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RealDataApplication.r
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################################################################################
# NLCTS REAL DATA APPLICATION #
################################################################################
library(mvtnorm)
library(numDeriv)
library(Matrix)
library(MASS)
library(plyr)
library(testit)
library(Metrics)
library(haven)
library(tidyr)
library(labelled)
library(progress)
getwd()
dirname(rstudioapi::getSourceEditorContext()$path)
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
getwd()
A = read.table(file.path("data", "A1982.txt"))
head(A)
summary(A)
B = read.table(file.path("data", "B1994.txt"))
head(B)
summary(B)
regTimeA = A$time
regTimeB = B$time
pivs = c("sex", "dob_yy", "dob_mm", "dob_dd", "reg", "state")
pivs_stable = c(TRUE, TRUE, TRUE, TRUE, FALSE, FALSE)
Linked_id = intersect(A$seq, B$seq)
Nlinks = length(Linked_id)
print("Size of the overlapping set:")
print(Nlinks)
print("Represented as a fraction of the smallest file:")
print(Nlinks / min(nrow(A), nrow(B)))
nvalues = rep(0, length(pivs))
all = rbind( A[,pivs], B[,pivs])
for (k in 1:length(pivs)){
unique_values = unique(all[,k])
unique_values = unique_values[unique_values!=0]
nvalues[[k]] = length(unique_values)
}
nvalues # c(2, 36, 12, 31, 12, 47)
# Add covariates for unstable PIVs survival model
pSameH.varA = list(c(), c(), c(), c(), c("helpertype", "helperpresent"), c("helpertype", "helperpresent"))
pSameH.varB = list(c(), c(), c(), c(), c("helpertype", "helperdo1", "helperdo2"), c("helpertype", "helperdo1", "helperdo2"))
XA = data.frame(A)
XB = data.frame(B)
################################################################################
# LAUNCH THE ALGORITHM ON REAL DATA #
################################################################################
dataNLTCS = list( pivsA = A[,pivs],
pivsB = B[,pivs],
nvalues = nvalues,
pivs_stable = pivs_stable,
regTimeA = regTimeA,
regTimeB = regTimeB,
XA = XA,
XB = XB,
pSameH.varA = pSameH.varA,
pSameH.varB = pSameH.varB)
source("recordlinkage.r")
Rcpp:::sourceCpp("functions.cpp")
fit = stEM( data = dataNLTCS,
nIter = 250,
nBurnin = 25,
MStepIter = 25,
trace = 1 )
################################################################################
# VISUALISE THE RESULTS #
################################################################################
par(mfrow=c(1,2))
Delta = matrix(0, nrow=nrow(dataNLTCS$pivsA), ncol=nrow(dataNLTCS$pivsB))
for (lr in 1:Nlinks)
{
i = which(A$seq==Linked_id[lr])
j = which(B$seq==Linked_id[lr])
Delta[i,j] = 1
}
image(x=1:nrow(dataNLTCS$pivsB), y=1:nrow(dataNLTCS$pivsA), z=t(as.matrix(Delta)), xlab="obs. in B", ylab="obs. in A")
title(main = c("True linkage matrix", "underlying the data", sprintf("%s linked record pairs", sum(Delta))), font.main = 4)
image(x=1:nrow(dataNLTCS$pivsB), y=1:nrow(dataNLTCS$pivsA), z=t(as.matrix(fit$Delta)), xlab="obs. in B", ylab="obs. in A")
title(main = c("Estimated linkage matrix fitting the data","taking into account unstability in PIVs", sprintf("%s linked record pairs (proba > .5)", sum(fit$Delta>0.5))), font.main = 4)
par(mfrow=c(1,1))
# gamma
gamma = fit$gamma
plot(gamma, type="l", ylim=c(0,1), xlab = "MC-EM Iterations", ylab = "gamma")
abline(h = Nlinks/min(nrow(A), nrow(B)), col="red")
par(mfrow=c(2,3))
# eta
eta = fit$eta
for(k in 1:length(eta))
{
vec = c("eta 1")
plot(eta[[k]][,1], ylim=c(0,1), type="l", col=1, xlab = "MC-EM Iterations", ylab = sprintf("PIV %s", pivs[k]))
for(j in 2:ncol(eta[[k]]))
{
lines(eta[[k]][,j], ylim=c(0,1), col=j)
