updated package names & general tidying; introduced proportions stub

.Rbuildignore

+^.*\.Rproj$
+^\.Rproj\.user$

DESCRIPTION

+Package: packageTemplate
+Title: What the Package Does (One Line, Title Case)
+Version: 0.0.0.9000
+Authors@R: 
+    person(given = "First",
+           family = "Last",
+           role = c("aut", "cre"),
+           email = "first.last@example.com")
+Description: What the package does (one paragraph).
+License: What license it uses
+Encoding: UTF-8
+LazyData: true
+RoxygenNote: 6.1.0

NAMESPACE

+# Generated by roxygen2: do not edit by hand
+
+export(estimateEffectSizes)
+import(data.table)
+import(pwr)
+import(reshape2)

R/power.R

+#--- Sample power related functions ---#
+
+#' Estimate detectable effect sizes using statistical power analysis
+#'
+#' \code{estimateEffectSizes} calculates required sample sizes for a given set of p values and samples.
+#'
+#' 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.
+#'
+#' @param mean the estimated mean value to use
+#' @param sd the estimated stadnard deviation to use
+#' @param samples a list of sample sizes to iterate over
+#' @param power power value to use
+#'
+#' @author Ben Anderson, \email{b.anderson@@soton.ac.uk}
+#' @export
+#' @import data.table
+#' @import reshape2
+#' @family Power functions
+
+estimateEffectSizes <- function(mean,sd,samples,power){
+  # obtain effect sizes using supplied mean & sd
+  sigs <- c(0.01,0.05,0.1,0.2) # force these, can always remove later
+  nSigs <- length(sigs)
+  nSamps <- length(samples)
+  # initialise power results array
+  resultsArray <- array(numeric(nSamps*nSigs),
+                        dim=c(nSamps,nSigs)
+  )
+  # loop over significance values
+  for (p in 1:nSigs){
+    for (s in 1:nSamps){ # loop over the sample sizes
+      result <- power.t.test( # pwr.t.test?
+        n = samples[s],
+        delta = NULL,
+        sd = sd,
+        sig.level = sigs[p],
+        power = power,
+        alternative = c("one.sided")
+      )
+      resultsArray[s,p] <- result$delta/mean # report effect size against sample size
+    }
+  }
+  dt <- data.table::as.data.table(resultsArray) # convert to dt for tidying
+  dt <- dt[,
+           .(
+             sampleN = testSamples,
+             "p = 0.01" = 100*V1, # "Detectable % effect (p = 0.01)"
+             "p = 0.05" = 100*V2, # "Detectable % effect (p = 0.05)"
+             "p = 0.1" = 100*V3, # "Detectable % effect (p = 0.1)"
+             "p = 0.2" = 100*V4 # "Detectable % effect (p = 0.2)"
+           )
+           ]
+  
+  longDT <- data.table::as.data.table(reshape2::melt(dt, id=c("sampleN")))
+  longDT <- data.table::setnames(longDT, "value", "effectSize")
+  longDT <- data.table::setnames(longDT, "variable", "pValue")
+  return(longDT) # returned the tidied & long form dt
+}
+
+#' Estimate proportion margins of error using statistical power analysis
+#'
+#' \code{estimateProportions} calculates required sample sizes for a given set of p values and samples.
+#'
+#' 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.
+#'
+#' @param mean the estimated mean value to use
+#' @param sd the estimated stadnard deviation to use
+#' @param samples a list of sample sizes to iterate over
+#' @param power power value to use
+#'
+#' @author Ben Anderson, \email{b.anderson@@soton.ac.uk}
+#' @export
+#' @import data.table
+#' @import reshape2
+#' @import pwr
+#' @family Power functions
+
+estimateEffectSizes <- function(mean,sd,samples,power){
+  # obtain effect sizes using supplied mean & sd
+  sigs <- c(0.01,0.05,0.1,0.2) # force these, can always remove later
+  nSigs <- length(sigs)
+  nSamps <- length(samples)
+  # initialise power results array
+  resultsArray <- array(numeric(nSamps*nSigs),
+                        dim=c(nSamps,nSigs)
+  )
+  # loop over significance values
+  for (p in 1:nSigs){
+    for (s in 1:nSamps){ # loop over the sample sizes
+      # pwr.t.test?
+      result <- power.t.test(
+        n = samples[s],
+        delta = NULL,
+        sd = sd,
+        sig.level = sigs[p],
+        power = power,
+        alternative = c("one.sided")
+      )
+      resultsArray[s,p] <- result$delta/mean # report effect size against sample size
+    }
+  }
+  dt <- data.table::as.data.table(resultsArray) # convert to dt for tidying
+  dt <- dt[,
+           .(
+             sampleN = testSamples,
+             "p = 0.01" = 100*V1, # "Detectable % effect (p = 0.01)"
+             "p = 0.05" = 100*V2, # "Detectable % effect (p = 0.05)"
+             "p = 0.1" = 100*V3, # "Detectable % effect (p = 0.1)"
+             "p = 0.2" = 100*V4 # "Detectable % effect (p = 0.2)"
+           )
+           ]
+  
+  longDT <- data.table::as.data.table(reshape2::melt(dt, id=c("sampleN")))
+  longDT <- data.table::setnames(longDT, "value", "effectSize")
+  longDT <- data.table::setnames(longDT, "variable", "pValue")
+  return(longDT) # returned the tidied & long form dt
+}
+
+

