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model 1.R
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library(dplyr)
library(tidyr)
library(tidymodels)
library(runjags)
set.seed(123)
ratio <- 0.8
# data --------------------------------------------------------------------
predictors <- c("DEROG", "DELINQ", "CLAGE", "NINQ", "DEBTINC")
target <- "BAD"
dataset1 <- read.csv("Non-imputed/nonImputedHMEQ.csv") %>%
select(all_of(c(target, predictors))) %>%
drop_na()
dataset2 <- read.csv("Imputed/imputedHMEQ.csv") %>%
select(all_of(c(target, predictors))) %>%
drop_na()
# Onto dataset 1 ----------------------------------------------------
# train-test split
data_split <- initial_split(dataset1, prop = ratio, strata = response)
train_data <- training(data_split)
test_data <- testing(data_split)
Xtrain <- train_data %>% select(-c(response)) %>% as.matrix()
XTrainSD <- apply(Xtrain, 2, sd)
XTrainMean <- apply(Xtrain, 2, mean)
scaledXTrain <- scale(Xtrain, center = XTrainMean, scale = XTrainSD)
scaledXTest <- test_data %>% select(-c(response)) %>% as.matrix() %>% scale(center = XTrainMean, scale = XTrainSD)
yTrain <- train_data %>% select(response) %>% as.matrix() %>% as.numeric()
# Frequentist Approach for Inits
frequentist_model <- glm(BAD ~ ., data = train_data, family = binomial(link = "logit"))
initsList <- list(
zbeta0=coef(frequentist_model)["(Intercept)"],
zbeta=coef(frequentist_model)[-1]
)
# datalist
dataList <- list(
y = yTrain,
X = scaledXTrain,
xPred = scaledXTest,
Ntotal = nrow(scaledXTrain),
Nx = ncol(scaledXTrain),
Npred = nrow(scaledXTest),
xsd = XTrainSD,
xm = XTrainMean
)
# model
monitorList = c( "zbeta0" , "zbeta" , "guess", "beta0", "beta", "train", "pred" )
modelString = "
model {
# Likelihood
for ( i in 1:Ntotal ) {
y[i] ~ dbern( theta[i] )
theta[i] <- ( guess*(1/2) + (1.0-guess)*ilogit(zbeta0+sum(zbeta[1:Nx]*X[i,1:Nx])) )
}
# Priors
zbeta0 ~ dnorm( 0 , 1.0E-6 ) #Intercept
zbeta[1] ~ dnorm( 0 , 1.0E-6) #DEROG
zbeta[2] ~ dnorm(0.375, 1 / (0.10^2)) #CLAGE
zbeta[3] ~ dnorm( 0 , 1.0E-6 ) #DELINQ
zbeta[4] ~ dnorm( 0 , 1.0E-6 ) #NINQ
zbeta[5] ~ dnorm(0.375, 1 / (0.10^2)) #DEBTINC
guess ~ dbeta(1,19)
# Training Accuracy
for (i in 1:Ntotal){
train[i] <- guess*(1/2) + (1.0-guess)*ilogit(zbeta0 + sum(zbeta[1:Nx] * X[i,1:Nx]))
}
# Predictions
for (k in 1:Npred){
pred[k] <- guess*(1/2) + (1.0-guess) *ilogit(zbeta0 + sum(zbeta[1:Nx] * xPred[k,1:Nx]))
}
# Scale back for Inference
beta[1:Nx] <- zbeta[1:Nx] / xsd[1:Nx]
beta0 <- zbeta0 - sum( zbeta[1:Nx] * xm[1:Nx] / xsd[1:Nx] )
}
"
writeLines( modelString , con="TEMPmodel.txt" )
# Tuning
adaptSteps <- 100
burnInSteps <- 5000
numSavedSteps <- 1000
thinSteps <- 100
nChains <- 4
nIter = ceiling( ( numSavedSteps * thinSteps ) / nChains )
runjagsMethod <- "parallel"
# Run
runJagsOut <- run.jags( method=runjagsMethod ,
model="TEMPmodel.txt" ,
monitor=monitorList ,
data=dataList ,
inits=initsList ,
n.chains=nChains ,
