WIP/Other/FRG_SummarySE.R

In lbusett/frgtool: Analyze Post Fire Vegetation Regeneration from Time Series of Summertime MODIS data

# 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)
}