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Modeling.Rmd
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Modeling.Rmd
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---
title: "Predicting the oxygen affinity of human haemoglobin"
author: Saw Simeon, Chuleeporn Phanus-umporn, Nuttapat Anuwongcharoen, Virapong Prachayasittikul,
Jarl E. S. Wikberg, Leif Bulow and Chanin Nantasenamat
date: "July 19, 2016"
output: pdf_document
---
```{r, warning=FALSE, message=FALSE, error=FALSE}
library(readxl)
data <- read_excel("data.xlsx", sheet = 1)
Affinity <- data$Affinity
set.seed(100)
data <- data[-1]
data <- data.frame(data)
column_names <- colnames(data)
label_names <- gsub("X", "p", column_names)
colnames(data) <- label_names
column_names <- colnames(data)
label_names2 <- gsub("_scl", "z", column_names)
colnames(data) <- label_names2
data <- cbind(Affinity, data)
```
## Function for creating Decision Tree
```{r, warning = FALSE, message = FALSE, error = FALSE}
dt_C50_train <- function(x, model) {
if (model == "Decreased") {
library(parallel)
library(doSNOW)
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
rm(data)
rm(data_Decrease)
rm(data_Increase)
rm(data_Normal)
index <- caret::createDataPartition(input$Affinity, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
rm(input)
rm(index)
model <- C50::C5.0(Affinity~., data = train)
actual <- train$Affinity
prediction <- predict(model, train, type = "class")
rm(model)
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[[1]], (result[[2]] + result[[3]]), (result[[4]] + result[[7]]),
(result[[5]] + result[[9]]))
}
} else if (model == "Increased") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- C50::C5.0(Affinity~., data = train)
actual <- train$Affinity
prediction <- predict(model, train, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[5], (result[2] + result[8]), (result[4] + result[6]),
result[1] + result[9])
}
} else if (model == "Normal") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- C50::C5.0(Affinity~., data = train)
actual <- train$Affinity
prediction <- predict(model, train, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[9], (result[3] + result[6]), (result[4] + result[6]),
(result[1] + result[5]))
}
return(ok)
stopCluster(cl)
}
}
mean_and_sd <- function(x) {
c(round(mean(x, na.rm = TRUE), digits = 2),
round(sd(x, na.rm = TRUE), digits = 2))
}
results_training_C50 <- function(x, mode) {
if (mode == "Decreased") {
yes <- dt_C50_train(x, model = "Decreased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if(mode == "Increased") {
yes <- dt_C50_train(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if (mode == "Normal") {
yes <- dt_C50_train(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
}
return(results_all)
}
dt_C50_cv <- function(x, model) {
if (model == "Decreased") {
library(parallel)
library(doSNOW)
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
k = 10
index <- sample(1:k, nrow(train), replace = TRUE)
folds <- 1:k
myRes <- data.frame()
for (j in 1:k) {
training <- subset(train, index %in% folds[-j])
testing <- subset(train, index %in% c(j))
model_train <- randomForest::randomForest(Affinity~., data = training)
prediction <- predict(model_train, testing)
actual <- testing$Affinity
ok <- data.frame(prediction, actual)
myRes <- rbind(myRes, ok)
}
prediction <- myRes$prediction
actual <- myRes$actual
result <- caret::confusionMatrix(prediction, actual)
rm(myRes)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[[1]], (result[[2]] + result[[3]]), (result[[4]] + result[[7]]),
(result[[5]] + result[[9]]))
}
} else if (model == "Increased") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
k = 10
index <- sample(1:k, nrow(train), replace = TRUE)
folds <- 1:k
myRes <- data.frame()
for (j in 1:k) {
training <- subset(train, index %in% folds[-j])
testing <- subset(train, index %in% c(j))
model_train <- randomForest::randomForest(Affinity~., data = training)
prediction <- predict(model_train, testing)
