Nothing
doRBS <- structure(function(#Run RBS segmentation
### High-level function for multivariate recursive binary (RBS) segmentation
Y,
### A \code{n*p} signal to be segmented
K,
### The number of change points to find
stat=NULL,
### A vector containing the names or indices of the columns of \code{Y} to be segmented
...,
### Further arguments to be passed to 'segmentByRBS'
verbose=FALSE
### A \code{logical} value: should extra information be output ? Defaults to \code{FALSE}.
) {
##details<<This function is a wrapper around the lower-level
##segmentation function \code{\link{segmentByRBS}}. It can be run
##on p-dimensional, piecewise-constant data in order to defined a
##set of candidate change points. It is recommended to prune this
##list of candidates using dynamic programming
##(\code{\link{pruneByDP}}), combined with a selection of the best
##number of change points. The \code{\link{jointSeg}} function
##provides a convenient wrapper for performing segmentation, pruning
##and model selection.
##seealso<<\code{\link{PSSeg}}, \code{\link{pruneByDP}}
##references<<Gey, S., & Lebarbier, E. (2008). Using CART to Detect
##Multiple Change Points in the Mean for Large
##Sample. http://hal.archives-ouvertes.fr/hal-00327146/
## Argument 'Y'
if (is.null(dim(Y)) || is.data.frame(Y)) {
if (verbose) {
print("Coercing 'Y' to a matrix")
}
Y <- as.matrix(Y)
} else if (!is.matrix(Y)){
stop("Argument 'Y' should be a matrix, vector or data.frame")
}
## Argument 'stat'
if (!is.null(stat)) {
if (is.numeric(stat)) {
mm <- match(stat, 1:ncol(Y))
} else if (is.character(stat)) {
mm <- match(stat, colnames(Y))
}
if (sum(is.na(mm))) {
guilty <- paste("'", stat[which(is.na(mm))], "'", sep="", collapse=",")
stop("Undefined column(s) selected in 'Y':", guilty, ". Please check argument 'stat'")
} else {
Y <- Y[, mm, drop=FALSE]
}
}
res <- segmentByRBS(Y, K, ...)
res
###An object of the same structure as the output of \code{\link{segmentByRBS}}
}, ex=function(){
p <- 2
trueK <- 10
len <- 5e4
sim <- randomProfile(len, trueK, 1, p)
Y <- sim$profile
K <- 2*trueK
res <- doRBS(Y, K)
getTpFp(res$bkp, sim$bkp, tol=10, relax = -1) ## true and false positives
cols <- rep(2, K)
cols[1:trueK] <- 3
par(mfrow=c(p,1))
for (ii in 1:p) {
plot(Y[, ii], pch=19, cex=0.2)
abline(v=res$bkp[1:trueK], col= cols)
abline(v=sim$bkp, col=8, lty=2)
}
## NA:s in one dimension at a true breakpoint
jj <- sim$bkp[1]
Y[jj-seq(-10, 10), p] <- NA
res2 <- doRBS(Y, K)
getTpFp(res2$bkp, sim$bkp, tol=10, relax = -1) ## true and false positives
## NA:s in both dimensions at a true breakpoint
Y[jj-seq(-10, 10), ] <- NA
res3 <- doRBS(Y, K)
getTpFp(res3$bkp, sim$bkp, tol=10, relax = -1) ## true and false positives
})
############################################################################
## HISTORY:
## 2013-12-09
## o Renamed to 'doRBS'
## 2013-12-05
## o Now dropping row names of 'Y'.
## 2013-03-07
## o Add return parameter RSE to compute model selection on jointSeg
## 2013-01-23
## o BUG FIX: Empty candidate list would give an error. Now returning
## early when 'minRegionSize' is too large for 'K'.
## o Replace all jumps by bkp
## 2013-01-09
## o Replace all jumps by bkp
## 2012-12-31
## o Now using 'anotherBkp' instead of 'oneBkp' in order for missing
## values to be handled.
## 2012-12-30
## o Now properly dealing with the special case K=0.
## 2012-12-27
## o Renamed to segmentByRBS.
## o Some code and doc cleanups.
## 2012-12-23
## o SPEEDUP: removed redundant calls to 'getRSE'.
## 2012-12-07
## o BUG FIX: index shift in correspondence b/w breakpoint position and interval.
## 2012-12-05
## o Created.
############################################################################
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