Logistic Regression

logistic_regression.R

# Plot the sigmoid function
library(ggplot2)
qplot(-10:10, 1/(1 + exp(-(-10:10))), geom="line", xlab="z", ylab="sigmoid function")

# Download South African heart disease data
sa.heart <- read.table("http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/SAheart.data", sep=",",head=T,row.names=1)

# Pretty plot
pairs(sa.heart[1:9],pch=21,bg=c("red","green")[factor(sa.heart$chd)])

# Apply logistic regression to South African heart data from ESL
sa.heart.fit <- glm(chd ~ ., family = binomial, data=sa.heart)
summary(sa.heart.fit)

# Use stepwise logistic regression to reduce the dimensions
library(MASS)
sa.heart.step <- stepAIC(glm(chd ~ ., data=sa.heart))
summary(sa.heart.step)