[R] Population abundance, change point
Jonathan P Daily
jdaily at usgs.gov
Wed Nov 17 15:17:16 CET 2010
Indeed I have looked into various non-standard changepoint analysis
methods. I figured the OP was more interested in traditional methods since
you have to spend less time justifying your methodology. Wavelets are one
potential nontraditional method, as is Significant Zero Crossings (R
package SiZer), which fits arbitrary-degree smoothing splines over a range
of bandwidth parameters and looks for changes. With large communities of
abundance counts, another approach that is gaining popularity is the
community-level indicator taxa analysis (TITAN), though that is not useful
to the OP.
--------------------------------------
Jonathan P. Daily
Technician - USGS Leetown Science Center
11649 Leetown Road
Kearneysville WV, 25430
(304) 724-4480
"Is the room still a room when its empty? Does the room,
the thing itself have purpose? Or do we, what's the word... imbue it."
- Jubal Early, Firefly
Mike Marchywka <marchywka at hotmail.com> wrote on 11/17/2010 09:11:11 AM:
> [image removed]
>
> RE: [R] Population abundance, change point
>
> Mike Marchywka
>
> to:
>
> jdaily, carusonm
>
> 11/17/2010 09:11 AM
>
> Cc:
>
> r-help, r-help-bounces
>
>
>
>
>
> > To: carusonm at gmail.com
> > From: jdaily at usgs.gov
> > Date: Wed, 17 Nov 2010 08:45:01 -0500
> > CC: r-help at r-project.org; r-help-bounces at r-project.org
> > Subject: Re: [R] Population abundance, change point
> >
> > There are really no set ways to determine a changepoint, since a
> > changepoint depends completely on what you decide. Recursive
partitioning
> > will fit a best changepoint, but it will pretty much always fit one.
This
> If you are open to newer ideas,
> have you looked at wavelets at all? these come up on googel along with
R.
> Also with aonly a few points, even 20-30, you coldconsider exhasiutvely
> fitting slopes to all 2^n subsets and plowing throgh the histograms
> looking for anything that may be publishable or illuminating about your
data.
> Fitting to your own model or null hypotheses would make interesting
> contrasts of course, " populations remained the same after atrazine
spill
> or asteroid hit" etc.
>
>
> > function can be found in the package rpart:
> >
> > > fit <- rpart(count ~ year, control = list(maxdepth = 1))
> > > summary(fit)
> >
> > However this measure offers no level of confidence. This is where
packages
> > like strucchange and party come into use, as they provide measures of
> > confidence. Alternatively, you could look into regression-based
methods
> > where the changepoint is some parameter. Piecewise regression, for
> > instance, is as simple as fitting a spline of degree 1 and changepoint
X:
> >
> > > library(splines)
> > > fit <- lm(count ~ bs(year, knots = X, degree = 1))
> > > plot(year, count)
> > > lines(year, fitted(fit))
> >
> > Then you can fit a regression at each year and compare. Alternatively,
> > since count data is often noisy, you could easily substitute quantile
> > regression for linear regression to much of the same effect (assuming
> > whatever tau you decide, I used 0.8 but this is arbitrary):
> >
> > > library(splines)
> > > library(quantreg)
> > > fit <- rq(count ~ bs(year, knots = X, degree = 1), tau = 0.8)
> > > plot(year, count)
> > > lines(year, fitted(fit))
> > --------------------------------------
> > Jonathan P. Daily
> > Technician - USGS Leetown Science Center
> > 11649 Leetown Road
> > Kearneysville WV, 25430
> > (304) 724-4480
> > "Is the room still a room when its empty? Does the room,
> > the thing itself have purpose? Or do we, what's the word... imbue it."
> > - Jubal Early, Firefly
> >
> > r-help-bounces at r-project.org wrote on 11/16/2010 05:30:49 PM:
> >
> > > [image removed]
> > >
> > > [R] Population abundance, change point
> > >
> > > Nicholas M. Caruso
> > >
> > > to:
> > >
> > > r-help
> > >
> > > 11/16/2010 05:32 PM
> > >
> > > Sent by:
> > >
> > > r-help-bounces at r-project.org
> > >
> > > I am trying to understand my population abundance data and am
looking
> > into
> > > analyses of change point to try and determine, at approximately what
> > point
> > > do populations begin to change (either decline or increasing).
> > >
> > > Can anyone offer suggestions on ways to go about this?
> > >
> > > I have looked into bcp and strucchange packages but am not
completely
> > > convinced that these are appropriate for my data.
> > >
> > > Here is an example of what type of data I have
> > > Year of survey (continuous variable) 1960 - 2009 (there are gaps in
the
> > > surveys (e.g., there were no surveys from 2002-2004)
> > > Relative abundance of salamanders during the survey periods
> > >
> > >
> > > Thanks for your help, Nick
> > >
> > > --
> > > Nicholas M Caruso
> > > Graduate Student
> > > CLFS-Biology
> > > 4219 Biology-Psychology Building
> > > University of Maryland, College Park, MD 20742-5815
> > >
> > >
> > >
> > >
> > > ------------------------------------------------------------------
> > > I learned something of myself in the woods today,
> > > and walked out pleased for having made the acquaintance.
> > >
> > > [[alternative HTML version deleted]]
> > >
> > > ______________________________________________
> > > R-help at r-project.org mailing list
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> > > PLEASE do read the posting guide
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> >
> > ______________________________________________
> > R-help at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
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