scripts/WinBUGS/_archive/WinBUGS_with-CAR.R
In n8thangreen/STIecoPredict: Multilevel Regression With Post-Stratification Using Natsal Data
# http://data.princeton.edu/pop510/hospBUGS.html
# run R as __administrator__
rm(list = ls())
library(MASS)
library(R2WinBUGS)
library(plyr)
library(lattice)
library(mcmcplots)
library(Hmisc)
# load("C:/Users/ngreen1/Dropbox/small-area & chlamydia/R_code/scripts/mrp/data/cleaned-regn-input-mrpNatsal.RData")
load("../../../mrp/data/cleaned-regn-input-mrpNatsal.RData")
load("../../../mrp/data/adjacency_matrix_england-list.RData")
###############
## prep data ##
###############
adjacency_matrix.england$num <- unname(adjacency_matrix.england$num)
adjacency_matrix.england$adj <- unname(adjacency_matrix.england$adj)
# convert all factors to integer from 1 to max
# and then map this back to original name at the end
Natsal0 <- Natsal
Natsal <- subset(Natsal, age>=15 & age<25) #NCSP range
# Natsal <- subset(Natsal, age>=15 & age<45) #boost sampled range
## for consistent cuts across arrays
## use post-stratification array as baseline
interiorcuts <- cut2(sim_prop_la$Conception.rate.per.1.000.women.in.age.group, g=10, onlycuts = T)
conception.under18$Conception.decile <- cut2(conception.under18$Conception.rate.per.1.000.women.in.age.group, cuts = interiorcuts)
# convert all factors to integer from 1 to max
# and then map this back to original name at the end
LA_factor.name <- droplevels(as.factor(Natsal$laname))
metUA_factor.name <- droplevels(as.factor(Natsal$metcounty_UA))
ethnic2.name <- droplevels(as.factor(Natsal$ethnic2))
ONS.name <- droplevels(as.factor(Natsal$`Numerical classification`))
gor.name <- droplevels(as.factor(Natsal$gor))
conception.name <- droplevels(as.factor(Natsal$Conception.decile))
gorLondon <- Natsal$gor==9
IMD.upperQ <- Natsal$`Average Score`>28
##TODO##
IMD.upperQ[is.na(IMD.upperQ)] <- FALSE ##TODO## fix original join (herefordshire, hull, peterborough, stoke on trent)
conception.name[is.na(conception.name)] <- levels(conception.name)[6] #"[33.1,35.8)" ##TODO## fix original join (city of london, cornwall, hackney, southend-on-sea)
ONS.name[is.na(ONS.name)] <- 3 ##TODO## fix join
## create look-up table for values only in sample
## this is useful to map back to the original names from which the integers are based
LA_factor_lookup <- data.frame(`LA.Name` = levels(LA_factor.name),
`la_factor` = sort(unique(as.numeric(LA_factor.name))))
LA_factor_lookup <- merge(LA_factor_lookup, laregionlookup2013, by.x="LA.Name", by.y="la_name", all.x=TRUE) #removes LAs not in sample
LA_factor_lookup <- merge(LA_factor_lookup, LAclassification.dat[,c("LA Name", "Classification", "Numerical classification")],
by.x = "LA.Name", by.y = "LA Name", all.x = TRUE)
LA_factor_lookup <- merge(LA_factor_lookup, IMD.dat2010[,c("LA Name", "Average Score")],
by.x = "LA.Name", by.y = "LA Name", all.x = TRUE)
LA_factor_lookup <- merge(LA_factor_lookup, conception.under18[,c("Name", "Conception.decile")],
by.x = "LA.Name", by.y = "Name", all.x = TRUE)
LA_factor_lookup <- within(LA_factor_lookup, london <- as.numeric(gor==9))
LA_factor_lookup$IMD.upperQ <- LA_factor_lookup$"Average Score">28
LA_factor_lookup$metcounty_UA.factor <- as.numeric(LA_factor_lookup$metcounty_UA)
LA_factor_lookup$metcounty_UA <- toupper(LA_factor_lookup$metcounty_UA) #move to prep file...
