Re: [R-sig-eco] vegan package: goodness.cca function? (citations needed)
On 25/11/2009 16:56, L Quinn lqu...@hotmail.com wrote: Hello, I posted this R-help and Ecolog-L before I knew about R-sig-ecology. I hope you will forgive the cross-posting, if you subscribe to those lists too. I deleted a number of species from a canonical correspondence analysis (CCA) model after using the function goodness.cca in vegan. In his explanation of the diagnostic tools for CCA (http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/goodness.cca.html), Jari Oksanen states that It is a common practise to use goodness statistics to remove species from ordination plots. However, I have not been able to find references on this common practice in the literature. I may be using inappropriate search terms?? However, if anybody can point me towards some cite-able references/rules of thumb on this, I would very much appreciate it. Hello, CanoDraw does so as default (or did when I last used several years ago), and since people commonly use CanoDraw, they commonly omit badly fitting species. This doesn't mean that they are aware of doing so. I don't know how well this is documented, but check CanoDraw manual and books about CANOCO. Cheers, Jari Oksanen And yes, I am also aware that Jari goes on to say that deleting species isn't always a good idea. Thank you! Lauren Quinn _ Bing brings you maps, menus, and reviews organized in one place. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] dissimilarity and species turnover
On 15/12/09 18:54 PM, Amanda Stanley ama...@appliedeco.org wrote: Robert, check out package ncf by Ottar Bjornstad http://cran.r-project.org/web/packages/ncf/index.html. The spline correlogram function might be what you need. From the documentation: The spline (cross-)correlogram differes from the spatial correlogram (and Mantel correlogram) in that it estimated spatial dependence as a continous functions of distance (rather than binning into distance classes). Howdy, Actually, the binning is not *the* problem (and basic Mantel test does not use binning at all: it is only for the correlograms). The problem is partitioning *distances* into *additive* components like is implicitly done when you have something like partial Mantel tests. What you can do for a starter is to go the October archive of R-sig-eco which has two threads on the very same issues (using two distance metrices in formula, Mantel test with skew-symmetric matrices?) The staring questions were not exactly identical to this question, but the discussion soon radiated to relevant issues. I'd recommend you check Sarah Goslee's comments at the minimum. If you want to go deeper here (and you should if you are serious), dig up the late 2008 issue of the Ecology with the Legendre mates vs. Tuomisto discussion -- somewhere around pages 3230 to 3256 of vol 89). That's for the starter. Cheers, Jari Oksanen I've been exploring using this approach for a similar problem. I'd be curious to know the opinions of this group if it appropriately deals with the issues surrounding Mantel tests. --Amanda Stanley *** Amanda G. Stanley, Ph.D. Project Director Institute for Applied Ecology P.O. Box 2855 Corvallis, OR 97339-2855 (Phone)541-753-3099 x133 (Fax)541-753-3098 ama...@appliedeco.org www.appliedeco.org From: Robert Ptacnik ptac...@icbm.de To: r-sig-ecology@r-project.org Date: Mon, 14 Dec 2009 17:26:39 +0100 Subject: [R-sig-eco] dissimilarity and species turnover Hi, mantel statistics has repeatedly been criticized. I wonder if there is an (approved) alternative for my problem: I aim to test whether one parameter (productivity, P) affects turnover (t) among ecological communities in time (T) or space (S). (ÄT and ÄS will be used as a co-variables). To avoid confusion - I do NOT aim to test whether P affects composition as such (which could be tested by an ordination method), but whether the degree of similarity among samples scales with P. My approach so far was to calculate a dissimilarity matrix from my community data, distance matrices for the relevant environmental data (ÄT, ÄS, ÄP) and a mean (P) matrix, giving the mean(P) for each pair of observations. The I performed mantel tests whether t correlates with mean(P), taking other variables into account (partialing out). However, mantel and especially partialing out are often criticized (e.g. see documentation in vegan). any views? thanks! Robert Robert Ptacnik, PhD ICBM, Univ. of Oldenburg Schleusenstrasse 1, DE-26382 Wilhelmshaven http://www.icbm.de/planktologie/en/ ptac...@icbm.de -- [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Fwd: Fwd: how to calculate axis variance in metaMDS, pakage vegan?
