Ecolog:
What are the uses of cover data, from the most common applications to the
most sublime and rare?
WT
----- Original Message -----
From: "Liz Pryde" <[email protected]>
To: <[email protected]>
Sent: Wednesday, November 30, 2011 3:25 PM
Subject: Re: [ECOLOG-L] Transformation of percent cover data for power
analysis
Hi Brian,
I've been encountering this percent cover issue for a while. Unfortunately
my data was all based on Braun-Blanquet estimates so analysis has been
tricky. I converted to midpoint percent estimates (not strictly legitimate
but served the purpose) and then had the same problem you did.
I've been using the logit transform, log(y/1-y). And yes, you do have to
add a small error term. I found the following caused the fewest problems
(although it looks quite messy):
log[((Y*0.998)+0.001)/(1 -((Y*0.998)+0.001))]
I seem to recall that just adding 0.001 (or some other such small number)
gave greatly inflated values for points close to 1 (I could be wrong,
sorry, my 1 year old is clinging onto my leg while I write this - I may
have a few too many brackets there as well!!).
There is a great reference for this but I don't have all the details in
front of me (and now have banana all down my good pants!!):
Type into google:
"The arcsine is asinine" by Warton et al
It gives a great justification for using the logit transform and also
explains why a GLM (logit link) isn't so appropriate for % cover data (it's
quite tempting to use).
Hope this helps,
Liz
On Wed, Nov 30, 2011 at 6:33 AM, Brian Mitchell
<[email protected]>wrote:
Hello ecolog,
I am working on a power analysis simulation for long-term forest
monitoring
data, with the goal of documenting our power to detect trends over time.
The simulation is based on a repeated measures hierarchical model, where
future data is simulated based on the initial data set and a bootstrap of
pilot data differences between observation periods, multiplied by a range
of
effect sizes (50% decline to 50% increase).
My question is about the appropriate transformation to use for percent
cover
data in this simulation. I don’t want to use raw percentages because the
simulation will easily result in proportions less than zero or greater
than
one. Similarly, a log transform can easily result in back-transformed
proportions greater than one. Most other transforms I’ve looked at would
not prevent back-transformed data from exceeding one or the other
boundaries. The exception is the logistic transform, which would indeed
force all simulated data to be between zero and one when back-transformed.
However, the logistic transform gives values of negative infinity for a
percent cover of zero, and positive infinity for a percent cover of one.
I
was thinking that adding a tiny number to zeros and subtracting a tiny
number from ones (e.g., 0.00001) would solve the problem (roughly
equivalent
to a log of x+1 transform), but I have been unable to find reference to
anyone using this approach for percent cover data. Does anyone have any
thoughts about the validity of my proposed approach or of another approach
that would help solve my problem?
Thanks!
Brian Mitchell
NPS Northeast Temperate Network Program Manager
Adjunct Assistant Professor, University of Vermont
[email protected]
--
Liz Pryde
PhD Candidate (off-campus)
School of Earth and Environmental Sciences
James Cook University
Thornbury, Melbourne
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