LA_factor_lookup$"Numerical classification" <- droplevels(as.factor(LA_factor_lookup$"Numerical classification"))
LA_factor_lookup$metcounty_UA <- droplevels(as.factor(LA_factor_lookup$metcounty_UA))
LA_factor_lookup$gor <- droplevels(as.factor(LA_factor_lookup$gor))
LA_factor_lookup$Conception.decile <- droplevels(as.factor(LA_factor_lookup$Conception.decile))
LA_factor_lookup$london <- droplevels(as.factor(LA_factor_lookup$london))
## match factor levels in Natsal and lookup
LA_factor.name <- factor(LA_factor.name, levels = levels(LA_factor_lookup$LA.Name))
metUA_factor.name <- factor(metUA_factor.name, levels = levels(LA_factor_lookup$metcounty_UA))
ONS.name <- factor(ONS.name, levels = levels(LA_factor_lookup$`Numerical classification`))
# conception.name <- factor(conception.name, levels = levels(LA_factor_lookup$Conception.decile))
WinBUGSdata <- data.frame(cttestly = Natsal$cttestly,
student = as.numeric(Natsal$student),
male = as.numeric(Natsal$rsex)-1, #1-male, 0-female
dage = Natsal$dage,
age = Natsal$dage-min(Natsal$dage)+1,
ethngrp = as.numeric(ethnic2.name),
IMD.upperQ = as.numeric(IMD.upperQ),
gor = as.numeric(gor.name),
metcounty_UA = as.numeric(metUA_factor.name),
gorLondon = as.numeric(gorLondon),
ONSclass = as.numeric(ONS.name),
la_factor = as.numeric(LA_factor.name),
smokenow = as.numeric(Natsal$smokenow),
conception = as.numeric(conception.name)
)
Nage <- length(table(WinBUGSdata$age))
Nla <- length(table(WinBUGSdata$la_factor))
Nethgrp <- length(table(WinBUGSdata$ethngrp))
NONSclass <- length(table(WinBUGSdata$ONSclass))
Nmetcounty_UA <- length(table(WinBUGSdata$metcounty_UA))
Nconception <- length(table(WinBUGSdata$conception))
Ngor <- length(table(WinBUGSdata$gor))
###########
## model ##
###########
# setwd("C:/Users/ngreen1/Dropbox/small-area & chlamydia/R_code/scripts")
setwd("../../../WinBUGS/temp_WinBUGS_output")
sink("model.txt")
cat("
model
{
# Hyperpriors
##TODO##
#put uniform of variance and then take 1/x
tau.age ~ dgamma(0.001, 0.001)
tau.ethngrp ~ dgamma(0.001, 0.001)
tau.la ~ dgamma(0.001, 0.001)
tau.ons ~ dgamma(0.001, 0.001)
tau.met ~ dgamma(0.001, 0.001)
tau.gor ~ dgamma(0.001, 0.001)
tau.conception ~ dgamma(0.001, 0.001)
tau.car ~ dgamma(0.001, 0.001)
sd.car <- sd(car[])
sd.r <- sd(r[])
lambda <- sd.car/(sd.car+sd.r)
# Priors
##TODO## alpha ~ dflat() ## the 'improper' flat distn as intercept for CAR model?
alpha ~ dunif(-2,1)
b.male ~ dunif(-2,1)
b.IMD ~ dunif(-2,1)
b.student ~ dunif(-2,1)
# Likelihood
for(i in 1:n) {
cttestly[i] ~ dbin(p[i], N)
logit(p[i]) <- alpha + b.male*male[i] + b.student*student[i] + b.IMD*IMD.upperQ[i] +
v[age[i]] + w[ethgrp[i]] +
r[la.factor[i]] +
g[metcounty.UA[i]] +
h[gor[i]] +
u[ONSclass[i]] + d[conception[i]] +
car[la.factor[i]]
}
# age
for(k in 1:K) {
v[k] ~ dnorm(0, tau.age)
}
# ethnic group
for(l in 1:L) {
w[l] ~ dnorm(0, tau.ethngrp)
}
# LA
for(s in 1:S) {
r[s] ~ dnorm(0, tau.la)
}
# met county
for(m in 1:M) {
g[m] ~ dnorm(0, tau.met)
}
# Region
for(q in 1:Q) {
h[q] ~ dnorm(0, tau.gor)
}
# ONS urban-rural classification
for(j in 1:J) {
u[j] ~ dnorm(0, tau.ons)
}
# <=18 years old conception rate
for(e in 1:E) {
d[e] ~ dnorm(0, tau.conception)
}
# neighbour contribution
for (nn in 1:sumNumNeigh) {
weights[nn] <- 1
}
# spatially structured errors
car[1:326] ~ car.normal(adj[], weights[], num[], tau.car)
