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# robumeta calculations
library(grid)
library(robumeta)

data(hierdat)

HTJ <- robu(effectsize ~ males + binge,
            data = hierdat, modelweights = "HIER",
            studynum = studyid,
            var.eff.size = var, small = FALSE)

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require(Formula)
require(metafor)
require(sandwich)
require(zoo)
require(lmtest)

#-----------------------------------------------
# Functions for making sandwich standard errors
#-----------------------------------------------

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# robumeta calculations
library(grid)
library(robumeta)
data(corrdat)
rho <- 0.8

HTJ <- robu(effectsize ~ males + college + binge,
            data = corrdat, 
            modelweights = "CORR", rho = rho,
            studynum = studyid,

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library(tidyr)
library(dplyr)
library(stringr)
library(ggplot2)

#--------------------------------
# format the data for graphing
#--------------------------------

crash_dat <- read.csv("http://blogs.edb.utexas.edu/pusto/files/2015/08/Yearly_crash_data_Austin_and_Travis_County.csv")

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library(tidyr)
library(dplyr)
library(stringr)
library(ggplot2)

cities_select <- c("HOUSTON","SAN ANTONIO","DALLAS","AUSTIN","FORT WORTH","EL PASO")

#----------------------------------------
# get population estimates
#----------------------------------------

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#----------------------------------------------
# data-generating model 
#----------------------------------------------

two_group_data <- function(iterations, n, p, var_ratio, delta) {
  Group <- c(rep("C", n * p), rep("T", n * (1 - p)))
  Y_C <- matrix(rnorm(iterations * n * p, mean = 0, sd = 1), n * p, iterations)
  Y_T <- matrix(rnorm(iterations * n * (1 - p), mean = delta, sd = sqrt(var_ratio)), n * (1 - p), iterations)
  dat <- data.frame(Group, rbind(Y_C, Y_T))
  return(dat)

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#----------------------------------------------
# data-generating model 
#----------------------------------------------

two_group_data <- function(iterations, n, p, var_ratio, delta) {
  Group <- c(rep("C", n * p), rep("T", n * (1 - p)))
  Y_C <- matrix(rnorm(iterations * n * p, mean = 0, sd = 1), n * p, iterations)
  Y_T <- matrix(rnorm(iterations * n * (1 - p), mean = delta, sd = sqrt(var_ratio)), n * (1 - p), iterations)
  dat <- data.frame(Group, rbind(Y_C, Y_T))
  return(dat)

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# Demonstrate the problem with gls model

library(nlme)
data(Ovary)

gls_raw <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data = Ovary,
               correlation = corAR1(form = ~ 1 | Mare),
               weights = varPower())

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---
title: "Designing simulation studies in R"
author: "James E. Pustejovsky"
date: "September 28, 2016"
output:
  ioslides_presentation:
    css: custom.css
    widescreen: true
    transition: faster
---

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require(Formula)
require(metafor)
require(sandwich)
require(zoo)
require(lmtest)

#-----------------------------------------------
# Identify outer-most clustering variable
#-----------------------------------------------