Hello all, For advice on the use of the arcsin transform, I recommend the following paper:
http://www.esajournals.org/doi/abs/10.1890/10-0340.1 The title itself is worth poking the link. David Schneider Quoting Jordan Marshall <[email protected]>: > Brian > > I use arcsin(square root(proportion)) anytime I'm doing analysis of > percent data. The reason may not be justified for the type of simulation > your running, which I'm not familiar with. I use this transformation > since percent data is inherently not normally distributed. > Arcsin(sqrt(proportion)) does transform the data to near normal > distribution. > > Jordan > > -- > Jordan M. Marshall, PhD > Assistant Professor > Department of Biology > Indiana University-Purdue University Fort Wayne > 2101 E. Coliseum Blvd. > Fort Wayne, IN 46805 > > Office (260) 481-6038 > Mobile (865) 919-9811 > Fax (260) 481-6087 > > www.jordanmarshall.com > > >>> On 11/30/2011 at 12:00 AM, ECOLOG-L automatic digest system > <[email protected]> wrote: > > Date: Tue, 29 Nov 2011 14:33:19 -0500 > > From: =?ISO-8859-1?Q?Brian_Mitchell?= <[email protected]> > > Subject: Transformation of percent cover data for power analysis > > > > 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 donât 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 Iâve 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] > This electronic communication is governed by the terms and conditions at http://www.mun.ca/cc/policies/electronic_communications_disclaimer_2011.php