}
", fill=TRUE)
sink()
########################
## WinBUGS input data ##
########################
win.data <- list(male = WinBUGSdata$male,
age = WinBUGSdata$age,
student = WinBUGSdata$student,
ethgrp = WinBUGSdata$ethngrp,
ONSclass = WinBUGSdata$ONSclass,
la.factor = WinBUGSdata$la_factor,
metcounty.UA = WinBUGSdata$metcounty_UA,
gor = WinBUGSdata$gor,
conception = WinBUGSdata$conception,
IMD.upperQ = WinBUGSdata$IMD.upperQ,
cttestly = WinBUGSdata$cttestly,
n = nrow(WinBUGSdata),
K = Nage, L = Nethgrp, J = NONSclass, E = Nconception,
S = Nla,
M = Nmetcounty_UA,
Q = Ngor,
sumNumNeigh = adjacency_matrix.england$sumNumNeigh,
adj = adjacency_matrix.england$adj,
num = adjacency_matrix.england$num,
N = 1)
# Initial values function
# inits <- function(){ list(alpha=rlnorm(1), b.age=rlnorm(1), b.male=rlnorm(1))}
inits <- list(
list(alpha=-1, b.male=-1, b.student=0, b.IMD=0,
tau.age=0.1, tau.ethngrp=2, tau.ons=0.4, tau.conception=0.1, tau.gor=0.1, tau.la=1, tau.met=0.1,
tau.car=0.3),
list(alpha=-1, b.male=-1, b.student=0, b.IMD=0,
tau.age=0.1, tau.ethngrp=3, tau.ons=0.1, tau.conception=0.2, tau.gor=0.1, tau.la=2, tau.met=0.2,
tau.car=0.1),
list(alpha=-1, b.male=-1, b.student=0, b.IMD=0,
tau.age=0.5, tau.ethngrp=1, tau.ons=0.2, tau.conception=0.3, tau.gor=0.1, tau.la=3, tau.met=0.3,
tau.car=0.2)
)
# Parameters to estimate (output)
params <- c("alpha", "b.male", "b.student", "b.IMD",
"tau.age", "tau.ethngrp", "tau.ons", "tau.conception", "tau.gor", "tau.la", "tau.met", "tau.car",
"v", "w", "u", "d", "h", "r", "g", "car", "lambda")
# MCMC settings
nc <- 3 #Number of Chains
ni <- 100#100000 #Number of draws from posterior
nb <- 2#00 #Number of draws to discard as burn-in
nt <- 2#50 #Thinning rate
bugsDir <- 'C:/Program Files (x86)/WinBUGS14'
# bugsDir <- 'C:/Users/nathan.green.PHE/Documents/WinBUGS14'
# Start Gibbs sampling
out <- bugs(data=win.data, inits=inits, parameters.to.save=params,
model.file="model.txt", n.thin=nt, n.chains=nc, n.burnin=nb,
n.iter=ni, debug = TRUE, DIC = TRUE,
working.directory = getwd(),
bugs.directory = bugsDir)
save(out, file="MCMCoutput.RData")
outmcmc <- as.mcmc.list(out)
##################
## clean output ##
##################
simpleCap <- function(x) {
s <- strsplit(x, " ")[[1]]
paste(toupper(substring(s, 1,1)), tolower(substring(s, 2)),
sep="", collapse=" ")
}
ethngrp.names <- sapply(levels(ethnic2.name), simpleCap) #w #levels(Natsal$ethnic2)[sort(unique(as.numeric(Natsal$ethnic2)))]
age.names <- as.character(sort(unique(WinBUGSdata$dage))) #v
la.names <- sapply(as.character(levels(LA_factor.name)), simpleCap) #r
gor.names <- as.character(levels(gor.name)) #h
metUA.names <- sapply(as.character(levels(metUA_factor.name)), simpleCap) #g
UrbRur.names <- as.character(levels(ONS.name)) #u
conception.names <- as.character(levels(conception.name)) #d
## note that the ONS classifications are numbers as well as ages but they do not overlap
## recover the original factor values from the level numbers
## if not directly available
gor.names <- as.character(unique(merge(data.frame(gor.names), LA_factor_lookup[,c("gor","region_name")],
by.x="gor.names", by.y="gor", all.x=T, all=F, sort = F))$region_name)
gor.names <- sapply(gor.names, simpleCap)
