Commit c6b3ef6b authored by Ben Anderson's avatar Ben Anderson
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restructured in move to git.soton

parent deb163d2
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......@@ -10,4 +10,4 @@ Description: Code to support a paper on statistical power analysis.
License: What license it uses
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
RoxygenNote: 6.1.1
# Generated by roxygen2: do not edit by hand
# run this to knit the .Rmd to an html file
repoLoc <- dkUtils::findParentDirectory("weGotThePower")
# this will fail if you do not have access to the UoO GREENGrid Data archive (via HCS)
# it will also sometimes fail on the small sample section due to random processes. Just re-run it.
rmdFile <- paste0(repoLoc, "/code/weGotThePower.Rmd")
rmarkdown::render(input = rmdFile,
output_format = "html_document2",
output_file = paste0(repoLoc, "/output/weGotThePower.html")
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......@@ -468,12 +468,11 @@ linkedTestDT <- linkedTestDT[season == "Winter" & !]
# create small sample - be warned, this is a random process so you will get different results each time you run it
smallTestDT <- sample_frac(linkedTestDT, 2, replace = TRUE)
smallTestDT <-, 2, replace = TRUE))
t <- smallTestDT[, .("mean W" = mean(meanW),
"sd W" = sd(meanW),
"n households" = .N),
keyby = .(nPeople)]
"n households" = .N), keyby = .(nPeople)]
knitr::kable(t, caption = "Number of households and summary statistics per group (winter heat pump use)")
......@@ -574,7 +573,7 @@ Now:
```{r creatLargeN}
# fix.
# we just randomly re-sample the GREEN Grid data
largeTestDT <- sample_frac(linkedTestDT, 40, replace = TRUE)
largeTestDT <-, 40, replace = TRUE))
t <- largeTestDT[, .("mean W" = mean(meanW),
"sd W" = sd(meanW),
# We Got The Power: Analysis for a short paper on statistical power & statistical significance
And the various confusions that arise...
* Latest draft:
* [.Rmd]( code (mostly stats & notes, little text)
* [.html](weGotThePower.html) output (as above)
theme: jekyll-theme-slate
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......@@ -22,7 +22,7 @@ estimateMeanEffectSizes(mean, sd, samples, power)
Returns a data.table of effect sizes for a given sample size. Calculates these for p = 0.01, 0.05, 0.1 & 0.2. Pick out the ones you want.
Other Power functions: \code{\link{estimateProportionEffectSizes}}
Other Power functions: \code{\link{estimateProportionSampleSizes}}
Ben Anderson, \email{}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/power.R
\title{Estimate detectable proportion differences for given sample sizes using statistical power analysis}
estimateProportionEffectSizes(samples, power)
estimateProportionSampleSizes(p1, p2, samples, power)
\item{p1}{the estimated proportion in sample 1}
\item{samples}{a list of sample sizes to iterate over}
\item{power}{power value to use}
\item{mean}{the estimated mean value to use}
\item{sd}{the estimated stadnard deviation to use}
\item{sd}{the estimated proportion in sample 2}
\code{estimateProportionEffectSizes} calculates required sample sizes for a given set of p values and samples.
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# run this to knit the .Rmd to an html file in the docs/ directory for github pages
myParams$repoLoc <- dkUtils::findParentDirectory("weGotThePower")
rmdFile <- paste0(myParams$repoLoc, "/paper/weGotThePower.Rmd")
rmarkdown::render(input = rmdFile,
output_format = "html_document2",
output_file = paste0(myParams$repoLoc, "/docs/weGotThePower.html")
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