On 10/12/09 23:03 PM, gabriel singer gabriel.sin...@univie.ac.at wrote: A difference between two communities within a host could still exist and could make perfect sense, too, when you regard community as a random factor. Then community may introduce some extra variation (compared to the within-community variation), experimentally seen interesting and important, because the replication of communities makes sure you are not pseudoreplicating. I am not sure however, how to declare the correct df for the random factor in adonis in this case... anybody knows better than I? There is no way. Random factors can be only mimicked by stratified permutation, but they cannot be defined in adonis. It is only for fixed effect models. With latest R-Forge version of vegan you can use simulate.rda to mimic random models in RDA, but there is no way of having correct kind of permutations for mixed models in any other vegan function. Cheers, Jari Oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] capscale() for PCoA-CDA
On 3/12/09 23:54 PM, gabriel singer gabriel.sin...@univie.ac.at wrote: Hi everybody, Anybody has used capscale() in package vegan to compute a PCoA-CDA as suggested by Anderson and Willis 2003 (Ecology 84: 511 ff) using one or more factors as predictors? Then I wonder about: *) How to interpret interactions of factors? Why are interactions (specified as ~factor1*factor2 in the function call) shown as continuous predictors (using arrows) in the plot function? Wouldn´t centroids for all cells in the design be more appropriate? Aren´t factorial interactions in a CDA setting more or less meaningless? Internally capscale() uses constrasts of variables, and they are treated as continuous variables and shown as arrows in plots. However, if the constrasts correspond to simple factors, they are not drawn but their centroids are shown. For ordered factors you get both centroids and the arrows. The interactions of contrasts cannot be shown as simple class means and therefore they are drawn as arrows. The simple centroids are not appropriate, but you should have centroids of all combinations of class levels of interacting factors. If you think that factorial interactions in *** (what is CDA?) are meaningless, why do you want to use them? I wouldn't say they are meaningless, because that depends on your meaning. Often they are difficult to interpret, but that's another issue. *) How to get classification statistics? And how to efficiently run a leave 1 out classification analysis? I thought of manually writing code that checks for the closest centroid. Would it be appropriate to use Euclidean distance as a criterion for this since it happens in PCo space? Probably there are more efficient functions which I do not know of, yet,... for example a function that allows extraction of distances of all objects to all centroids? There is no such thing. Contributed code will be reviewed for inclusion into vegan. *) Is the application of capscale on a Euclidean distance matrix equivalent to a classical DFA aka CDA on the original data - or am I completely wrong with this idea? No, it isn't equal to DFA aka CDA. Perhaps... Depends on what are DFA and CDA. With Euclidean distances, capscale() is equivalent to redundancy analysis (RDA). Guessing that DFA aka CDA are discriminant analysis, RDA is not equal to them. The major difference is that RDA uses no information about scatter of points with respect to the class centroids, but it only uses class centroids. The RDA tries to maximize the distances among class centroids, but it doesn't try to maximize the separation of points of different classes. The methods are very different although the results may have some similarities. This is connected to the previous question: because RDA (that is in the heart of capscale()) does not try to optimize in classification, there is no classification statistic to be optimized. That should be estimated independently of the analysis and after the analysis, and there are no functions for the purpose in vegan. *) Given only one factor as a predictor, I guess using permutest() or anova() on an object resulting from capscale is completely equivalent to a direct application of adonis()? Correct? Have you tried this? After trying, you could tell us if this is true. I wouldn't expect this. The results may not be completely different, but internally the methods are pretty different, and when I tried with the same random number seed and hence same permutations, the results were not identical. Cheers, Jari ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] how to calculate axis variance in metaMDS, pakage vegan?
