#
# Data for Logistic Regression Model.

z1 <- rep(0.5,100)
z2 <- rep(1.0,100)
z3 <- rep(1.5,100)
z4 <- rep(2.0,100)
y <- c(z1-.5, z2-1, z3-.5, z4-1)

x1 <- 3 + rnorm(400)
x2 <- c(z1, (3 + rnorm(300)))
x3 <- c(z2, z1, (3 + rnorm(200)))
x4 <- c(z3, z2, z1, (3 + rnorm(100)))

summary(x1)
summary(x2)
summary(x3)
summary(x4)

GLM.1 <- glm(y ~ x1 + x2 + x3 + x4, family=binomial(logit))
summary(GLM.1)

logreg1 <- data.frame(y,x1,x2,x3,x4)
summary(logreg1)