UrbRur.names <- as.character(unique(merge(data.frame(UrbRur.names), LA_factor_lookup[,c("Classification","Numerical classification")],
by.x="UrbRur.names", by.y="Numerical classification", all.x=T, all=F, sort = F))$Classification)
## read results back in
coda1.file <- "../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/coda1.txt"
coda2.file <- "../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/coda2.txt"
coda3.file <- "../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/coda3.txt"
##or
# coda1.file <- "../temp_WinBUGS_output/coda1.txt"
# coda2.file <- "../temp_WinBUGS_output/coda2.txt"
# coda3.file <- "../temp_WinBUGS_output/coda3.txt"
outmcmc <- read.bugs(c(coda1.file, coda2.file, coda3.file))
## alternative approach
# coda1 <- read.delim(coda1.file, header=FALSE)
# codaIndex <- read.delim("../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/codaIndex.txt", header=FALSE)
# # codaIndex <- read.delim("../temp_WinBUGS_output/codaIndex.txt", header=FALSE)
# outmat <- data.frame(matrix(coda1$V2, nrow = min(which(coda1$V1==max(coda1$V1)))))
# names(outmat) <- codaIndex$V1
outmat <- plyr::ldply(outmcmc, data.frame)
## replace parameter names with proper labels
## read.bugs
names(outmat)[grepl("w\\.[1234567890]+", names(outmat))] <- ethngrp.names
names(outmat)[grepl("v\\.[1234567890]+", names(outmat))] <- age.names
names(outmat)[grepl("r\\.[1234567890]+", names(outmat))] <- la.names
names(outmat)[grepl("g\\.[1234567890]+", names(outmat))] <- metUA.names
names(outmat)[grepl("h\\.[1234567890]+", names(outmat))] <- gor.names
names(outmat)[grepl("u\\.[1234567890]+", names(outmat))] <- UrbRur.names
names(outmat)[grepl("d\\.[1234567890]+", names(outmat))] <- conception.names
## read.delim
# names(outmat)[grepl("w\\[.*\\]", names(outmat))] <- ethngrp.names
# names(outmat)[grepl("v\\[.*\\]", names(outmat))] <- age.names
# names(outmat)[grepl("r\\[.*\\]", names(outmat))] <- la.names
# names(outmat)[grepl("g\\[.*\\]", names(outmat))] <- metUA.names
# names(outmat)[grepl("h\\[.*\\]", names(outmat))] <- gor.names
# names(outmat)[grepl("u\\[.*\\]", names(outmat))] <- UrbRur.names
# names(outmat)[grepl("d\\[.*\\]", names(outmat))] <- conception.names
save(outmat, file="../mrp/data/outmat.RData")
######################
## diagnostic plots ##
######################
## using mcmc object
par(mar=c(5,15,4,2))
caterplot(outmcmc, lwd=c(1,4), style = "plain", col="black", pch=19, reorder=FALSE, axes=F, cex.labels=1.2,
parms = c("w", "b.male", "b.student", "d", "h", "u"),
labels = c(ethngrp.names, "Male", "Student", conception.names, gor.names, UrbRur.names))
axis(1); axis(3)
abline(v=0, lty=2, col="red")
## LAs only
par(mar=c(5,5,4,2))
caterplot(outmcmc, lwd=c(1,2), style = "plain", col="black", pch=19, reorder=TRUE, axes=F, labels = NA,
parms = c("r"))
axis(1); axis(3)
abline(v=0, lty=2, col="red")
## metUA only
par(mar=c(5,5,4,2))
caterplot(outmcmc, lwd=c(1,2), style = "plain", col="black", pch=19, reorder=TRUE, axes=F, labels = NA,
parms = c("g"))
axis(1); axis(3)
abline(v=0, lty=2, col="red")
## age only
par(mar=c(5,10,4,2))
caterplot(outmcmc, style = "plain", col="black", pch=19, reorder=FALSE, axes=F, cex.labels=0.8,
parms = c("v"),
labels = age.names)
axis(1); axis(3)
abline(v=0, lty=2, col="red")
mcmcplot(outmcmc)
n8thangreen/STIecoPredict documentation built on June 7, 2020, 12:50 p.m.