vec <- append(vec, sprintf("eta %s", j))
}
legend("topright", legend=vec, col=seq_len(ncol(eta[[k]])), lty=1:4, cex=0.8)
}
par(mfrow=c(2,3))
# omega
omega = fit$omega
for(k in 1:length(omega))
{
if(!dataNLTCS$pivs_stable[k]){
vec = c("time difference")
plot(omega[[k]][,1], ylim=c(min(omega[[k]])-0.5, max(omega[[k]])+0.5), type="l", col=1, xlab = "MC-EM Iterations", ylab = sprintf("Survival model coef. for unstable PIV %s", pivs[k]))
if(ncol(omega[[k]])>=2){
vec <- append(vec, pSameH.varA[[k]])
vec <- append(vec, pSameH.varB[[k]])
for(c in 2:ncol(omega[[k]]))
{
lines(omega[[k]][,c], col=c)
}
}
legend("topright", legend=vec, col=c(1:ncol(omega[[k]])), lty=1:4, cex=0.8)
}
}
par(mfrow=c(2,3))
# phi
phi = fit$phi
for(k in 1:length(phi))
{
plot(phi[[k]][,1], ylim=c(0,1), type="l", col=1, xlab = "MC-EM Iterations", ylab = sprintf("PIV %s", pivs[k]))
lines(phi[[k]][,2], col=2)
lines(phi[[k]][,3], col=3)
legend("topright", legend=c("agree", "missings A", "missings B"), col=c(1,2,3), lty=1:4, cex=0.8)
}
# Confusion matrix
FPR_list = c()
TPR_list = c()
for(t in seq(0, 1.1, by=0.1)){
TP_ = sum( (fit$Delta > t) & (Delta == 1) )
TN_ = sum( (fit$Delta < t) & (Delta == 0) )
FP_ = sum( (fit$Delta > t) & (Delta == 0) )
FN_ = sum( (fit$Delta < t) & (Delta == 1) )
FPR_list = append(FPR_list, FP_ / (FP_ + TN_))
TPR_list = append(TPR_list, TP_ / (TP_ + FN_))
}
TruePositive = (fit$Delta > 0.5) & (Delta == 1)
TrueNegative = (fit$Delta < 0.5) & (Delta == 0)
FalsePositive = (fit$Delta > 0.5) & (Delta == 0)
FalseNegative = (fit$Delta < 0.5) & (Delta == 1)
TP = sum( TruePositive )
TN = sum( TrueNegative )
FP = sum( FalsePositive )
FN = sum( FalseNegative )
FPR = FP / (FP + TN)
TPR = TP / (TP + FN)
par(mfrow=c(1,2))
plot(FPR_list, TPR_list, xlim=c(0,1), ylim=c(0,1))
title(sprintf("AUC: %s", round(auc(Delta, fit$Delta),2)))
lines(c(0,1), c(0,1))
plot(FPR_list, TPR_list, xlim=c(0,0.002), ylim=c(0,1))
title(c(sprintf("TP: %s, FP: %s, FN: %s", TP, FP, FN), sprintf("FPR: %s, TPR: %s", round(FPR,2), round(TPR,2)), "(for proba = .5)"))
lines(c(0,1), c(0,1))
# Agreements
linkedRecords = which(Delta==1, arr.ind=TRUE)
linkedRecordsFN = which(FalseNegative, arr.ind=TRUE)
linkedRecordsTP = which(TruePositive, arr.ind=TRUE)
for (k in 1:length(pivs))
{
print(sprintf("PIV %s", pivs[k]))
agreementD = sum(A[linkedRecords[,1], pivs[k]] == B[linkedRecords[,2], pivs[k]]) / nrow(linkedRecords)
agreementFN = sum(A[linkedRecordsFN[,1], pivs[k]] == B[linkedRecordsFN[,2], pivs[k]]) / nrow(linkedRecordsFN)
agreementTP = sum(A[linkedRecordsTP[,1], pivs[k]] == B[linkedRecordsTP[,2], pivs[k]]) / nrow(linkedRecordsTP)
missingsAD = sum(A[linkedRecords[,1], pivs[k]] == 0) / nrow(linkedRecords)
missingsBD = sum(B[linkedRecords[,2], pivs[k]] == 0) / nrow(linkedRecords)
missingsAFN = sum(A[linkedRecordsFN[,1], pivs[k]] == 0) / nrow(linkedRecordsFN)
missingsBFN = sum(B[linkedRecordsFN[,2], pivs[k]] == 0) / nrow(linkedRecordsFN)
missingsATP = sum(A[linkedRecordsTP[,1], pivs[k]] == 0) / nrow(linkedRecordsTP)
missingsBTP = sum(B[linkedRecordsTP[,2], pivs[k]] == 0) / nrow(linkedRecordsTP)
print(sprintf("FN agree at %s percent - FN missing in A: %s percent - FN missing in B: %s percent", round(agreementFN*100,2), round(missingsAFN*100,2), round(missingsBFN*100,2)))
print(sprintf("D agree at %s percent - D missing in A: %s percent - D missing in B: %s percent", round(agreementD*100,2), round(missingsAD*100,2), round(missingsBD*100,2)))
print(sprintf("TP agree at %s percent - TP missing in A: %s percent - TP missing in B: %s percent", round(agreementTP*100,2), round(missingsATP*100,2), round(missingsBTP*100,2)))
}