Paul Have you considered doing your analyses in two stages: (a) presence/absence indicator where all values other than zero become '1' and you are effectively analysing the probability of presence (or absence) at your estimated or simulated points? (b) normal score transform or whatever on your actual "value if present" The combination of the results from (a) and (b) might give you a better handle than trying to include the zeroes as part of the same distribution. We have had some pretty good results using this approach on birds and bugs, so it might well work with fish! Isobel http://www.kriging.com/coursesPaul Walline <[EMAIL PROTECTED]> wrote: Ive been a lurker for a while, and have learned a lot from reading thediscussions, so thanks in advance for that.My question concerns the use of the normal score transform when makingrepeated conditional sequential Gaussian simulations using GSLIB. I believethe criticism that the backtransform would give biased results (as discussedin the Saito and Goovaerts 2000 paper in the discussion about Multi-GausinanKriging) does not apply to simulations because at each point to besimulated, a single normal score value is drawn at random from the cdfobtained by kriging. The averaging takes place in the original data space. Icame to this conclusion from trying to figure out how I could apply thecorrection described in the Saito and Goovaerts paper.But even if the above is true, I may still have a problem because of thehigh percentage of zeros in my data sets, which ranges from 4 to 22%. I(the GSLIB program
actually) rank these zero values randomly and I dontknow how to implement the suggestion (of Goovaerts, citing Verly 1986) ofranking them based on the average value in a search radius so that zerosnear high densities have higher ranks than those in low density areas. Formy purposes, I calculate the total abundance for each realization, and usethe frequency distribution of these totals to calculate empirical confidenceintervals, so Im mostly interested in the variability in these totalabundance realizations. How would the zeros affect this? Someone hassuggested that doing the ranking randomly would increase the nugget effectof the normal score variograms. However, I have 6 data sets and the oneswith the highest % of zeros are not the ones with the largest nuggets. Ifthe nugget has been artificially inflated because zeros are not correlatedafter nscore transform when in fact they are correlated in the raw dataspace, is it
reasonable to say that the variability of the simulated totalabundances would be overestimated (and thus conservative)?Cheers,Paul WallineNOAA Fisheries, Alaska Fisheries Science Center* By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm )* To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED]Signoff ai-geostats * By using the ai-geostats mailing list you agree to follow its rules
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