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# http://data.princeton.edu/pop510/hospBUGS.html
# run R as __administrator__
rm(list = ls())
library(MASS)
library(R2WinBUGS)
library(plyr)
library(lattice)
library(mcmcplots)
library(Hmisc)
# load("C:/Users/ngreen1/Dropbox/small-area & chlamydia/R_code/scripts/mrp/data/cleaned-regn-input-mrpNatsal.RData")
load("../../../mrp/data/cleaned-regn-input-mrpNatsal.RData")
load("../../../mrp/data/adjacency_matrix_england-list.RData")
###############
## prep data ##
###############
adjacency_matrix.england$num <- unname(adjacency_matrix.england$num)
adjacency_matrix.england$adj <- unname(adjacency_matrix.england$adj)
# convert all factors to integer from 1 to max
# and then map this back to original name at the end
Natsal0 <- Natsal
Natsal <- subset(Natsal, age>=15 & age<25) #NCSP range
# Natsal <- subset(Natsal, age>=15 & age<45) #boost sampled range
## for consistent cuts across arrays
## use post-stratification array as baseline
interiorcuts <- cut2(sim_prop_la$Conception.rate.per.1.000.women.in.age.group, g=10, onlycuts = T)
conception.under18$Conception.decile <- cut2(conception.under18$Conception.rate.per.1.000.women.in.age.group, cuts = interiorcuts)
# convert all factors to integer from 1 to max
# and then map this back to original name at the end
LA_factor.name <- droplevels(as.factor(Natsal$laname))
metUA_factor.name <- droplevels(as.factor(Natsal$metcounty_UA))
ethnic2.name <- droplevels(as.factor(Natsal$ethnic2))
ONS.name <- droplevels(as.factor(Natsal$`Numerical classification`))
gor.name <- droplevels(as.factor(Natsal$gor))
conception.name <- droplevels(as.factor(Natsal$Conception.decile))
gorLondon <- Natsal$gor==9
IMD.upperQ <- Natsal$`Average Score`>28
##TODO##
IMD.upperQ[is.na(IMD.upperQ)] <- FALSE ##TODO## fix original join (herefordshire, hull, peterborough, stoke on trent)
conception.name[is.na(conception.name)] <- levels(conception.name)[6] #"[33.1,35.8)" ##TODO## fix original join (city of london, cornwall, hackney, southend-on-sea)
ONS.name[is.na(ONS.name)] <- 3 ##TODO## fix join
## create look-up table for values only in sample
## this is useful to map back to the original names from which the integers are based
LA_factor_lookup <- data.frame(`LA.Name` = levels(LA_factor.name),
`la_factor` = sort(unique(as.numeric(LA_factor.name))))
LA_factor_lookup <- merge(LA_factor_lookup, laregionlookup2013, by.x="LA.Name", by.y="la_name", all.x=TRUE) #removes LAs not in sample
LA_factor_lookup <- merge(LA_factor_lookup, LAclassification.dat[,c("LA Name", "Classification", "Numerical classification")],
by.x = "LA.Name", by.y = "LA Name", all.x = TRUE)
LA_factor_lookup <- merge(LA_factor_lookup, IMD.dat2010[,c("LA Name", "Average Score")],
by.x = "LA.Name", by.y = "LA Name", all.x = TRUE)
LA_factor_lookup <- merge(LA_factor_lookup, conception.under18[,c("Name", "Conception.decile")],
by.x = "LA.Name", by.y = "Name", all.x = TRUE)
LA_factor_lookup <- within(LA_factor_lookup, london <- as.numeric(gor==9))
LA_factor_lookup$IMD.upperQ <- LA_factor_lookup$"Average Score">28
LA_factor_lookup$metcounty_UA.factor <- as.numeric(LA_factor_lookup$metcounty_UA)
LA_factor_lookup$metcounty_UA <- toupper(LA_factor_lookup$metcounty_UA) #move to prep file...