actual <- testing$Affinity
ok <- data.frame(prediction, actual)
myRes <- rbind(myRes, ok)
}
prediction <- myRes$prediction
actual <- myRes$actual
result <- caret::confusionMatrix(prediction, actual)
rm(myRes)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[5], (result[2] + result[8]), (result[4] + result[6]),
result[1] + result[9])
}
} else if (model == "Normal") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
k = 10
index <- sample(1:k, nrow(train), replace = TRUE)
folds <- 1:k
myRes <- data.frame()
for (j in 1:k) {
training <- subset(train, index %in% folds[-j])
testing <- subset(train, index %in% c(j))
model_train <- randomForest::randomForest(Affinity~., data = training)
prediction <- predict(model_train, testing)
actual <- testing$Affinity
ok <- data.frame(prediction, actual)
myRes <- rbind(myRes, ok)
}
prediction <- myRes$prediction
actual <- myRes$actual
result <- caret::confusionMatrix(prediction, actual)
rm(myRes)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[9], (result[3] + result[6]), (result[4] + result[6]),
(result[1] + result[5]))
}
return(ok)
stopCluster(cl)
}
}
results_cv_C50 <- function(x, mode) {
if (mode == "Decreased") {
yes <- dt_C50_cv(x, model = "Decreased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if(mode == "Increased") {
yes <- dt_C50_cv(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if (mode == "Normal") {
yes <- dt_C50_cv(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
}
return(results_all)
}
dt_C50_test <- function(x, model) {
if (model == "Decreased") {
library(parallel)
library(doSNOW)
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- C50::C5.0(Affinity~., data = train)
actual <- test$Affinity
prediction <- predict(model, test, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[[1]], (result[[2]] + result[[3]]), (result[[4]] + result[[7]]),
(result[[5]] + result[[9]]))
}
} else if (model == "Increased") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- C50::C5.0(Affinity~., data = train)
actual <- test$Affinity
prediction <- predict(model, test, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[5], (result[2] + result[8]), (result[4] + result[6]),
result[1] + result[9])
}
} else if (model == "Normal") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- C50::C5.0(Affinity~., data = train)
actual <- test$Affinity
prediction <- predict(model, test, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[9], (result[3] + result[6]), (result[4] + result[6]),
(result[1] + result[5]))
}
return(ok)
stopCluster(cl)
}
}
results_testing_C50 <- function(x, mode) {
if (mode == "Decreased") {
yes <- dt_C50_test(x, model = "Decreased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if(mode == "Increased") {
yes <- dt_C50_test(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if (mode == "Normal") {
yes <- dt_C50_test(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
}
return(results_all)
}
```
## Performance with Decision Tree
```{r, error=FALSE, message=FALSE, warning=FALSE}
dt_train <- function(x) {
result_train <- c(results_training_C50(x, mode = "Decreased"), results_training_C50(x, mode = "Normal"), results_training_C50(x, mode = "Increased"))
names(result_train) <- c("Training for Decreased (Mean and SD of ACC, SENS, SPEC, MCC)", "Training for Normal (Mean and SD of ACC, SENS, SPEC, MCC)", "Testing for Increased (Mean and SD of ACC, SENS, SPEC, MCC)")
return(result_train)
}
dt_cv <- function(x) {
result_cv <- c(results_cv_C50(x, mode = "Decreased"), results_cv_C50(x, mode = "Normal"), results_cv_C50(x, mode = "Increased"))
names(result_cv) <- c("CV for Decreased (Mean and SD of ACC, SENS, SPEC, MCC)", "CV for Normal (Mean and SD of ACC, SENS, SPEC, MCC)", "CV for Increased (Mean and SD of ACC, SENS, SPEC, MCC)")
return(result_cv)
}
dt_test <- function(x) {
result_test <- c(results_testing_C50(x, mode = "Decreased"), results_testing_C50(x, mode = "Normal"), results_testing_C50(x, mode = "Increased"))
