1. This is a question about statistical methodology, not R. Hence
inappropriate here.
2. Replies should therefore be private.
3. Consult the literature.
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
Clifford Stoll
On Thu, Jun 19, 2014 at 6:23 PM, Rich Shepard <rshepard at
appl-ecosys.com> wrote:> I have several small biological count-based data sets with one or more
> rows having zero proportion. The other proportions in the row sum to 1.000
> (or 0.9999 in the sixth data row below because of rounding errors in the
> computer). An example is:
>
> sampdate filter gather graze predate shred
> 2000-07-18 0.0550 0.5596 0.0734 0.2294 0.0826
> 2003-07-08 0.0734 0.6147 0.0183 0.2294 0.0642
> 2005-07-13 0.1161 0.5714 0.0357 0.1696 0.1071
> 2006-06-28 0.1000 0.4667 0.1500 0.1333 0.1500
> 2010-09-14 0.0778 0.6111 0.0444 0.1889 0.0778
> 2011-07-13 0.0879 0.5714 0.0659 0.2747 0.0000
> 2012-07-11 0.1042 0.5313 0.0625 0.2396 0.0625
>
> My concern is that in most field-biological (ecological/environmental)
> data there can be two explanations for zero counts: the organism was not
> present on that date or it was present but not collected. There is no way
to
> determine which case holds true in each instance, but the ecological
> interpretations differ.
>
> The zCompositions package offers several methods of imputing a value to
> replace the zeros. As I'm completely new to compositional data analyses
> (CoDA) I would appreciate advice on how to select the most appropriate
> method for these data sets. The available methods are: Geometric Bayesian
> multiplicative, BM, (GBM, default); square root BM (SQ); Bayes-Laplace BM
> (BL); count zero multiplicative (CZM); user-specified hyper-parameters
> (user).
>
> These biological data seem to me to be different from geochemical or
> economic data I see in package data sets or the CoDA references I've
> acquired and read.
>
> Advice and suggestions (including references to application of CoDA to
> ecological/environmental data) will be appreciated.
>
> Rich
>
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