adapt=adaptSteps ,
burnin=burnInSteps ,
sample=numSavedSteps ,
thin=thinSteps ,
summarise=FALSE ,
plots=FALSE )
# On dataset 2 ------------------------------------------------------------
# For dataset2
data_split <- initial_split(dataset2, prop = ratio, strata = response)
train_data <- training(data_split)
test_data <- testing(data_split)
Xtrain <- train_data %>% select(-c(response)) %>% as.matrix()
sdCLAGE <- Xtrain[,"CLAGE"] %>% sd()
sdDEBTINC <- Xtrain[,"DEBTINC"] %>% sd()
meanCLAGE <- Xtrain[,"CLAGE"] %>% mean()
meanDEBTINC <- Xtrain[,"DEBTINC"] %>% mean()
scaledXTrain <- Xtrain
scaledXTrain[,c("CLAGE", "DEBTINC")] <- Xtrain[,c("CLAGE", "DEBTINC")] %>% scale(center = c(meanCLAGE, meanDEBTINC), scale = c(sdCLAGE, sdDEBTINC))
scaledXTest <- test_data %>% select(-c(response)) %>% as.matrix()
scaledXTest[,c("CLAGE", "DEBTINC")] <- scaledXTest[,c("CLAGE", "DEBTINC")] %>% scale(center = c(meanCLAGE, meanDEBTINC), scale = c(sdCLAGE, sdDEBTINC))
yTrain <- train_data %>% select(response) %>% as.matrix() %>% as.numeric()
yTest <- test_data %>% select(response) %>% as.matrix() %>% as.numeric()
# Frequentist Approach for Inits
frequentist_model <- glm(BAD ~ ., data = train_data, family = binomial(link = "logit"))
coef(frequentist_model)
initsList <- list(
zbeta0=coef(frequentist_model)["(Intercept)"],
beta = coef(frequentist_model)[c("DEROG", "DELINQ", "NINQ")],
zbeta=coef(frequentist_model)[c("CLAGE", "DEBTINC")],
guess=0.3
)
# Datalist
dataList <- list(
y = yTrain,
X = scaledXTrain,
Xpred = scaledXTest,
Ntotal = nrow(scaledXTrain),
Npred = nrow(scaledXTest),
sd = c(sdCLAGE, sdDEBTINC),
mean = c(meanCLAGE, meanDEBTINC)
)
# Model
monitorList = c( "zbeta0" , "zbeta", "beta",
"guess", "beta0", "pred", "betaDEBTINC", "betaCLAGE" )
modelString = "
model {
# Likelihood
for ( i in 1:Ntotal ) {
y[i] ~ dbern( theta[i] )
theta[i] <- ( guess*(1/2) + (1.0-guess)*ilogit(zbeta0+sum(beta[1:3]*X[i,1:3])+sum(zbeta[1:2]*X[i,4:5])) )
}
# Priors
zbeta0 ~ dnorm( 0 , 1.0E-6 ) #Intercept
beta[1] ~ dnorm( 0 , 1.0E-6 ) #DEROG
beta[2] ~ dnorm( 0 , 1.0E-6 ) #DELINQ
beta[3] ~ dnorm( 0 , 1.0E-6 ) #NINQ
zbeta[1] ~ dnorm( 0.004201792 , 0.01 ) #CLAGE
zbeta[2] ~ dnorm( 20.3454 , 0.0002777778 ) #DEBTINC
guess ~ dbeta(1,19)
# Predictions
for (k in 1:Npred){
pred[k] <- ilogit(zbeta0+sum(beta[1:3]*Xpred[k,1:3])+sum(zbeta[1:2]*Xpred[k,4:5]))
}
# Scale back for Inference
betaCLAGE <- zbeta[1] * sd[1]
betaDEBTINC <- zbeta[2] * sd[2]
beta0 <- zbeta0 - sum( zbeta[1:2] * mean / sd)
}
"
# Tuning
adaptSteps <- 100
burnInSteps <- 5000
numSavedSteps <- 500
thinSteps <- 150
nChains <- 4
nIter = ceiling( ( numSavedSteps * thinSteps ) / nChains )
runjagsMethod <- "parallel"
# Run
runJagsOut <- run.jags( method=runjagsMethod ,
model="TEMPmodel.txt" ,
monitor=monitorList ,
data=dataList ,
inits=initsList ,
n.chains=nChains ,
adapt=adaptSteps ,
burnin=burnInSteps ,
sample=numSavedSteps ,
thin=thinSteps ,
summarise=FALSE ,
plots=FALSE )