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#' Running Means
#'
#' Extracts the running means by sliding a window comprised of \code{wsize} time points, and in each window, the mean for each variable is computed.
#' Each time the window is slid, the oldest time point is discarded and the latest time point is added.
#'
#' @param data \emph{N} x \emph{v} dataframe where \emph{N} is the no. of time points and \emph{v} the no. of variables
#' @param wsize Window size
#'
#' @return Running means time series
#' @importFrom roll roll_mean
#' @export
#'
#' @examples
#' phase1=cbind(rnorm(50,0,1),rnorm(50,0,1)) #phase1: Means=0
#' phase2=cbind(rnorm(50,1,1),rnorm(50,1,1)) #phase2: Means=1
#' X=rbind(phase1,phase2)
#' RS=runMean(data=X,wsize=25)
#' ts.plot(RS, gpars=list(xlab="Window", ylab="Means", col=1:2,lwd=2))
#'
runMean <- function(data, wsize = 25) {
data <- as.data.frame(data)
labs <- colnames(data)
data <- as.matrix(data) #the roll package needs a matrix
N <- nrow(data)
wstep <- 1 #set to 1 for the kcpRS package
windows <- floor(((N - wsize) / wstep) + 1)
RunMean_TS <- roll_mean(data, width = wsize) #uses an online algorithm to compute the running statistics
RunMean_TS <- RunMean_TS[seq(wsize, wsize + windows - 1, wstep), ]
RunMean_TS <- as.data.frame(RunMean_TS)
colnames(RunMean_TS) <- labs
return(RunMean_TS)
}
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