richarddmorey’s gists

## stereograms.csv

        
        ID
        fuseTime
        condition
        logFuseTime

        
        1
        47.20001
        NV
        3.85439410445589

        
        2
        21.99998
        NV
        3.09104154426699

        
        3
        20.39999
        NV
        3.01553441065397

        
        4
        19.70001
        NV
        2.98061914335803

        
        5
        17.4
        NV
        2.85647020622048

        
        6
        14.7
        NV
        2.68784749378469

        
        7
        13.39999
        NV
        2.59525396068793

        
        8
        13
        NV
        2.56494935746154

        
        9
        12.3
        NV
        2.50959926237837

## meta-normal.Rmd

      
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              richarddmorey
            / meta-normal.Rmd
          
          
            Created Jul 4, 2017
          
          
            Bayesian normal meta-analysis with descriptives
          
        
      View meta-normal.Rmd
  
    
  title
  author
  date
  output
  
  
  Normal meta-analysis
  Richard D. Morey
  04/07/2017
  html_document
  
  
# data here


## cov.R
x = scan()
0.78
0.71
0.69
0.71
0.73
0.68
0.69
0.64
0.64

## _scatter.R
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")

## onetail_lmBF.R
## 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

## delta_t.R
### 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){

## missings_jags.R
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)

## t1err-sim.R
### 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

## getSpookyNums.R
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)

## normal.xml
<?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>