smcmc: Performs spatial NBDA in a Bayesian context with an...
In spatialnbda: Performs spatial NBDA in a Bayesian context
Description
Usage
Arguments
Value
Author(s)
Examples
Description
Performs spatial network based diffusion analysis in a
Bayesian context. This analysis includes values of an environmental
covariate in the modelling process. The figure below depicts a
point pattern formed by nest/home-range locations superimposed over a plot
of the environmental covariate expressed as an image. This type of
data can be analysed by this function (given the accompanying diffusion times
and id's). The example dataset is analysed using this function (in addition
to mcmc)
Usage
1
2
smcmc(formatteddata, its, pilot_tuner1, pilot_tuner2,
pilot_tuner3, start1, start2, start3)
Arguments
formatteddata
Formatted data using the function FormatData
its
Number of iterations
pilot_tuner1
pilot tuner for the social parameter
pilot_tuner2
pilot tuner for the baseline parameter
pilot_tuner3
pilot tuner for the spatial/environmental parameter
start1
start value for the social parameter
start2
start value for the baseline rate parameter
start3
start value for the spatial/environmental parameter
Value
The output is a list that contains:
(i) The posterior simulated values for each parameter, and
(ii) The posterior summaries for each parameter
Trace plots for the social and asocial parameters are provided together with
a density and acf plot for the social parameter.
Author(s)
Glenna Nightingale
Examples
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library(SocialNetworks)
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (formatteddata, its, pilot_tuner1, pilot_tuner2, pilot_tuner3,
start1, start2, start3)
{
TimeD = formatteddata[[1]][, 7]
censored = formatteddata[[1]][, 6]
Aij = formatteddata[[1]][, 8]
NaiveD = formatteddata[[2]]
spatcov = formatteddata[[1]][, 9]
s0 = start1
baseline_rate = lambda0 = start2
environmental = beta0 = start3
acceptcounter = 0
Jumbo <- array(0, c(its, 3))
newparam <- array(0.5, 3)
CurrentParam <- array(0.5, 3)
newparam[1] = (CurrentParam[1] <- s0)
newparam[2] = (CurrentParam[2] <- lambda0)
newparam[3] = (CurrentParam[3] <- beta0)
updates <- function(CurrentParam, newparam) {
GU3 <- runif(1, CurrentParam[1] - pilot_tuner1, CurrentParam[1] +
pilot_tuner1)
proposal = c(GU3, CurrentParam[2:3])
num <- CpS(proposal)[[1]]
den <- CpS(CurrentParam)[[1]]
acc <- exp(num - den)
acceptr <- min(1, acc)
r <- runif(1)
newparam[1] <- ifelse((r <= acceptr), GU3, CurrentParam[1])
return(newparam[1])
}
updatelambda <- function(CurrentParam, newparam) {
GU3 <- runif(1, CurrentParam[2] - pilot_tuner2, CurrentParam[2] +
pilot_tuner2)
proposal = c(CurrentParam[1], GU3, CurrentParam[3])
num <- CpS(proposal)[[1]]
den <- CpS(CurrentParam)[[1]]
acc <- exp(num - den)
acceptt <- min(1, acc)
r <- runif(1)
newparam[2] <- ifelse((r <= acceptt), GU3, CurrentParam[2])
acceptcounter <- ifelse((r <= acceptt), 1, 0)
list(newparam[2], acceptcounter)
}
updatecovariate <- function(CurrentParam, newparam) {
GU3 <- runif(1, CurrentParam[3] - 5, CurrentParam[3] +
5)
proposal = c(CurrentParam[1:2], GU3)
num <- CpS(proposal)[[1]]
den <- CpS(CurrentParam)[[1]]
acc <- exp(num - den)
acceptr <- min(1, acc)
r <- runif(1)
newparam[3] <- ifelse((r <= acceptr), GU3, CurrentParam[3])
return(newparam[3])
}
CpS = function(parameterproposal) {
baseline = exp(parameterproposal[2])
social_rate = exp(parameterproposal[1])
