christophergandrud

library(pacman)

p_load(MASS, tidyverse)

# set seed
set.seed(4321)

# set simulation paramters
n <- 100  # sample size

christophergandrud

library(pacman)

p_load(MASS, tidyverse)

# set seed
set.seed(8764)

# set simulation paramters
n <- 100  # sample size

christophergandrud

set.seed(19743)

library(tidyverse)
library(ggrepel)
library(forcats)

tau <- function(x) {
    x^2
}

christophergandrud

#' @import ggplot2
#' @export

beta_plot <- function(n = 10000, a = 1, b = 3) {
    # draw distributions
    sims <- rbeta(n = n, shape1 = a, shape2 = b)

    # convert to data frame for ggplot2 compatability
    sims <- data.frame(x = sims)

christophergandrud

---
title: "The zeligverse and beyond"
output: html_document
---

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

[for Zelig version 5.1.1.9000, [setdirect branch](https://github.com/IQSS/Zelig/tree/setxdirect)]

christophergandrud

#' Converts an R reStructuredText document (Rrst) to R Markdown (Rmd)
#'
#' @param input character string with the path of the file to convert
#' @param output optional character string with the path for the output file
#' @param out_dir optional character string with the output directory. Not used
#'   if \code{output} is supplied.
#' @param output_file logical whether to return the output to a file or not.
#' If \code{FALSE}, then \code{output} and \code{out_dir} are ignored.
#'
#' @importFrom readr read_file write_file

christophergandrud

parse.formula<-function( formula, model,data=NULL){
        if(class(formula)[[1]]=="multiple")
          return(formula)
        nrUserOpt<-nrUserReq<-nrUserFixed<-nrUserSubreq<-0
        userOpt<-userReq<-userFixed<-userSubreq<-list()
        
        fc <- paste("describe.", model, sep = "")
        if (!exists(fc))
          stop("describe.",model," does not exsist")
        modelReq<-do.call(fc,list())

christophergandrud

# Initialize Zelig5 least squares object
z5 <- zls$new()

# Estimate ls model
z5$zelig(Fertility ~ Education, data = swiss)

# Simulate quantities of interest
z5$sim()

# Plot quantities of interest

christophergandrud

# Estimate ls model
z4 <- zelig(Fertility ~ Education, data = swiss, model = 'ls')

# Set Eductation to 5 and 15
z4_set_low <- setx(z4, Education = 5)
z4_set_high <- setx(z4, Education = 15)

# Simulate results
z4_sim <- sim(z4, x = z4_set_low, x1 = z4_set_high)

christophergandrud

# Load the Zelig package
library(Zelig)

# Initialize Zelig5 least squares object
z5 <- zls$new()

# Estimate ls model
z5$zelig(Fertility ~ Education, data = swiss)

# Set Education to 5 and 15