I have a question that I'm not sure has a right answer, but I would appreciate any and all opinions, especially if you know of any citations to back them up. In the past, when dealing with univariate data, I have always been a proponent of using Fisher's Protected LSD for pairwise comparisons because it is the most powerful procedure, and the accuracy of its experimentwise error rate is based on a sound argument that was subsequently proven to be true with simulations in Carmer and Swanson (1973 ). I included the full reference below. At the moment, I'm working with community structure data (multivariate nonparametric) at multiple sites and there is an need to determine which sites are different from which other sites. My colleague has used a Bonferroni correction for this type of question the past, but I tend to think that is most likely too conservative. I'm interested to know if any of you have dealt with a similar problem and/or if you know if anyone has done any work on comparing pariwise comparisons procedures for multivariate nonparametric data. I've done a preliminary literature search with no success, but am still looking. Thanks for your help. -Penelope Carmer, S. G. and M. R. Swanson (1973). "An evaluation of ten pairwise multiple comparison procedures by Monte Carlo methods." Journal of the American Statistical Association 68 (341): 66-74. ============================== Penelope S. Pooler Quantitative Ecologist National Park Service I&M Northeast Coastal and Barrier Network URI Coastal Institute in Kingston #1 Greenhouse Rd., Rm 205 Kingston, RI 02881 Penelope_Pooler at nps.gov Ph.: (401) 874-7060
pairwise comparisons in community structure analysis data
2 messages · Penelope_Pooler at nps.gov, Gavin Simpson
On Mon, 2009-08-10 at 16:43 -0400, Penelope_Pooler at nps.gov wrote:
I have a question that I'm not sure has a right answer, but I would appreciate any and all opinions, especially if you know of any citations to back them up. In the past, when dealing with univariate data, I have always been a proponent of using Fisher's Protected LSD for pairwise comparisons because it is the most powerful procedure, and the accuracy of its experimentwise error rate is based on a sound argument that was subsequently proven to be true with simulations in Carmer and Swanson (1973 ). I included the full reference below. At the moment, I'm working with community structure data (multivariate nonparametric) at multiple sites and there is an need to determine which sites are different from which other sites. My colleague has used a Bonferroni correction for this type of question the past, but I tend to think that is most likely too conservative. I'm interested to know if any of you have dealt with a similar problem and/or if you know if anyone has done any work on comparing pariwise comparisons procedures for multivariate nonparametric data.
You don't say how you are comparing sites, i.e. to what is the Bonferroni correction applied? The p-value from ...? You are also a bit vague about the data structures/layout. If your data represent a set of sites with multiple samples taken at each of these sites, then you might want to look at adonis() and betadisper(), both functions in the vegan package. adonis() partitions the dissimilarities between sites on the basis of certain factors - so it is like ANOVA but with multivariate responses using any dissimilarity and you can think of this analysis as looking at whether the distances between points within a group (factor, site etc) are greater than the between groups distances or distances to other groups. If adonis() shows a significant difference between sites, this difference may be due to differences in the spread or variance of the respective groups, just like in standard ANOVA. betadisper() can be used to test for this. Unfortunately, adonis doesn't do pairwise comparisons, but with the right amount of force could be changed to do so. betadisper does do pairwise comparisons and even has a TukeyHSD method for it (for the multiple comparisons), but a user has recently pointed out that both the parametric p-value and the permutation-derived p-value of the main betadisper analysis don't have the right Type I error rates - Ho is accepted too many times when given random groups. This seems to be a problem with the general method and not with the implementation in vegan, and is something that I (as the author of betadisper) and Jari Oksanen are currently looking into (slowly on my part, I'm afraid...). If the above and Rodney's reply don't address your real problem per se, perhaps you could elaborate on what tests you are using and wish to correct for multiple comparisons? All the best, G
I've done a preliminary literature search with no success, but am still looking. Thanks for your help. -Penelope Carmer, S. G. and M. R. Swanson (1973). "An evaluation of ten pairwise multiple comparison procedures by Monte Carlo methods." Journal of the American Statistical Association 68 (341): 66-74. ============================== Penelope S. Pooler Quantitative Ecologist National Park Service I&M Northeast Coastal and Barrier Network URI Coastal Institute in Kingston #1 Greenhouse Rd., Rm 205 Kingston, RI 02881 Penelope_Pooler at nps.gov Ph.: (401) 874-7060
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