spatialC = exp(parameterproposal[2])
hazard = baseline * exp(spatialC) + (social_rate) * Aij
uncensored = 1 - censored
log_likelihood_u = sum(log(hazard * exp(-hazard * TimeD)) *
uncensored) + sum(-hazard * TimeD * NaiveD)
log_likelihood_c = sum(-hazard * censored)
log_likelihood = log_likelihood_u + log_likelihood_c
lambdaprior <- log(dunif(parameterproposal[2], -10, 10))
sprior <- log(dunif(parameterproposal[1], -10, 10))
sCprior <- log(dunif(parameterproposal[3], -10, 10))
pzoid <- log_likelihood + lambdaprior + sprior + sCprior
pzoid
}
for (t in 1:its) {
CurrentParam[1] = Jumbo[t, 1] = updates(CurrentParam,
newparam)[[1]]
CurrentParam[2] = Jumbo[t, 2] = updatelambda(CurrentParam,
newparam)[[1]]
CurrentParam[3] = Jumbo[t, 3] = updatecovariate(CurrentParam,
newparam)[[1]]
}
burnin = its/10
par(mfrow = c(2, 2))
plot(Jumbo[burnin:its, 1], type = "l", col = "blue", ylab = "social effect",
main = "Trace plot for social effect, s' ", lwd = 2)
plot(Jumbo[burnin:its, 2], type = "l", col = "red", ylab = "asocial effect",
main = "Trace plot for asocial effect, lambda0' ", lwd = 2)
plot(Jumbo[burnin:its, 3], type = "l", col = "lightgoldenrod",
ylab = "asocial effect", main = "Trace plot for spatial effect, beta0' ",
lwd = 2)
params = c(mean(Jumbo[burnin:its, 1]), mean(Jumbo[burnin:its,
2]), mean(Jumbo[burnin:its, 3]))
creds = c(sd(Jumbo[burnin:its, 1]), sd(Jumbo[burnin:its,
2]), sd(Jumbo[burnin:its, 3]))
mcmcresults = list(Jumbo, params, creds)
mcmcresults
}
#---------------------------------------------------------------
# Run spatial NBDA to estimate the social and asocial parameters
# s and lambda.
# The associations for the social network in this example are calculated
# using an interaction function that assumes each individual has
# an area of interaction or zone of influence.
#---------------------------------------------------------------
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()
smcmc(shape,10000,5,1,1,-5,-6,-5)
proc.time() - ptm
spatialnbda documentation built on May 2, 2019, 8:54 a.m.
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Description Usage Arguments Value Author(s) Examples
Description
Performs spatial network based diffusion analysis in a
Bayesian context. This analysis includes values of an environmental
covariate in the modelling process. The figure below depicts a
point pattern formed by nest/home-range locations superimposed over a plot
of the environmental covariate expressed as an image. This type of
data can be analysed by this function (given the accompanying diffusion times
and id's). The example dataset is analysed using this function (in addition
to mcmc)
Usage
1 2 | smcmc(formatteddata, its, pilot_tuner1, pilot_tuner2,
pilot_tuner3, start1, start2, start3)
|
Arguments
formatteddata |
Formatted data using the function FormatData |
its |
Number of iterations |
pilot_tuner1 |
pilot tuner for the social parameter |
pilot_tuner2 |
pilot tuner for the baseline parameter |
pilot_tuner3 |
pilot tuner for the spatial/environmental parameter |
start1 |
start value for the social parameter |
start2 |
start value for the baseline rate parameter |
start3 |
start value for the spatial/environmental parameter |
Value
The output is a list that contains: (i) The posterior simulated values for each parameter, and (ii) The posterior summaries for each parameter Trace plots for the social and asocial parameters are provided together with a density and acf plot for the social parameter.
