richarddmorey

---
title: "Generated data demo"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
runtime: shiny
---

```{r setup, include=FALSE}

richarddmorey

### Utility functions

# Do a single t test simulation
# report the p value
ttest.sim = function(n, func, true.mean = 0, alpha = 0.05){
  x = func(n) - true.mean
  t.test(x)$p.value
}

# Do a sequence of M t tests, report significance 

richarddmorey

### Data

t = 2
N = 20
rscale = sqrt(2)/2


### Begin utility functions

posterior = Vectorize(function(delta, t, N1, N2 = NULL, rscale = sqrt(2)/2, log = FALSE){

richarddmorey

############
# Option 1: Get all solutions
# using brute force
############

## This function creates every possible distribution of responses for
## a likert scale with nlev responses. This is total brute force. There's
## probably a better way.
## Argument:
## v : initially, the total number of responses

richarddmorey

## For the singer dataset
library(lattice)
data(singer)

## Get data ready (recode to two factors)
singer$female = factor(with(singer, grepl("S",voice.part) | grepl("A",voice.part)))
singer$high = factor(with(singer, grepl("S",voice.part) | grepl("T",voice.part)))

## We will test the hypothesis that the main effect
## of "high voice" is such that high voiced singers are shorter

richarddmorey

d = read.csv("https://gist.githubusercontent.com/richarddmorey/f7c3ed9fe3f9f1fc0520f332b4a8efd7/raw/900d75f765a086dba65a316504c926da1c1e894a/golf.csv")

plot(d$score, d$size, xlab = "Course score", ylab = "Perceived size", axes = FALSE, ylim = c(0,9), xlim = c(60,140), pch = 19)
axis(2, at = 1:9, las = 1)
axis(1)
abline(lm(size~score, data = d))

cor(d$size, d$score, method = "spearman")
# edge of significance
cor.test(d$size, d$score, method = "spearman")

richarddmorey

x = scan()
0.78
0.71
0.69
0.71
0.73
0.68
0.69
0.64
0.64

richarddmorey

---
title: "Normal meta-analysis"
author: "Richard D. Morey"
date: "04/07/2017"
output: html_document
---


```{r}
# data here

richarddmorey

richarddmorey

## Explore the sampling distribution of eta^2, omega^2-hat, and epsilon^2
## Richard D. Morey
## Sept 30, 2017

## Settings

## Number of participants in group
N = 10
## Number of groups
J = 3