(This article was first published on

**[R] tricks**, and kindly contributed to R-bloggers)I spend some time looking for an algorithm to find local extrema in a vector (time series). The solution I used is to “walk” through the vector by step larger than 1, in order to retain only one value even when the values are very noisy (see the picture at the end of the post).

It goes like this :

findpeaks <- function(vec,bw=1,x.coo=c(1:length(vec))) { pos.x.max <- NULL pos.y.max <- NULL pos.x.min <- NULL pos.y.min <- NULL for(i in 1:(length(vec)-1)) { if((i+1+bw)>length(vec)){ sup.stop <- length(vec)}else{sup.stop <- i+1+bw } if((i-bw)<1){inf.stop <- 1}else{inf.stop <- i-bw} subset.sup <- vec[(i+1):sup.stop] subset.inf <- vec[inf.stop:(i-1)] is.max <- sum(subset.inf > vec[i]) == 0 is.nomin <- sum(subset.sup > vec[i]) == 0 no.max <- sum(subset.inf > vec[i]) == length(subset.inf) no.nomin <- sum(subset.sup > vec[i]) == length(subset.sup) if(is.max & is.nomin){ pos.x.max <- c(pos.x.max,x.coo[i]) pos.y.max <- c(pos.y.max,vec[i]) } if(no.max & no.nomin){ pos.x.min <- c(pos.x.min,x.coo[i]) pos.y.min <- c(pos.y.min,vec[i]) } } return(list(pos.x.max,pos.y.max,pos.x.min,pos.y.min)) }

To

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