On 2/12/09 19:55 PM, Gian Maria Niccolò Benucci gian.benu...@gmail.com wrote: Okey, I understood... I have a matrix of 40 rows (samples) and 29 columns (species). In the ordination graph the data divide in two clades ( as i supposed they must to)... and that's my best solution for reduce the Stress... metaMDS(sqrtABCD, distance = bray, k = 23, trymax = 50, autotransform =F) - NMS.trial Gian, This looks very much like badly degenerate solution. You shouldn't use 23 axes in NMDS, in particular with 40 x 20 source data. In Euclidean space that data would give you rank of 20 or you could find at maximum 20 axes in metric scaling. In the Bray-Curtis space the situation is more complicated, but one random data set (Poisson random variates with lambda = 3.14) gave 25 positive and 14 negative eigenvalues. Probably the 23 dimensions you specify exhaust the real part of your space even in metric scaling, and probably (and obviously) fail miserably in nonmetric scaling. You shouldn't get stress of that magnitude with a decent model with data like that. It has never occurred to me that anybody would like to have NMDS with that high number of dimensions. Usually we want to use two, sometimes one or two more, but that's about the limit. Do the same and set k=2 to k=4 at maximum. If you want to have mapping of all of your real space (i.e., ignore the complex space), you can use metric scaling. The standard R choice is cmdscale(). The vegan alternatives are capscale() which also can do unconstrained metric scaling, returns information both on the real and imaginary components of your space, and has plot and other support functions. The low level alternative in vegan is wcmdscale() which also is used by capscale(), but does not have any support functions (lacks even print.wcmdscale!) NMDS is really intended for nonlinear mapping onto *low* number of dimensions. Cheers, Jari Oksanen NMS.trial Call: metaMDS(comm = sqrtABCD, distance = bray, k = 23, trymax = 100, autotransform = F) Nonmetric Multidimensional Scaling using isoMDS (MASS package) Data: sqrtABCD Distance: bray shortest Dimensions: 23 Stress: 0.2548688 Two convergent solutions found after 8 tries Scaling: centring, PC rotation, halfchange scaling Species: expanded scores based on ŒsqrtABCD‚ With more than 23 dimensions R gave me that result: metaMDS(sqrtABCD, distance = bray, k = 30, trymax = 50, Using step-across dissimilarities: Too long or NA distances: 230 out of 780 (29.5%) Stepping across 780 dissimilarities... Errore in isoMDS(dist, k = k, trace = isotrace) : initial configuration must be complete Inoltre: Warning messages: 1: In cmdscale(d, k) : some of the first 30 eigenvalues are 0 2: In sqrt(ev) : Si è prodotto un NaN ...Is normal I got better ordination (sepatation of different samples, that I know they're different) with few dimension also if the Stress is high? ... I supposed, that If we use as many dimensions as there are variables, then we can perfectly reproduce the observed distance matrix. Isn't it? But, of course, our goal is to reduce the observed complexity of nature, that is, to explain the distance matrix in terms of fewer underlying dimensions... So what is best at the end?? And also wich is the function for plotting the stress values versus the number of dimnsions and how to read the plot? I hope I was clear, thank you so much! Yours, G. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] how to calculate axis variance in metaMDS, pakage vegan?
On 2/12/09 19:55 PM, Gian Maria Niccolò Benucci gian.benu...@gmail.com wrote: ... I supposed, that If we use as many dimensions as there are variables, then we can perfectly reproduce the observed distance matrix. Isn't it? Gian, Not quite so. I think it would be useful to consult a good book, but here some explanation. The NMDS is not a simple reproduction method, but it is a non-linear regression problem. For n points and k dimensions we fit a nonlinear regression with n*k parameters fitted to n*(n-1)/2 observations. It doesn't require much intuition to see that this is not well defined for k approaching n, and then the non-linear regression fails. For details, the non-linear regression function is isoreg() in R, and the model fitting happens with optim() using method = BFGS (Broyden, Fletcher, Goldfarb Shanno). All this is not very obvious because it is done within a C function in the MASS package. The NMDS is nonlinear just in order to be able to produce a good mapping with low values of k: so stick with low values of k. If you want to have complete mapping of dissimilarities, you should use metric scaling. Then you typically ignore the latter axes. However, even here the situation is not as clear as you write. If you use Euclidean distances, then the number of variables give the number of dimensions of metric scaling. With Euclidean distances, the complete solution also exactly reproduces the observed distances. However, with non-Euclidean dissimilarities (like Bray-Curtis in your case) the situation is more complicated. Metric scaling and complete mapping is Euclidean, and if your dissimilarities are non-Euclidean, you have a problem (that you usually ignore). Firstly, the number of above zero eigenvalues and corresponding real eigenvalues is not directly defined by the number of variables. Secondly, you cannot reproduce the observed dissimilarities from real eigenvectors because that reproduction is Euclidean and your measure was non-Euclidean. For exact reproduction, you should subtract the distances in imaginary space (negative eigenvalues) from distances in the real space (positive eigenvalues). We actually do it exactly like this in the betadisper() function in vegan, and for this reason the wcmdscale() function of vegan also returns information on complex eigenvectors and negative eigenvalues. For your other post that came when I wrote this: stress 11.6 is really fine. I think that if you get stress down to 5% (0.05) or less, then there is something fishy in your data or in your model specification, like overfitting. Cheers, Jari Oksanen But, of course, our goal is to reduce the observed complexity of nature, that is, to explain the distance matrix in terms of fewer underlying dimensions... So what is best at the end?? And also wich is the function for plotting the stress values versus the number of dimnsions and how to read the plot? I hope I was clear, thank you so much! Yours, G. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Rotations for PCoA?
