#' Error bar calculations
#'
#' @param mydata the data frame we will calculate error bars for
#' @param measurevar the dependent variable we are calculating error bars for
#' @param groupvars grouping variables for error bars
#' @param time time variable (ie. isi, interval)
#' @param type type variable (ie. stimulus category)
#' @param group group variable (ie. condition)
#' @return N, mean, standard deviation, standard error, 95% confidence interval
#' @import
#' plyr
#' @examples
#' errorBars(mydata="mydata", measurevar="measurevar", groupvars=c(time="time",type="type",group="group"))
#' @export
errorBars <- function(mydata="mydata", measurevar="measurevar", groupvars=c(time="time",type="type",group="group"), na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (yy, na.rm=FALSE) {
if (na.rm) sum(!is.na(yy))
else length(yy)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(mydata, groupvars, .drop=.drop,
.fun = function(xx, col) {
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)
# 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)}
#plotdata=errorBars(mydata)
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