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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 |
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--- | |
title: "Normal meta-analysis" | |
author: "Richard D. Morey" | |
date: "04/07/2017" | |
output: html_document | |
--- | |
```{r} | |
# data here |
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x = scan() | |
0.78 | |
0.71 | |
0.69 | |
0.71 | |
0.73 | |
0.68 | |
0.69 | |
0.64 | |
0.64 |
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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") |
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## 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 |
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############ | |
# 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 |
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### 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){ |
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### 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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--- | |
title: "Generated data demo" | |
output: | |
flexdashboard::flex_dashboard: | |
orientation: rows | |
vertical_layout: scroll | |
runtime: shiny | |
--- | |
```{r setup, include=FALSE} |
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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) |