diamonaj

genout <- GenMatch(Tr=treat, X=X, BalanceMatrix=BalanceMat, estimand="ATT", exact = TRUE, M=1,
                   pop.size=16, max.generations=10, wait.generations=1)

#The outcome variable
Y=re78/1000

#
# Now that GenMatch() has found the optimal weights, let's estimate
# our causal effect of interest using those weights
#

diamonaj

genout <- GenMatch(Tr=treat, X=X, BalanceMatrix=BalanceMat)

#The outcome variable
Y=re78/1000

#
# Now that GenMatch() has found the optimal weights, let's estimate
# our causal effect of interest using those weights
#
mout <- Match(Y=Y, Tr=treat, X=X, Weight.matrix=genout)

diamonaj

sink("transcript.txt", split = TRUE)

cat(getwd(), "\n")

rm(list=ls())
library(foreign)

foo <- read.csv("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00087718-6996/dw-data.csv")

X = cbind(foo$age, foo$education, foo$black, foo$hispanic, foo$married, foo$nodegree, foo$re75, foo$re74)

diamonaj

# https://tinyurl.com/y9syodxg
rm(list=ls())
library(Matching
        )
library(foreign)

foo <- read.csv("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00087718-6996/dw-data.csv")

X = cbind(foo$age, foo$education, foo$black, foo$re75, foo$re74)

diamonaj

# If you are copy/pasting this to an R Notebook, select "Raw" from the GitHub page before you copy.

# Check the sensitivity of your Genetic Matching results, using "hlsens" and "psens" in the rbounds library.
# You must load the library first.
# You should load your mout2 object... e.g., load("mout2")

## Here is the working matching code, in case it's helpful:
foo <- read.csv(url("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00089202-1711/daughters.csv"))

which.two.or.more.girls <- which(foo$ngirls >= 2 &  foo$nboys == 0)

diamonaj

# If you are copy/pasting this to an R Notebook, select "Raw" from the GitHub page before you copy.

# Table 1 in Ebonya
# Indep. vars
# anygirls: Any female children
# ngirls: Number of female children
# totchi: Total number of children

# white: White=1, 0 otherwise
# female: Female=1, 0 otherwise

diamonaj

# If you are copy/pasting this to an R Notebook, select "Raw" from the GitHub page before you copy.

# Replication file of Section 5 in
# Iacus, King, Porro (2011), Multivariate Matching Methods 
# That Are Monotonic Imbalance Bounding, JASA, V 106, N. 493,
# p. 345-361

foo <- read.csv(url("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00089202-1711/daughters.csv"))

# Table 1 in Ebonya

diamonaj

######### RUN THE CODE BELOW IN R. R-STUDIO IS THE RECOMMENDED IDE. BOTH R AND R-STUDIO ARE FREE.
######### QUESTIONS SHOULD BE POSTED TO PIAZZA
######### THE ACTUAL ASSIGNMENT BEGINS ON LINE 71 (where it says "ASSIGNMENT 1...")


### Multilateral Development Institution Data
foo <- read.csv("https://tinyurl.com/yb4phxx8") # read in the data

# column names
names(foo)

diamonaj

PEACEKEEPING WORKOUT (based on  King, Gary;Zeng, Langche, 2007, 
                      "Replication data for: When Can History be Our Guide? 
                      The Pitfalls of Counterfactual Inference", 
                      https://hdl.handle.net/1902.1/DXRXCFAWPK, 
                      Harvard Dataverse, V4, 
                      UNF:3:DaYlT6QSX9r0D50ye+tXpA== [fileUNF] )

# CONSIDER USING THE JUPYTER NOTEBOOK WITH R-SERVER KERNEL (NEVER R-SAGE KERNEL)
foo_orig <- read.csv("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00086677-3767/peace.csv")

diamonaj

PEACEKEEPING WORKOUT (based on  King, Gary;Zeng, Langche, 2007, 
                      "Replication data for: When Can History be Our Guide?) 

# CONSIDER USING THE JUPYTER NOTEBOOK WITH R-SERVER KERNEL (NEVER R-SAGE KERNEL)

rm(list=ls()) # WARNING: THIS DELETES EVERYTHING IN YOUR R WORKSPACE
detach(foo) # probably unnecessary, but just in case you had it attached...
library(Matching)
foo <- read.csv("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00086677-3767/peace.csv")