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

## 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

### 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

set.seed(123) # make reproducible
M = 10000 # Number of posterior samples

N = 20
# sample the IV
x = rnorm(N, 10, 5)
# regression model for DVs
y = 100 + 3*x + rnorm(N,0,10)

## Delete missing data (3 in each)

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 

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getSpookyNums = function(M){
  s = tempfile() # to ensure no caching
  my.url = paste0("http://richarddmorey.org/spooky.php?n=",M,"&",s) 
  as.numeric(readLines(my.url))
}


x = getSpookyNums(10)

richarddmorey

<?xml version="1.0" encoding="utf-8"?>
<distribution>
	<names>
		<shortname>Normal</shortname>
		<longname>Normal</longname>
		<alternate>Gaussian</alternate>
		<id>normal</id>
	</names>
	<type>continuous</type>
	<support>

richarddmorey

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

```{r setup, include=FALSE}

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devtools::source_gist("2bd1b7a409fad3d71fc2d60884c85e50", filename = "runJAGSDemo.R")

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y = 13
N = 35

a = 1
b = 1

theta = seq(0, 1, len = 100)
likelihood = theta ^ y * (1-theta)^(N-y) #dbinom(y, N, theta)
prior = theta ^ (a-1) * (1-theta)^(b-1) #dbeta(theta, a, b)
posterior1 = prior * likelihood