LA_factor_lookup$"Numerical classification" <- droplevels(as.factor(LA_factor_lookup$"Numerical classification"))
LA_factor_lookup$metcounty_UA <- droplevels(as.factor(LA_factor_lookup$metcounty_UA))
LA_factor_lookup$gor <- droplevels(as.factor(LA_factor_lookup$gor))
LA_factor_lookup$Conception.decile <- droplevels(as.factor(LA_factor_lookup$Conception.decile))
LA_factor_lookup$london <- droplevels(as.factor(LA_factor_lookup$london))
## match factor levels in Natsal and lookup
LA_factor.name <- factor(LA_factor.name, levels = levels(LA_factor_lookup$LA.Name))
metUA_factor.name <- factor(metUA_factor.name, levels = levels(LA_factor_lookup$metcounty_UA))
ONS.name <- factor(ONS.name, levels = levels(LA_factor_lookup$`Numerical classification`))
# conception.name <- factor(conception.name, levels = levels(LA_factor_lookup$Conception.decile))
WinBUGSdata <- data.frame(cttestly = Natsal$cttestly,
student = as.numeric(Natsal$student),
male = as.numeric(Natsal$rsex)-1, #1-male, 0-female
dage = Natsal$dage,
age = Natsal$dage-min(Natsal$dage)+1,
ethngrp = as.numeric(ethnic2.name),
IMD.upperQ = as.numeric(IMD.upperQ),
gor = as.numeric(gor.name),
metcounty_UA = as.numeric(metUA_factor.name),
gorLondon = as.numeric(gorLondon),
ONSclass = as.numeric(ONS.name),
la_factor = as.numeric(LA_factor.name),
smokenow = as.numeric(Natsal$smokenow),
conception = as.numeric(conception.name)
)
Nage <- length(table(WinBUGSdata$age))
Nla <- length(table(WinBUGSdata$la_factor))
Nethgrp <- length(table(WinBUGSdata$ethngrp))
NONSclass <- length(table(WinBUGSdata$ONSclass))
Nmetcounty_UA <- length(table(WinBUGSdata$metcounty_UA))
Nconception <- length(table(WinBUGSdata$conception))
Ngor <- length(table(WinBUGSdata$gor))
###########
## model ##
###########
# setwd("C:/Users/ngreen1/Dropbox/small-area & chlamydia/R_code/scripts")
setwd("../../../WinBUGS/temp_WinBUGS_output")
sink("model.txt")
cat("
model
{
# Hyperpriors
##TODO##
#put uniform of variance and then take 1/x
tau.age ~ dgamma(0.001, 0.001)
tau.ethngrp ~ dgamma(0.001, 0.001)
tau.la ~ dgamma(0.001, 0.001)
tau.ons ~ dgamma(0.001, 0.001)
tau.met ~ dgamma(0.001, 0.001)
tau.gor ~ dgamma(0.001, 0.001)
tau.conception ~ dgamma(0.001, 0.001)
tau.car ~ dgamma(0.001, 0.001)
sd.car <- sd(car[])
sd.r <- sd(r[])
lambda <- sd.car/(sd.car+sd.r)
# Priors
##TODO## alpha ~ dflat() ## the 'improper' flat distn as intercept for CAR model?
alpha ~ dunif(-2,1)
b.male ~ dunif(-2,1)
b.IMD ~ dunif(-2,1)
b.student ~ dunif(-2,1)
# Likelihood
for(i in 1:n) {
cttestly[i] ~ dbin(p[i], N)
logit(p[i]) <- alpha + b.male*male[i] + b.student*student[i] + b.IMD*IMD.upperQ[i] +
v[age[i]] + w[ethgrp[i]] +
r[la.factor[i]] +
g[metcounty.UA[i]] +
h[gor[i]] +
u[ONSclass[i]] + d[conception[i]] +
car[la.factor[i]]
}
# age
for(k in 1:K) {
v[k] ~ dnorm(0, tau.age)
}
# ethnic group
for(l in 1:L) {
w[l] ~ dnorm(0, tau.ethngrp)
}
# LA
for(s in 1:S) {
r[s] ~ dnorm(0, tau.la)
}
# met county
for(m in 1:M) {
g[m] ~ dnorm(0, tau.met)
}
# Region
for(q in 1:Q) {
h[q] ~ dnorm(0, tau.gor)
}
# ONS urban-rural classification
for(j in 1:J) {
u[j] ~ dnorm(0, tau.ons)
}
# <=18 years old conception rate
for(e in 1:E) {
d[e] ~ dnorm(0, tau.conception)
}
# neighbour contribution
for (nn in 1:sumNumNeigh) {
weights[nn] <- 1
}