On 20/10/09 13:19 PM, Etienne Laliberté etiennelalibe...@gmail.com wrote: Date: Mon, 19 Oct 2009 13:44:57 +0200 From: Jan Hanspach jan.hansp...@ufz.de Subject: [R-sig-eco] Rotations for PCoA? To: r-sig-ecology@r-project.org Message-ID: 4adc5139.9000...@ufz.de Content-Type: text/plain; charset=ISO-8859-1; format=flowed Now, I want to know if it is possible to get rotations for my traits, like it is calculated for a PCA? So that I can plot my traits within the ordinations space (or give the values in a table). Thanks! Jan Pierre Legendre recently emailed me one function which does exactly that. I haven't found it on his website so I assume it's not official yet. In any case, I'm sure he wouldn't mind me sharing it and it can certainly give you some good ideas on how to represent the original variables in a PCoA biplot. Here's the code. Enjoy. Howdy Folks, Actually, it is trivial to have rotation scores for continuous variables: it is nothing but matrix multiplication. The problem here is that the original analysis used Gower distance for mixed metrics, and we should map the factor variables onto continuous variables in the same way as the Gower distance does. After that it is easy to find the rotation scores (but see below on metrics). While this can be done, this probably is not wanted since the interpretation of rotation for factors is non-intuitive, to put it mildly. Gower distance handles factors in a special way, and they are not the simple factor contrasts you get in standard R functions. How they are actually handled can be seen in the Fortran code for daisy or in Gower's paper. An extra complication is that Gower distance uses Manhattan metric. Therefore it is not possible to just transform data matrix into a form that would give the rotation scores in Euclidean PCoA. The standard way (that is not used in Gower distance) to transform factors into continuous data is to use mm - model.matrix(~ ., mydatawithfactors) Which gives you a model matrix where factors are broken into continuous contrast variables. You can get rotation, biplot scores or what ever you call for these contrasts, but that is only the beginning of the problems -- what are you going to do with those scores? Perhaps you can try vegan function envfit which finds the rotation scores for continuous variables and class centroids for factor variables. They are not the same as the strict rotation scores for factors in Gower metric (but should be the same as the Legendre scores for continuous variables), but may be more intuitive. Cheers, jari oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] using two distance metrices in formula
On 13/10/2009 17:51, Sarah Goslee sarah.gos...@gmail.com wrote: Jens, You can make your imported dissimilarities into a dist object quite easily. Say D is your imported dissimilarity data. It needs to be in lower-triangular format (assuming you imported it as a symmetric square matrix). D - as.matrix(D) D - D[col(D) row(D)] attr(D, Size) - N # number of samples (# of rows and cols) attr(D, Labels) - 1:N # or names of samples, of length N attr(D, Diag) - FALSE attr(D, Upper) - FALSE attr(D, method) - imported # name of index, or NULL class(D) - dist There is still easier way: D - as.dist(D), or D - as.dist(as.matrix(D)) which actually does exactly those things Sarah list above. On Tue, Oct 13, 2009 at 10:20 AM, Jens Oldeland oldel...@gmx.de wrote: Dear R-sig-ecology group, is there a way to use two self-made dissimilarity matrices for the left-hand side (LHS) and right hand side (RHS) in vegan functions such as capscale or adonis? I created those matrices in a GIS using 3D-information therefore I don´t want to simply use distances between 2D-coordinates. But as I found out, there is only the possibility to use a dist object for LHS. Can anyone suggest a solution or had similar problems? However, vegan functions such as capscale or adonis do not accept dissimilarities on both sides of the formula. The RHS always must be rectangular data. There are some functions that take dist objects on both sides, but they are less powerful, and in general, I wouldn't recommend their use except in special situations (although they are available in vegan). It is difficult to suggest a solution when we do not know what you want to solve. Why would you want to have dissimilarities on both sides of the formula? You can do that with, say, Mantel tests available in several packages in R, but what are the things you want to solve by using dissimilarities? Cheers, Jari Oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] using two distance metrices in formula
