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### This exercise requires installing a bunch of packages---
### Unfortunately, the precise sequence and rules for installing may vary
### depending upon your computer and configuration.

## ***Taken from Chapter 5 in Kosuke Imai's "Quantitative Social Science"
## Transcribed by Alexis Diamond, all errors my own...

##########################################################################

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install.packages("devtools")
library(devtools)
devtools::install_github("kosukeimai/qss-package", build_vignettes = TRUE)

library(qss)

install.packages("tm")
install.packages("SnowballC")

library(tm)

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#############
# Answer key to pre-class work for Lesson 1


#***Step 1:

#Load dataset 
#Note: This step may take several seconds to complete
# click the link for the data set and see that it's a .csv file
# so use "read.csv" -- also, don't name this data object "data"

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---
title: "Assignment 3"
output: pdf_document
date: '2022-11-30'
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
set.seed(1)
```

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## Code Cell 1 of 4

# Show median treatment effect estimation here
#install.packages("quantreg")
library(quantreg)
library(Matching)
data(lalonde)

rqfit_r50 <- rq(re78 ~ treat, tau = 0.5, data = lalonde)
summary(rqfit_r50)

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# From class on Monday, Nov 14

# Switching to the OBSERVATIONAL Lalonde data set

# do genetic matching using the observational lalonde data
# use a caliper = to 0.1 standard deviations for every x.
library(Matching)
lalonde <- read.csv("https://tinyurl.com/st9n3dl")

X = cbind(lalonde$age, lalonde$educ, lalonde$black, lalonde$hisp, lalonde$married, lalonde$nodegr, lalonde$u74, lalonde$u75, lalonde$re75, lalonde$re74)

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# This is my code I want to share with you! :)

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die_rolls <- 1:6
storage <- c()

for(i in 1:1000)
  {
  rolls_for_the_week <- sample(die_rolls, 7, replace = TRUE)
  storage[i] <- sum(rolls_for_the_week == 1) == 0 # if there's no 1, then TRUE
}

cat("\nthe probability of not rolling a 1 all week is estimated =", 

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conditional <- function(a,b) {
  # inputs 2 Boolean variables representing atomic sentences a, b
  # outputs the truth value of the sentence "a -> b"
  return(!a | b)
}

premise1 <- function(a,b,c) {return(conditional(!a, !b))}

premise2 <- function(a,b,c) {return(a | !c)}

diamonaj

######################################################################
# Parts 1 and 2 (below) tell a story: 
# If you fit your model using the "data = ___" approach, and you don't
# use $ to define your variables (i.e., you don't type "x = training$x")
# then you don't get into trouble when you use predict() and supply
# a "newdata = ____ " object that has the same structure as your orig data.

# However, if you fit your model using the $ approach, then you seem to be
# OK using "predict()" and "newdata = training$x" AS LONG AS IT'S LOESS.
# But if it's linear regression, you are out of luck.