# spatially structured errors
car[1:326] ~ car.normal(adj[], weights[], num[], tau.car)
}
", fill=TRUE)
sink()
########################
## WinBUGS input data ##
########################
win.data <- list(male = WinBUGSdata$male,
age = WinBUGSdata$age,
student = WinBUGSdata$student,
ethgrp = WinBUGSdata$ethngrp,
ONSclass = WinBUGSdata$ONSclass,
la.factor = WinBUGSdata$la_factor,
metcounty.UA = WinBUGSdata$metcounty_UA,
gor = WinBUGSdata$gor,
conception = WinBUGSdata$conception,
IMD.upperQ = WinBUGSdata$IMD.upperQ,
cttestly = WinBUGSdata$cttestly,
n = nrow(WinBUGSdata),
K = Nage, L = Nethgrp, J = NONSclass, E = Nconception,
S = Nla,
M = Nmetcounty_UA,
Q = Ngor,
sumNumNeigh = adjacency_matrix.england$sumNumNeigh,
adj = adjacency_matrix.england$adj,
num = adjacency_matrix.england$num,
N = 1)
# Initial values function
# inits <- function(){ list(alpha=rlnorm(1), b.age=rlnorm(1), b.male=rlnorm(1))}
inits <- list(
list(alpha=-1, b.male=-1, b.student=0, b.IMD=0,
tau.age=0.1, tau.ethngrp=2, tau.ons=0.4, tau.conception=0.1, tau.gor=0.1, tau.la=1, tau.met=0.1,
tau.car=0.3),
list(alpha=-1, b.male=-1, b.student=0, b.IMD=0,
tau.age=0.1, tau.ethngrp=3, tau.ons=0.1, tau.conception=0.2, tau.gor=0.1, tau.la=2, tau.met=0.2,
tau.car=0.1),
list(alpha=-1, b.male=-1, b.student=0, b.IMD=0,
tau.age=0.5, tau.ethngrp=1, tau.ons=0.2, tau.conception=0.3, tau.gor=0.1, tau.la=3, tau.met=0.3,
tau.car=0.2)
)
# Parameters to estimate (output)
params <- c("alpha", "b.male", "b.student", "b.IMD",
"tau.age", "tau.ethngrp", "tau.ons", "tau.conception", "tau.gor", "tau.la", "tau.met", "tau.car",
"v", "w", "u", "d", "h", "r", "g", "car", "lambda")
# MCMC settings
nc <- 3 #Number of Chains
ni <- 100#100000 #Number of draws from posterior
nb <- 2#00 #Number of draws to discard as burn-in
nt <- 2#50 #Thinning rate
bugsDir <- 'C:/Program Files (x86)/WinBUGS14'
# bugsDir <- 'C:/Users/nathan.green.PHE/Documents/WinBUGS14'
# Start Gibbs sampling
out <- bugs(data=win.data, inits=inits, parameters.to.save=params,
model.file="model.txt", n.thin=nt, n.chains=nc, n.burnin=nb,
n.iter=ni, debug = TRUE, DIC = TRUE,
working.directory = getwd(),
bugs.directory = bugsDir)
save(out, file="MCMCoutput.RData")
outmcmc <- as.mcmc.list(out)
##################
## clean output ##
##################
simpleCap <- function(x) {
s <- strsplit(x, " ")[[1]]
paste(toupper(substring(s, 1,1)), tolower(substring(s, 2)),
sep="", collapse=" ")
}
ethngrp.names <- sapply(levels(ethnic2.name), simpleCap) #w #levels(Natsal$ethnic2)[sort(unique(as.numeric(Natsal$ethnic2)))]
age.names <- as.character(sort(unique(WinBUGSdata$dage))) #v
la.names <- sapply(as.character(levels(LA_factor.name)), simpleCap) #r
gor.names <- as.character(levels(gor.name)) #h
metUA.names <- sapply(as.character(levels(metUA_factor.name)), simpleCap) #g
UrbRur.names <- as.character(levels(ONS.name)) #u
conception.names <- as.character(levels(conception.name)) #d
## note that the ONS classifications are numbers as well as ages but they do not overlap
## recover the original factor values from the level numbers
## if not directly available
gor.names <- as.character(unique(merge(data.frame(gor.names), LA_factor_lookup[,c("gor","region_name")],
by.x="gor.names", by.y="gor", all.x=T, all=F, sort = F))$region_name)
gor.names <- sapply(gor.names, simpleCap)
