I'm sure there must be various peak-finding algorithms out there. Not knowing of any, I have written one myself*, but I thought I'd ask to see what's out there. Basically, I have a 2-dimensional data set and I want to identify local peaks in the data, while ignoring "trivial" peaks. My naive algorithm first identifies every peak and valley (point of inflection change in the graph), then shaves off shallow peaks and valleys based on an arbitrary depth parameter, then returns whatever is left. This produces decent results, but, again, I'd like to know what other implementations are available. (* source available on request)
Peak finding algorithm
2 messages · gene, Roger Koenker
You might want to look at the ftnonpar package. You haven't quite specified whether you are thinking about estimating densities, or regression functions or some third option, or whether 2-dimensional means: functions R -> R or functions R^2 -> R, my recollection is that ftnonpar is (mostly?) about the R -> R case. url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820
On Dec 9, 2004, at 3:01 PM, Gene Cutler wrote:
I'm sure there must be various peak-finding algorithms out there. Not knowing of any, I have written one myself*, but I thought I'd ask to see what's out there. Basically, I have a 2-dimensional data set and I want to identify local peaks in the data, while ignoring "trivial" peaks. My naive algorithm first identifies every peak and valley (point of inflection change in the graph), then shaves off shallow peaks and valleys based on an arbitrary depth parameter, then returns whatever is left. This produces decent results, but, again, I'd like to know what other implementations are available. (* source available on request)
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