names(result_test) <- c("Testing for Decreased (Mean and SD of ACC, SENS, SPEC, MCC)", "Testing for Normal (Mean and SD of ACC, SENS, SPEC, MCC)", "Testing for Increased (Mean and SD of ACC, SENS, SPEC, MCC)")
return(result_test)
}
decision_tree <- function(x) {
result <- c(dt_train(x), dt_cv(x), dt_test(x))
names(result) <- c("Training", "Cross Validation", "Testing")
return(result)
}
results <- decision_tree(data)
```
## Function for creating random forest
```{r, warning=FALSE, message=FALSE, error=FALSE}
rf_train <- function(x, model) {
if (model == "Decreased") {
library(parallel)
library(doSNOW)
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- randomForest::randomForest(Affinity~., data = train)
actual <- train$Affinity
prediction <- predict(model, train, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[[1]], (result[[2]] + result[[3]]), (result[[4]] + result[[7]]),
(result[[5]] + result[[9]]))
}
} else if (model == "Increased") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- randomForest::randomForest(Affinity~., data = train)
actual <- train$Affinity
prediction <- predict(model, train, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[5], (result[2] + result[8]), (result[4] + result[6]),
result[1] + result[9])
}
} else if (model == "Normal") {
cl <- makeCluster(detectCores())
registerDoSNOW(cl)
ok <- list(100)
ok <- foreach(i = 1:100) %dopar% {
data <- x
data_Decrease <- subset(data, Affinity == "Decreased")
data_Increase <- subset(data, Affinity == "Increased")
data_Normal <- subset(data, Affinity == "Normal")
data_Decrease <- dplyr::sample_n(data_Decrease, size = 59, replace = TRUE)
data_Increase <- dplyr::sample_n(data_Increase, size = 59, replace = TRUE)
data_Normal <- dplyr::sample_n(data_Normal, size = 59, replace = TRUE)
input <- rbind(data_Decrease, data_Increase, data_Normal)
index <- caret::createDataPartition(input$Affinity, times = 1, p = 0.8,
list = FALSE)
train <- input[index, ]
test <- input[-index, ]
model <- randomForest::randomForest(Affinity~., data = train)
actual <- train$Affinity
prediction <- predict(model, train, type = "class")
result <- caret::confusionMatrix(prediction, actual)
result <- result$table
result <- as.numeric(result)
ok[[i]] <- cbind(result[9], (result[3] + result[6]), (result[4] + result[6]),
(result[1] + result[5]))
}
return(ok)
stopCluster(cl)
}
}
mean_and_sd <- function(x) {
c(round(mean(x, na.rm = TRUE), digits = 2),
round(sd(x, na.rm = TRUE), digits = 2))
}
results_training_rf <- function(x, mode) {
if (mode == "Decreased") {
yes <- rf_train(x, model = "Decreased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if(mode == "Increased") {
yes <- rf_train(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")
} else if (mode == "Normal") {
yes <- rf_train(x, model = "Increased")
great <- data.frame(yes)
TP <- seq(from = 1, to = 400, by = 4)
FN <- seq(from = 2, to = 400, by = 4)
FP <- seq(from = 3, to = 400, by = 4)
TN <- seq(from = 4, to = 400, by = 4)
results <- mapply(c, great[TP], great[FN], great[FP], great[TN])
data <- data.frame(results)
rm(yes)
rm(great)
rm(results)
m = ncol(data)
ACC <- matrix(nrow = m, ncol = 1)
SENS <- matrix(nrow = m, ncol = 1)
SPEC <-matrix(nrow = m, ncol = 1)
MCC <- matrix(nrow = m, ncol = 1)
for(i in 1:m){
ACC[i,1] = (data[1,i]+data[4,i])/(data[1,i]+data[2,i]+data[3,i]+data[4,i])*100
SENS[i,1] = (data[4,i])/(data[3,i]+data[4,i])*100
SPEC[i,1] = (data[1,i]/(data[1,i]+data[2,i]))*100
MCC1 = (data[1,i]*data[4,i]) - (data[2,i]*data[3,i])
MCC2 = (data[4,i]+data[2,i])*(data[4,i]+data[3,i])
MCC3 = (data[1,i]+data[2,i])*(data[1,i]+data[3,i])
MCC4 = sqrt(MCC2)*sqrt(MCC3)
MCC[i,1] = MCC1/MCC4
}
results_ACC <- mean_and_sd(ACC)
rm(ACC)
results_SENS <- mean_and_sd(SENS)
rm(SENS)
results_SPEC <- mean_and_sd(SPEC)
rm(SPEC)
results_MCC <- mean_and_sd(MCC)
rm(MCC)
results_all <- (data.frame(c(results_ACC, results_SENS, results_SPEC, results_MCC)))
rownames(results_all) <- c("ACC_Mean", "ACC_SD", "Sens_Mean", "Sens_SD", "Spec_Mean", "Spec_SD",
"MCC_Mean", "MCC_SD")