On 13/10/09 18:44 PM, Jens Oldeland oldel...@gmx.de wrote: Hi again, our distance matrices are 1) genetic distance (Jaccard) and 2) 3D-Euclidean Distance and the question we want to solve is if there is an effect called Isolation by Distance (IBD) in our data (genetic and real-distances of snails on the island of crete) or not. There was a debate on the topic if the mantel test or the partial mantel test (isn´t this similar to MRM?) in several papers mainly in evolution-journals: Raufaste, N. and F. Rousset. 2001. Are partial Mantel tests adequate? Evolution 55:17031705 Castellano, S. and E. Balletto. 2002. Is the partial Mantel test inadequate? Evolution 56:18711873. Geffen, E., Anderson, M.J., Wayne, R.K. (2004). Climate and habitat barriers to dispersal in the highly mobile grey wolf. Molecular Ecology, 13, 2481-2490 explain it nicely on page p.2483 (LHS) The problem arises due to the lack of independence of individual distances in a distance matrix. Although a simple Mantel test overcomes this issue by the use of permutations, a permutational approach does not necessarily solve problems introduced by several uncontrolled nuisance parameters in the case of more than one regressor (i.e. partial tests). Thus, we do not use a Mantel approach here, but rather use the distance-based multivariate approach of McArdle Anderson (2001). The important point is that, for dbRDA, the individual distances are not treated as a single univariate response variable, as in the Mantel test, but rather the individual sites are the units of observation for analysis, about which we have calculated distances using an entire set of genetic variables. The distance matrix is therefore treated as information regarding multivariate response.Taking this multivariate approach avoids the problems associated with the partial Mantel test. Jens, There has been a very similar discussion in the Ecology recently between my good friends, Hanna Tuomisto co vs. Pierre Legendre co. However, the point here and above exactly was that you cannot use dissimilarities on the RHS (lack of independence), but you must use rectangular data in dbRDA. If you use distances on the RHS you won't have dbRDA but you get Mantel family methods (like MRM in ecodist). The problem, of course, is how to map distances onto Euclidean space (= rectangular data) *and* still study the effects of the distances instead of the effects of *location*. I don't know any really good solution here, but all proposed solutions have their problems. Pierre Legendre, Daniel Borcard and Hanna Tuomisto have all tried to convince me of their point of view, and while all their conflicting arguments make sense, they are not yet an optimal solution. Cheers, Jari Oksanen so we thought it would be a good idea not to use mantel and friends since the problem of IBD seems to need a different approach here. best, Jens Sarah Goslee schrieb: That doesn't make much sense to me. You'd need an entirely different method than capscale. Perhaps what you're looking for is more like multiple regression on distance matrices (implemented in MRM in ecodist)? Lichstein, J. 2007. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology 188: 117-131. Legendre, P.; Lapointe, F. and Casgrain, P. 1994. Modeling brain evolution from behavior: A permutational regression approach. Evolution 48: 1487-1499. Sarah On Tue, Oct 13, 2009 at 11:13 AM, Jens Oldeland oldel...@gmx.de wrote: Dear Sarah dear Jari, many thanks for your explanations. However, it wasnt what I thought about, sorry I definitely have to be more specific about the problem. Okay I try be more precise: the problem was that for example capscale accepts capscale(dist.matrix.1 ~ N + P + K *Ag, data=varechem) but I need capscale(dist.matrix.1 ~ dist.matrix.2, data=dist.matrix.2) so the trick was not on how to create a distance matrix but how to use a second on in a formula. We are trying a similar analysis like the the distlm program by Marti Anderson does, however we had a problem with that and wanted to try the analysis in R. thanks already for all your comments ! best Jens ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] trouble with BiodiversityR:diversityresult function
Roman,
The user interface of vegan function 'specpool' changed in latest release,
and the BiodiversityR didn't follow along. The index name should now be
jack1 instead of Jack.1. This requires changes in a couple of places in
BiodiversityR, but I don't know its source code so well that I could easily
spot those changes (probably in BiodiversityRGUI, diversityresult,
diversityresult0). I have cc'ed this to Roeland Kindt, but I think the
Internet connection is not always so good so that it may take some time
before he answers. I can look if I can hack around this in vegan.
