# Helper functions for computation od Confidence intervals for the means
# in a within-subject design derived from http://wiki.stdout.org/rcookbook/Graphs/
summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL,
idvar=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {
# Ensure that the betweenvars and withinvars are factors
factorvars <- sapply(data[, c(betweenvars, withinvars), drop=FALSE], FUN=is.factor)
if (!all(factorvars)) {
nonfactorvars <- names(factorvars)[!factorvars]
message("Automatically converting the following non-factors to factors: ",
paste(nonfactorvars, collapse = ", "))
data[nonfactorvars] <- lapply(data[nonfactorvars], factor)
}
# Norm each subject's data
data <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop)
# This is the name of the new column
measureNormedVar <- paste(measurevar, "Normed", sep="")
# Replace the original data column with the normed one
data[,measurevar] <- data[,measureNormedVar]
# Collapse the normed data - now we can treat between and within vars the same
datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars), na.rm=na.rm,
conf.interval=conf.interval, .drop=.drop)
# Apply correction from Morey (2008) to the standard error and confidence interval
# Get the product of the number of conditions of within-S variables
nWithinGroups <- prod(sapply(datac[,withinvars, drop=FALSE], FUN=nlevels))
correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )
# Apply the correction factor
datac$sd <- datac$sd * correctionFactor
datac$se <- datac$se * correctionFactor
datac$ci <- datac$ci * correctionFactor
return(datac)
}
normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL,
na.rm=FALSE, .drop=TRUE) {
require(plyr)
# Measure var on left, idvar + between vars on right of formula.
data.subjMean <- ddply(data, c(idvar, betweenvars), .drop=.drop,
.fun = function(xx, col, na.rm) {
c(subjMean = mean(xx[,col], na.rm=na.rm))
},
measurevar,
na.rm
)
# Put the subject means with original data
data <- merge(data, data.subjMean)
# Get the normalized data in a new column
measureNormedVar <- paste(measurevar, "Normed", sep="")
data[,measureNormedVar] <- data[,measurevar] - data[,"subjMean"] +
mean(data[,measurevar], na.rm=na.rm)
# Remove this subject mean column
data$subjMean <- NULL
return(data)
}
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
require(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This is does the summary; it's not easy to understand...
datac <- ddply(data, groupvars, .drop=.drop,
.fun= function(xx, col, na.rm) {
c( N = length2(xx[,col], na.rm=na.rm),
mean = mean (xx[,col], na.rm=na.rm),
sd = sd (xx[,col], na.rm=na.rm)
)
},
measurevar,
na.rm
)
# Rename the "mean" column
datac <- rename(datac, c("mean"=measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
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