Vegan-Adonis-NMDS-SIMPER
Gavin and Brandon, Yes, I am aware that betadisper() does not actually give you a test of differences between centroids, but the fact that it does calculate centroids is quite valuable for interpretation, in my opinion, especially when using non-euclidean distance matrices (e.g., Bray-Curtis) and also if you would prefer NOT to do additional pairwise tests between levels, but still would like to have some idea as to which pairwise differences between levels might be most responsible for the effect. When using bray-curtis distances, you can't get centroids by calculating averages of abundances among the observations of interest. If you just want to use a NMDS ordination with levels symbol-coded to make them distinct, that's fine. Sometimes folks calculate the average axis score per group or level of group and plot that. That's fine, too. The nice thing about obtaining centroids calculated using betadisper() is that they are based on a principal coordinates analysis that uses ALL the axes, not just the first two or three axes in the ordination. It is likely that if the first two or three axes of the NMDS explain most of the important variation, the average scores per level for those three axes will probably tell the same information as the centroids will. Even though it wasn't intended for this purpose, Sharon Graham and I, together, figured out that you could use the centroids calculated by betadisper() to analyze split-plot and repeated-measures designs using adonis. So, its value extends beyond what it was intended for. Steve J. Stephen Brewer Professor Department of Biology PO Box 1848 University of Mississippi University, Mississippi 38677-1848 Brewer web page - http://home.olemiss.edu/~jbrewer/ FAX - 662-915-5144 Phone - 662-915-1077
On 3/27/14 10:47 AM, "Gavin Simpson" <ucfagls at gmail.com> wrote:
Note that `betadisper()` only considers statistically dispersions about the group centroids. It might show the centroids and return their values, but it doesn't consider differences in those centroids. As far is `betadisper()` is concerned, the group centroids could all be made exactly equal and it wouldn't change the results as it is only the spread about the centroid that is used. HTH G On 27 March 2014 06:47, Brandon Gerig <bgerig at nd.edu> wrote:
Hi Steve, Yes, this is precisely what I am interested in doing. It seems like betadisper might be a good way to visualize differences/similarities in the dispersion and examine differences among centroids for the levels within a factor. Am I correct in thinking that if I conduct additional PERMANOVA tests on a reduced data set, I could be evaluating differences between the levels of a main effect? Could anyone provide a citation for a paper that uses a similar procedure? On Wed, Mar 26, 2014 at 3:21 PM, Steve Brewer <jbrewer at olemiss.edu> wrote:
Brandon, Are you asking if you can use betadisper as a substitute for post-anova pairwise comparisons among levels? After using betadisper to obtain dispersions, I believe you can plot the centroids for each level. In addition to telling you if the dispersions differ among levels, you could see how the centroids differ from one another. Is this what you want to know? If so, realize that it won't give you pairwise significance tests for differences between levels. For that, you might want to do additional permanovas on reduced datasets containing only the two levels you want to compare. You could then adjust the p-values for multiple tests after the fact. Hope this helps, Steve J. Stephen Brewer Professor Department of Biology PO Box 1848 University of Mississippi University, Mississippi 38677-1848 Brewer web page - http://home.olemiss.edu/~jbrewer/ FAX - 662-915-5144 Phone - 662-915-1077 On 3/26/14 10:57 AM, "Brandon Gerig" <bgerig at nd.edu> wrote:
Thanks for the words of caution on simper. Am I completely off base in thinking that betadiver function
(analgous to
Levene's test) could be used to examine variation between levels
within
main effects? Cheers On Mon, Mar 24, 2014 at 5:08 PM, Brandon Gerig <bgerig at nd.edu> wrote:
I am assessing the level of similarity between PCB congener
profiles in
spawning salmon and resident stream in stream reaches with and
without
salmon to determine if salmon are a significant vector for PCBs in tributary foodwebs of the Great Lakes. My data set is arranged in a matrix where the columns represent the congener of interest and the rows represent either a salmon
(migratory)
or resident fish (non migratory) from different sites. You can think
of
this in a manner analogous to columns representing species composition
and
rows representing site. Currently, I am using the function Adonis to test for dissimilarity between fish species, stream reaches (with and without salmon) and
lake
basin (Superior, Huron, Michigan). The model statement is:
m1<adonis(congener~FISH*REACH*BASIN,data=pcbcov,method="bray",permutat ion s=999) The output indicates significant main effects of FISH, REACH, and
BASIN
and significant interactions between FISH and BASIN, and BASIN and REACH. Is it best to then interpret this output via an NMDS ordination
plot or
use something like the betadiver function to examine variances
between
main effect levels or both? Also, can anyone recommend a procedure to identify the congeners
that
contribute most to the dissimilarity between fish, reaches, and basins?. I was thinking the SIMPER procedure but am not yet sold. Any advice is appreciated! -- Brandon Gerig PhD Student Department of Biological Sciences University of Notre Dame
--
Brandon Gerig
PhD Student
Department of Biological Sciences
University of Notre Dame
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Brandon Gerig
PhD Student
Department of Biological Sciences
University of Notre Dame
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-- Gavin Simpson, PhD