Module-6-Example-3.R

# setwd("SET THE Working Director to THE PATH TO THIS DIRECTORY")

# install.packages("aod")
# install.packages("pROC")

library(aod)
library(stats)
library(pROC)

data<-read.csv("Datasets/cevent.csv")

attach(data)
# print a small part of the data 
head(data)


m <- glm(data$event ~ data$chol, family = "binomial")


summary(m)

# ORs per 1 unit increase 
# same as calculation by hand (OR): exp(0.02119)

exp(m$coefficients[2])

exp(cbind(OR = coef(m), confint.default(m)))



# OR per 10 unit increase 
exp(m$coefficients[2]*10)
exp((m$coefficients[2]-qnorm(0.975)*summary(m)$coefficients[2,2])*10)
exp((m$coefficients[2]+qnorm(0.975)*summary(m)$coefficients[2,2])*10)

# The same cab be produced by using the following 
exp(10 * cbind(OR = coef(m), confint.default(m)))


# predict risk for each patient
risk <-predict(m, type=c("response"))

risk 


# predict risk for patient with chol of 190: exp(-3.12716+0.02119*190)/(1+exp(-3.12716+0.02119*190))
risk[41]

exp(m$coefficients[1] + m$coefficients[2]*190) / (1+exp(m$coefficients[1] + m$coefficients[2]*190))



# multiple logistic regression 
data$male <- ifelse(data$sex =="M", 1, 0)
m2<-glm(data$event ~ data$chol + data$male + data$age, family="binomial")
summary(m2)


# ORs per 1 unit increase 
exp(cbind(OR = coef(m2), confint.default(m2)))

# ROC curve 
# install.package("pROC")
library(pROC)

# using model with chol and sex and age 
data$prob <-predict(m2, type=c("response"))

# ROC Curve 
g <- roc(data$event ~ data$prob)

# Get the Area under the curve
# c-statistics 
g
print(g)

# Plot the ROC Curve. 
plot(g)

# Just print the results 
roc(data$event ~ data$prob)

# or plot the graphs as well
roc(data$event ~ data$prob, plot=TRUE)

# To get ride of the padding of graph, you can use the par function to set some enviromental variables. 
par(pty="s")

roc(data$event ~ data$prob, plot=TRUE)


# Another way would be to see the x axis as 1-Specificity
plot(1- g$specificities, g$sensitivities, type="l", xlab="1-Specifity", ylab="Sensivity", main="ROC Curve")
abline(a=0, b=1)
grid()


# If you want to have a normal x-axis from zero to one 
roc(data$event ~ data$prob, plot=TRUE, legacy.axes=TRUE)

# if you want to get values in precentages 
roc(data$event ~ data$prob, plot=TRUE, legacy.axes=TRUE, percent=TRUE)

# If you want to understand better specificities and sensitivities. 
# These are just false positive and true negatives. 
roc(data$event ~ data$prob, plot=TRUE, legacy.axes=TRUE, percent=TRUE, xlab="False Positive (%)", ylab="True Positive (%)")

# chaning the color 
roc(data$event ~ data$prob, plot=TRUE, legacy.axes=TRUE, percent=TRUE, xlab="False Positive (%)", ylab="True Positive (%)", col="blue", lwd=4)