2,lenh),1000)
spatialareas = areas
len = length(x[,1])
Diffusions = rep(1,len)
for(i in 2:10){
addon = rep(i,len)
Diffusions = c(Diffusions,addon)
}
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
}
}
shape = FormatData(Times,spatialnetwork,Ids,Groups,Diffusions,Events,spatialnetwork)
#ptm <- proc.time()
#mcmc(shape,10000,0.05,0.05,-3,-5)
#proc.time() - ptm
#ptm <- proc.time()
#rjmcmc(shape,10000,5,1,-3,-3,1,10,10)
#proc.time() - ptm
# Example 2
data(papertimes)
data(papernests)
data(x)
data(y)
z = array(0,c(length(x[,1]),1))# setting up array for storing spatial covariate information
for(i in 1:70){ # simulating spatial covariate information
xx = x[,1][i]
yy = y[,1][i]
z[i] = (3*xx + 14*yy) * exp(2 * (.4*xx - 1))
}
Times = papertimes[,1]
Ids = papernests[,1]
Diffusions = rep(1,length(Times))
Groups = rep(1,length(Times))
Events = c(1:length(Times))
socialites = matrix(1,nrow=70,ncol=70)
plot(x[,1],y[,1],xlab="x",ylab="y",cex=2,pch=16,main="Point pattern of nest positions")
areas = calculate.areas(x[,1],y[,1],rep(0.05,70),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
}
}
shape = FormatData(Times,spatialnetwork,Ids,Groups,Diffusions,Events,spatialnetwork,z)
#ptm <- proc.time()
#mcmc(shape,10000,5,1,-5,-6)
#proc.time() - ptm
#ptm <- proc.time()
#nullmcmc(shape,10000,1,-5)
#proc.time() - ptm
#ptm <- proc.time()
#rjmcmc(shape,10000,5,1,0,0,2,5,5)
#proc.time() - ptm
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