The book: A Handbook of Statistical Analyses Using R
2008/10/29
rle and inverse.rle
rle return a list of run length of the vector, length for the run length, value for it's corresponding value
inverse.rle rollback the original vector
example:
> x <- c(1,0,0,1,1,1,1,1,0,0,0) > (tmp <- rle(x)) Run Length Encoding lengths: int [1:4] 1 2 5 3 values : num [1:4] 1 0 1 0
>inverse.rle(tmp)
[1] 1 0 0 1 1 1 1 1 0 0 0
Application: Finding peaks in vector
getPeaks <- function(x) { tmp <- rle(diff(x)>0) change.index <- cumsum(c(1, tmp$lengths)) ind <- change.index[which(tmp$values ==FALSE)] return(ind) }
> x<-c(rnorm(300),rnorm(700,5,2)) > d1<- density(x) > ind <- getPeaks(d1$y) > plot(d1,xlab="",ylab="") > ind [1] 149 279 > points(d1$x[ind], d1$y[ind], pch = 16, col="red")
inverse.rle rollback the original vector
example:
> x <- c(1,0,0,1,1,1,1,1,0,0,0) > (tmp <- rle(x)) Run Length Encoding lengths: int [1:4] 1 2 5 3 values : num [1:4] 1 0 1 0
>inverse.rle(tmp)
[1] 1 0 0 1 1 1 1 1 0 0 0
Application: Finding peaks in vector
getPeaks <- function(x) { tmp <- rle(diff(x)>0) change.index <- cumsum(c(1, tmp$lengths)) ind <- change.index[which(tmp$values ==FALSE)] return(ind) }
> x<-c(rnorm(300),rnorm(700,5,2)) > d1<- density(x) > ind <- getPeaks(d1$y) > plot(d1,xlab="",ylab="") > ind [1] 149 279 > points(d1$x[ind], d1$y[ind], pch = 16, col="red")
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