[R] Magnitude of trend in time series

2007-01-13 Thread Barry Baker
Hello,

I am analyzing some climate time series data using the Mann Kendall package
and was wondering if there was a way to calculate the trend using Sen's
nonparametric estimator slope in R?

Thank you in advance,
Barry
_
Barry Baker, Ph.D.
Global Climate Change Initiative
The Nature Conservancy
2424 Spruce St., Suite 100
Boulder, CO 80302

Tel: (303)-541-0322
Fax: (303)-449-4328

http://nature.org/tncscience/scientists/misc/baker.html

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Re: [R] Magnitude of trend in time series

2007-01-13 Thread mike waters
 Rand Wilcox has produced a set of functions for S-Plus and (IIRC) R, which
includes Theil-Sen regression.

The following url gives a pdf to the workshop that they were designed to
accompany, along with instructions on how to source and use them.

http://psychology.usc.edu/rwilcox/workshop.pdf 

The following url is to his homepage which gives the above link plus those
to the function files themselves.

http://psychology.usc.edu/faculty_homepage.php?id=43


HTH

Regards

Mike

-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Barry Baker
Sent: 12 January 2007 23:48
To: r-help@stat.math.ethz.ch
Subject: [R] Magnitude of trend in time series

Hello,

I am analyzing some climate time series data using the Mann Kendall package
and was wondering if there was a way to calculate the trend using Sen's
nonparametric estimator slope in R?

Thank you in advance,
Barry
_
Barry Baker, Ph.D.
Global Climate Change Initiative
The Nature Conservancy
2424 Spruce St., Suite 100
Boulder, CO 80302

Tel: (303)-541-0322
Fax: (303)-449-4328

http://nature.org/tncscience/scientists/misc/baker.html

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and provide commented, minimal, self-contained, reproducible code.

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Re: [R] Magnitude of trend in time series

2007-01-13 Thread hadley wickham
 I am analyzing some climate time series data using the Mann Kendall package
 and was wondering if there was a way to calculate the trend using Sen's
 nonparametric estimator slope in R?

I think you can do it with the mblm package.  But I vaguely remember
that the results didn't look reasonable.  I've included some
simulation/testing code that I used below.

b1 - 5
x - 1:20 - mean(1:20)
y - 0 + b1 * x

n1 - function() y + rnorm(20, 0, 2)
ln1 - function() y + log(rnorm(20, 0, 0.7))
ln2 - function() y + log(rnorm(20, 0, 1.5))

fitlm - function(f=n1) {
  m - lm(n1() ~ x)
  ci - t(sapply(c(0.9, 0.95, 0.99), function(x) confint(m, level=x)[2,]))
  b1  ci[,1]  b1  ci[,2]
}
fitts - function(f=n1) {
  x - x # because mblm mucks up the environments
  y2 - n1()
  m - mblm(y2 ~ x, repeated=FALSE)
  ci - t(sapply(c(0.9, 0.95, 0.99), function(x) confint(m, level=x)[2,]))
  b1  ci[,1]  b1  ci[,2]
}

n1lm - replicate(1000, fitlm(n1))
n1ts - replicate(100, fitts(n1))
ln1lm - replicate(1000, fitlm(ln1))
ln1ts - replicate(100, fitts(ln1))
ln2lm - replicate(1000, fitlm(ln2))
ln2ts - replicate(100, fitts(ln2))


Hadley

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