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set.seed(20181001)
### LOOCV 

# Load packages and data
library(Matching)
library(boot)
data(lalonde)

# Train your model on ALL the data -- Use glm instead of lm

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library(haven)
library(arm)
df <- read_dta("C:/Users/Vinic/Downloads/turnout.dta")
View(df)
df[1,1]
df[1,]

lm2 <- glm(turnout ~ ., data = df, family = binomial)
summary(lm2)

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library(boot)

#estimate the mean via bootstrapping
boot.mean <- function(data,index) return(mean(data[index]))

#calculate the CI via t-distribution
t.dist.ci <- function(samp) {
  df <- length(samp) - 1
  factors <- qt(c(0.025, 0.975), df = df)
  samp.mean <- mean(samp) 

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