UrbRur.names <- as.character(unique(merge(data.frame(UrbRur.names), LA_factor_lookup[,c("Classification","Numerical classification")],
by.x="UrbRur.names", by.y="Numerical classification", all.x=T, all=F, sort = F))$Classification)
## read results back in
coda1.file <- "../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/coda1.txt"
coda2.file <- "../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/coda2.txt"
coda3.file <- "../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/coda3.txt"
##or
# coda1.file <- "../temp_WinBUGS_output/coda1.txt"
# coda2.file <- "../temp_WinBUGS_output/coda2.txt"
# coda3.file <- "../temp_WinBUGS_output/coda3.txt"
outmcmc <- read.bugs(c(coda1.file, coda2.file, coda3.file))
## alternative approach
# coda1 <- read.delim(coda1.file, header=FALSE)
# codaIndex <- read.delim("../WinBUGS/WinBUGS_MAIN/WinBUGS_MAIN-all_LA-vars/codaIndex.txt", header=FALSE)
# # codaIndex <- read.delim("../temp_WinBUGS_output/codaIndex.txt", header=FALSE)
# outmat <- data.frame(matrix(coda1$V2, nrow = min(which(coda1$V1==max(coda1$V1)))))
# names(outmat) <- codaIndex$V1
outmat <- plyr::ldply(outmcmc, data.frame)
## replace parameter names with proper labels
## read.bugs
names(outmat)[grepl("w\\.[1234567890]+", names(outmat))] <- ethngrp.names
names(outmat)[grepl("v\\.[1234567890]+", names(outmat))] <- age.names
names(outmat)[grepl("r\\.[1234567890]+", names(outmat))] <- la.names
names(outmat)[grepl("g\\.[1234567890]+", names(outmat))] <- metUA.names
names(outmat)[grepl("h\\.[1234567890]+", names(outmat))] <- gor.names
names(outmat)[grepl("u\\.[1234567890]+", names(outmat))] <- UrbRur.names
names(outmat)[grepl("d\\.[1234567890]+", names(outmat))] <- conception.names
## read.delim
# names(outmat)[grepl("w\\[.*\\]", names(outmat))] <- ethngrp.names
# names(outmat)[grepl("v\\[.*\\]", names(outmat))] <- age.names
# names(outmat)[grepl("r\\[.*\\]", names(outmat))] <- la.names
# names(outmat)[grepl("g\\[.*\\]", names(outmat))] <- metUA.names
# names(outmat)[grepl("h\\[.*\\]", names(outmat))] <- gor.names
# names(outmat)[grepl("u\\[.*\\]", names(outmat))] <- UrbRur.names
# names(outmat)[grepl("d\\[.*\\]", names(outmat))] <- conception.names
save(outmat, file="../mrp/data/outmat.RData")
######################
## diagnostic plots ##
######################
## using mcmc object
par(mar=c(5,15,4,2))
caterplot(outmcmc, lwd=c(1,4), style = "plain", col="black", pch=19, reorder=FALSE, axes=F, cex.labels=1.2,
parms = c("w", "b.male", "b.student", "d", "h", "u"),
labels = c(ethngrp.names, "Male", "Student", conception.names, gor.names, UrbRur.names))
axis(1); axis(3)
abline(v=0, lty=2, col="red")
## LAs only
par(mar=c(5,5,4,2))
caterplot(outmcmc, lwd=c(1,2), style = "plain", col="black", pch=19, reorder=TRUE, axes=F, labels = NA,
parms = c("r"))
axis(1); axis(3)
abline(v=0, lty=2, col="red")
## metUA only
par(mar=c(5,5,4,2))
caterplot(outmcmc, lwd=c(1,2), style = "plain", col="black", pch=19, reorder=TRUE, axes=F, labels = NA,
parms = c("g"))
axis(1); axis(3)
abline(v=0, lty=2, col="red")
## age only
par(mar=c(5,10,4,2))
caterplot(outmcmc, style = "plain", col="black", pch=19, reorder=FALSE, axes=F, cex.labels=0.8,
parms = c("v"),
labels = age.names)
axis(1); axis(3)
abline(v=0, lty=2, col="red")
mcmcplot(outmcmc)
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