Author(s)
Glenna Nightingale
Examples
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | library(SocialNetworks)
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (formatteddata, its, pilot_tuner1, pilot_tuner2, pilot_tuner3,
start1, start2, start3)
{
TimeD = formatteddata[[1]][, 7]
censored = formatteddata[[1]][, 6]
Aij = formatteddata[[1]][, 8]
NaiveD = formatteddata[[2]]
spatcov = formatteddata[[1]][, 9]
s0 = start1
baseline_rate = lambda0 = start2
environmental = beta0 = start3
acceptcounter = 0
Jumbo <- array(0, c(its, 3))
newparam <- array(0.5, 3)
CurrentParam <- array(0.5, 3)
newparam[1] = (CurrentParam[1] <- s0)
newparam[2] = (CurrentParam[2] <- lambda0)
newparam[3] = (CurrentParam[3] <- beta0)
updates <- function(CurrentParam, newparam) {
GU3 <- runif(1, CurrentParam[1] - pilot_tuner1, CurrentParam[1] +
pilot_tuner1)
proposal = c(GU3, CurrentParam[2:3])
num <- CpS(proposal)[[1]]
den <- CpS(CurrentParam)[[1]]
acc <- exp(num - den)
acceptr <- min(1, acc)
r <- runif(1)
newparam[1] <- ifelse((r <= acceptr), GU3, CurrentParam[1])
return(newparam[1])
}
updatelambda <- function(CurrentParam, newparam) {
GU3 <- runif(1, CurrentParam[2] - pilot_tuner2, CurrentParam[2] +
pilot_tuner2)
proposal = c(CurrentParam[1], GU3, CurrentParam[3])
num <- CpS(proposal)[[1]]
den <- CpS(CurrentParam)[[1]]
acc <- exp(num - den)
acceptt <- min(1, acc)
r <- runif(1)
newparam[2] <- ifelse((r <= acceptt), GU3, CurrentParam[2])
acceptcounter <- ifelse((r <= acceptt), 1, 0)
list(newparam[2], acceptcounter)
}
updatecovariate <- function(CurrentParam, newparam) {
GU3 <- runif(1, CurrentParam[3] - 5, CurrentParam[3] +
5)
proposal = c(CurrentParam[1:2], GU3)
num <- CpS(proposal)[[1]]
den <- CpS(CurrentParam)[[1]]
acc <- exp(num - den)
acceptr <- min(1, acc)
r <- runif(1)
newparam[3] <- ifelse((r <= acceptr), GU3, CurrentParam[3])
return(newparam[3])
}
CpS = function(parameterproposal) {
baseline = exp(parameterproposal[2])
social_rate = exp(parameterproposal[1])
spatialC = exp(parameterproposal[2])
hazard = baseline * exp(spatialC) + (social_rate) * Aij
uncensored = 1 - censored
log_likelihood_u = sum(log(hazard * exp(-hazard * TimeD)) *
uncensored) + sum(-hazard * TimeD * NaiveD)
log_likelihood_c = sum(-hazard * censored)
log_likelihood = log_likelihood_u + log_likelihood_c
lambdaprior <- log(dunif(parameterproposal[2], -10, 10))
sprior <- log(dunif(parameterproposal[1], -10, 10))
sCprior <- log(dunif(parameterproposal[3], -10, 10))
pzoid <- log_likelihood + lambdaprior + sprior + sCprior
pzoid
}
for (t in 1:its) {
CurrentParam[1] = Jumbo[t, 1] = updates(CurrentParam,
newparam)[[1]]
CurrentParam[2] = Jumbo[t, 2] = updatelambda(CurrentParam,
newparam)[[1]]
CurrentParam[3] = Jumbo[t, 3] = updatecovariate(CurrentParam,
newparam)[[1]]
}
burnin = its/10
par(mfrow = c(2, 2))
plot(Jumbo[burnin:its, 1], type = "l", col = "blue", ylab = "social effect",
main = "Trace plot for social effect, s' ", lwd = 2)
plot(Jumbo[burnin:its, 2], type = "l", col = "red", ylab = "asocial effect",
main = "Trace plot for asocial effect, lambda0' ", lwd = 2)
plot(Jumbo[burnin:its, 3], type = "l", col = "lightgoldenrod",
ylab = "asocial effect", main = "Trace plot for spatial effect, beta0' ",
lwd = 2)
params = c(mean(Jumbo[burnin:its, 1]), mean(Jumbo[burnin:its,
2]), mean(Jumbo[burnin:its, 3]))
creds = c(sd(Jumbo[burnin:its, 1]), sd(Jumbo[burnin:its,
2]), sd(Jumbo[burnin:its, 3]))
mcmcresults = list(Jumbo, params, creds)
mcmcresults
}
#---------------------------------------------------------------
# Run spatial NBDA to estimate the social and asocial parameters
# s and lambda.
# The associations for the social network in this example are calculated
# using an interaction function that assumes each individual has
# an area of interaction or zone of influence.
#---------------------------------------------------------------
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()
smcmc(shape,10000,5,1,1,-5,-6,-5)
proc.time() - ptm
|
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