Data Science Logistic Regression

Objective: Using Logistic Regression to handle a binary outcome.

 Given the prostate cancer dataset, in which biopsy results are given for 97 men: • 

You are to predict tumor spread in this dataset of 97 men who had undergone a biopsy. • 

The measures to be used for prediction are: age, lbph, lcp, gleason, and lpsa. 

This implies that binary dependent variable of lcavol will be the outcome variable. 

We start by loading the appropriate libraries in R: ROCR, ggplot2, and aod packages as follows: 

> install.packages("ROCR") 

> install.packages("ggplot2")

 > install.packages("aod") 

> library(ROCR) 

> library(ggplot2)

 > library(aod)

 Next, we load the csv file and check the statistical properties of the csv File as follow: 

> setwd("C:/RData") # your working directory > tumor <- read.csv("prostate.csv") # loading the file > str(tumor)

 # check the properties of the file . . . continue from here! 

Reference R Documentation (2016). Prostate cancer data. Retrieved from http://rafalab.github.io/pages/649/prostate.html