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**ac0901c23e4ecf3da3cb2c9ad93c2ce098d9b79d**authored by LixiangZhao98 on**22 February 2024, 16:36:31 UTC**, committed by LixiangZhao98 on**22 February 2024, 16:36:31 UTC****1 parent**47c54a8

confidenceIntervalMacros.R

```
################################################################
# Helpers for confidence intervals, mostly for bootstrapping.
# Computes all confidence intervals with the sensible defaults.
# 2014-2015 Pierre Dragicevic
#
# WARNING - This is an alpha version, meaning it's unfinished
#
################################################################
# For more details about bootstrapping.
# See http://www.mayin.org/ajayshah/KB/R/documents/boot.html for boot
# and (Kirby and Gerlanc, 2013) http://web.williams.edu/Psychology/Faculty/Kirby/bootes-kirby-gerlanc-in-press.pdf for bootES and for referencing the bootstrap method in your paper
library(boot)
library(PropCIs)
# If set to TRUE, bootstrap intervals remain the same across executions.
deterministic <- TRUE
# The number of bootstrap resamples. Typically recommended is >= 1,000.
# The higher the number, the more precise the interval but also the slower
# the computation.
replicates <- 10000
# The method for computing intervals. The adjusted bootstrap
# percentile (BCa) method is recommended by (Kirby and Gerlanc, 2013)
# and should work in most cases. For other methods type help(boot.ci).
# FIXME: changing this does not work. You have to modify some of the code
# below.
intervalMethod <- "bca"
# Number of significant digits used for text output. Using many digits
# is not recommended as it gives a misleading impression of precision.
significantDigits <- 2
#####################################################################
# Statistics
samplemedian <- function(x, d) {
return(median(x[d]))
}
samplemean <- function(x, d) {
return(mean(x[d]))
}
# Data transformations
# No transformation, yields arithmetic means.
identityTransform <- c(
function(x) {return (x)}, # the transformation
function(x) {return (x)}, # the inverse transformation
TRUE # TRUE if increasing, FALSE otherwise
)
# Log transformation, yields geometric means.
logTransform <- c(
function(x) {return (log(x))}, # the transformation
function(x) {return (exp(x))}, # the inverse transformation
TRUE # TRUE if increasing, FALSE otherwise
)
# Inverse transformation, yields harmonic means.
inverseTransform <- c(
function(x) {return (1/x)}, # the transformation
function(x) {return (1/x)}, # the inverse transformation
FALSE # TRUE if increasing, FALSE otherwise
)
# Returns the point estimate and confidence interval in an array of length 3
bootstrapCI <- function(statistic, datapoints) {
# Compute the point estimate
pointEstimate <- statistic(datapoints)
# Make the rest of the code deterministic
if (deterministic) set.seed(0)
# Generate bootstrap replicates
b <- boot(datapoints, statistic, R = replicates, parallel="multicore")
# Compute interval
ci <- boot.ci(b, type = intervalMethod)
# Return the point estimate and CI bounds
# You can print the ci object for more info and debug
lowerBound <- ci$bca[4]
upperBound <- ci$bca[5]
return(c(pointEstimate, lowerBound, upperBound))
}
# Returns the mean and its confidence interval in an array of length 3
bootstrapMeanCI <- function(datapoints) {
return(bootstrapCI(samplemean, datapoints))
}
# Returns the median and its confidence interval in an array of length 3
bootstrapMedianCI <- function(datapoints) {
return(bootstrapCI(samplemedian, datapoints))
}
exactMeanCI <- function(datapoints, transformation = identityTransform) {
datapoints <- transformation[[1]](datapoints)
pointEstimate <- mean(datapoints)
ttest <- t.test(datapoints)
lowerBound <- ttest[4]$conf.int[1]
upperBound <- ttest[4]$conf.int[2]
if (transformation[[3]])
return(transformation[[2]](c(pointEstimate, lowerBound, upperBound)))
else
return(transformation[[2]](c(pointEstimate, upperBound, lowerBound)))
}
percentCI <- function(datapoints, value) {
ncorrect <- length(datapoints[datapoints == value])
total <- length(datapoints)
percent <- 100 * ncorrect / total
proportionCI <- midPci(x = ncorrect, n = total, conf.level = 0.95)
percentCIlow <- 100 * proportionCI$conf.int[1]
percentCIhigh <- 100 * proportionCI$conf.int[2]
return (c(percent, percentCIlow, percentCIhigh))
}
# Returns the point estimate and confidence interval in a human-legible text format
formatCI <- function(CI, unit) {
point <- CI[1]
interval <- c(CI[2], CI[3])
# Format results in human-legible format using APA style
text <- paste(
prettyNum(point, digits=significantDigits),
unit,
", 95% CI [",
prettyNum(interval[1], digits=significantDigits), # APA recommends not reporting the unit again
", ",
prettyNum(interval[2], digits=significantDigits), # APA recommends not reporting the unit again
"]",
sep = "")
return(text)
}
# Returns the mean and its confidence interval in a human-legible text format
bootstrapMeanCI.text <- function(datapoints, unit) {
return(bootstrapCI.text(samplemean, datapoints, unit))
}
# Returns the median and its confidence interval in a human-legible text format
bootstrapMedianCI.text <- function(datapoints, unit) {
return(bootstrapCI.text(samplemedian, datapoints, unit))
}
```

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