Readme.md

 # Analysis for a short paper on statistical power & statistical significance
 
-And the various confusions that arise...
\ No newline at end of file
+And the various confusions that arise...
+
+Structured as an [R package](http://r-pkgs.had.co.nz/) for re-usability.
\ No newline at end of file

man/estimateEffectSizes.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/power.R
+\name{estimateEffectSizes}
+\alias{estimateEffectSizes}
+\title{Estimate detectable effect sizes using statistical power analysis}
+\usage{
+estimateEffectSizes(mean, sd, samples, power)
+
+estimateEffectSizes(mean, sd, samples, power)
+}
+\arguments{
+\item{mean}{the estimated mean value to use}
+
+\item{sd}{the estimated stadnard deviation to use}
+
+\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{samples}{a list of sample sizes to iterate over}
+
+\item{power}{power value to use}
+}
+\description{
+\code{estimateEffectSizes} calculates required sample sizes for a given set of p values and samples.
+
+\code{estimateProportions} calculates required sample sizes for a given set of p values and samples.
+}
+\details{
+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.
+
+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.
+}
+\author{
+Ben Anderson, \email{b.anderson@soton.ac.uk}
+
+Ben Anderson, \email{b.anderson@soton.ac.uk}
+}
+\concept{Power functions}

sizingDemandResponseTrialsNZ.Rmd → paper/sizingDemandResponseTrialsNZ.Rmd

@@ -47,9 +47,8 @@ rmdLibs <- c("data.table", # data munching
              "ggplot2", # for fancy graphs
              "readr", # writing to files
              "lubridate", # for today
-             "SAVEr", # power stats functions
-             "GREENGridData",
              "broom", # tidy test results
+             "dkUtils", # local utilities
              "knitr" # for kable
 )
 # load them
@@ -78,7 +77,7 @@ heatPumpData <- paste0(myParams$dPath, "Heat Pump_2015-04-01_2016-03-31_observat
 myParams$GGDataDOI <- "https://dx.doi.org/10.5255/UKDA-SN-853334"
 plotCaption <- paste0("Source: ", myParams$GGDataDOI)
 
-myParams$ccBY <- "includes/licenseCCBY.Rmd"
+myParams$license <- "includes/licenseCCBY.Rmd"
 myParams$support <- "includes/supportGeneric.Rmd"
 
 myParams$pubLoc <- "Southampton: University of Southampton"
@@ -101,7 +100,7 @@ cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2"
 
 ## License
 
-```{r ccby license, child=myParams$ccBY}
+```{r license, child=myParams$license}
 ```
  
 ## Citation
@@ -114,9 +113,9 @@ This work is (c) `r lubridate::year(today())` the authors.
 
 ## History
 
-Code history is generally tracked via the paper [repo](https://github.com/dataknut/powerSignificanceDesignAndDecisionMaking):
+Code & report history:
 
- * [Paper history](https://github.com/dataknut/powerSignificanceDesignAndDecisionMaking/commits/master)
+ * [Paper history](https://github.com/dataknut/weGotThePower/commits/master)
  
 ## Data:
 
@@ -137,6 +136,7 @@ This report contains the analysis for a paper of the same name. The text is stor
 
 # Sample design: statistical power
 
+## Means
 
 ```{r loadGgData, include =FALSE}
 dt <- data.table::as.data.table(readr::read_csv(heatPumpData, progress = FALSE))
@@ -144,7 +144,7 @@ dt <- data.table::as.data.table(readr::read_csv(heatPumpData, progress = FALSE))
 dt <- dt[, month := lubridate::month(r_dateTime)]
 dt <- dt[, year := lubridate::year(r_dateTime)]
 
-dt <- GREENGridData::addNZSeason(dt, r_dateTime)
+dt <- dkUtils::addNZSeason(dt, r_dateTime)
 ```
 
 
@@ -161,7 +161,8 @@ testSD <- mean(testDT[season == "Winter"]$meanW)
 testSamples <- seq(50,3000,50)
 testPower <- 0.8
 
-powerRes80DT <- SAVEr::estimateEffectSizes(testMean,testSD,testSamples,testPower) # auto-produces range of p values
+# use pac
+powerRes80DT <- estimateEffectSizes(testMean,testSD,testSamples,testPower) # auto-produces range of p values
 ```
 
 Figure \@ref(fig:ggHPSampleSizeFig80) shows the initial p = 0.05 plot.
@@ -286,6 +287,13 @@ knitr::kable(caption = "Full results table (part)",
 ```
 
 
+
+## Proportions
+
+Does not require a sample.
+
+
+
 # Testing for differences: effect sizes, confidence intervals and p values
 
 ## Getting it 'wrong'