---
title: "Intro to sreg"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Intro to sreg}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
Here, we provide the empirical application example using the data from (Chong et al., 2016),
who studied the effect of iron deficiency anemia on school-age children's educational attainment and cognitive ability in Peru. 
The example replicates the empirical illustration from (Bugni et al., 2019). For replication purposes, the data is included in the package and can be accessed by running `data("AEJapp")`. 
```{r}
library(sreg)
library(haven)
library(dplyr)
```
We can upload the `AEJapp` dataset to the R session via `data()`:
```{r}
data(AEJapp)
data <- AEJapp
```
It is pretty straightforward to prepare the data to fit the package syntax using `dplyr`:
```{r}
Y <- data$gradesq34
D <- data$treatment
S <- data$class_level
data.clean <- data.frame(Y, D, S)
data.clean <- data.clean %>%
  mutate(D = ifelse(D == 3, 0, D))
Y <- data.clean$Y
D <- data.clean$D
S <- data.clean$S
head(data.clean)
```
We can take a look at the frequency table of `D` and `S`:
```{r}
table(D = data.clean$D, S = data.clean$S)
```
Now, it is straightforward to replicate the results from (Bugni et al, 2019) using `sreg()`:
```{r}
result <- sreg::sreg(Y = Y, S = S, D = D)
print(result)
```
Besides that, sreg allows adding linear adjustments (covariates) to the estimation procedure:
```{r}
pills <- data$pills_taken
age <- data$age_months
data.clean <- data.frame(Y, D, S, pills, age)
data.clean <- data.clean %>%
  mutate(D = ifelse(D == 3, 0, D))
Y <- data.clean$Y
D <- data.clean$D
S <- data.clean$S
X <- data.frame("pills" = data.clean$pills, "age" = data.clean$age)
result <- sreg::sreg(Y, S, D, G.id = NULL, X = X)
print(result)
```