Cheers, Jari Oksanen
On 12/10/09 19:54 PM, Roman Luštrik roman.lust...@gmail.com wrote:
Hello list,
I have a felling this is a generic error but nonetheless, it has been
bugging me extensively. If anyone can chip in, I would most appreciate.
I'm trying to calculate expected species richness, following KindtCoe Tree
diversity analysis from my dataset but I get the following error:
divjack - diversityresult(PoCom, index=Jack.1)
Error in round(result, digits = digits) :
Non-numeric argument to mathematical function
PoCom is a community data.frame with rows as locations and columns as
species (as in test dataset called dune in the book mentioned earlier).
Here is a head(PoCom) and first few lines of str(PoCom):
head(PoCom)
Sal_idae Pil_mili Syl_idae Ner_sp. Ner_rava Sab_spin Neo_pseu
HM1 12 145 1311 28
HM2 1235 113 28 44 170
HM3 13 558 40027
HM4 141 23 56 91 7460
HM570 20 18 11 1140
HM6 13 140 70 398
str(PoCom)
'data.frame': 10 obs. of 83 variables:
$ Sal_idae : int 12 123 13 141 7 13 289 318 99 124
$ Pil_mili : int 14 5 55 23 0 14 15 23 86 138
$ Syl_idae : int 5 113 8 56 20 0 45 122 0 2
$ Ner_sp. : int 13 28 40 91 18 7 42 41 24 0
$ Ner_rava : int 1 44 0 74 11 0 16 34 0 35
$ Sab_spin : int 1 17 2 6 114 39 0 8 2 0
$ Neo_pseu : int 28 0 7 0 0 8 3 0 12 99
Debugging stops here:
Browse[1] n
debug: if (method != jackknife) {
result2 - round(result, digits = digits)
result2 - data.frame(result2)
colnames(result2) - index
}
Browse[1] n
debug: result2 - round(result, digits = digits)
Browse[1] n
Error in round(result, digits = digits) :
Non-numeric argument to mathematical function
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Re: [R-sig-eco] trouble with BiodiversityR:diversityresult function
On 12/10/09 19:54 PM, Roman Luštrik roman.lust...@gmail.com wrote: Hello list, I have a felling this is a generic error but nonetheless, it has been bugging me extensively. If anyone can chip in, I would most appreciate. I'm trying to calculate expected species richness, following KindtCoe Tree diversity analysis from my dataset but I get the following error: divjack - diversityresult(PoCom, index=Jack.1) Error in round(result, digits = digits) : Non-numeric argument to mathematical function Roman, I am not quite sure how BiodiversityR displays the results (I do not use it regularly). However, when I look at its documentation, it looks to me like it only would call specpool() of vegan and format the result. So if you go to the script windown in BiodiversityR and type specpool(PoCom) and click submit you should get the result. Cheers, Jari ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Mantel test with skew-symmetric matrices?
On 1/10/09 20:36 PM, Sarah Goslee sarah.gos...@gmail.com wrote:
I can only speak for the mantel() within ecodist, but I can tell you that it
will not take full matrices - the upper triangle will be dropped. You could
roll your own very easily, but it would be exceedingly slow, eg:
mat1 - # some square skew-symmetric matrix
mat2 - # some other square skew-symmetric matrix
mantelr - cor(as.vector(mat1), as.vector(mat2))
nperm - 100 # bigger for real problem, of course
permresults - numeric(nperm)
permresults[1] - mantelr
for(thisperm in 2:nperm) {
randsample - sample(1:nrow(mat1))
permresults[thisperm] - cor(as.vector(mat1[randsample,
randsample]), as.vector(mat2))
}
and then use permresults to estimate your test statistic of interest.
Sarah Steve,
This was the design I had on my mind. However, I was not sure how
skew-symmetry actually was defined, and therefore I didn't know if free
permutation of rows and columns (even when done correctly like above) will
retain the skew-symmetry. The free permutation would be OK for non-symmetric
matrices, but what about skew-symmetric? (Little thinking and pen paper
probably would give a quick answer, but I won't do that for a while).
Cheers, Jari
I haven't thought at all about any statistical issues raised by use of full
non-symmetric matrices - you're on your own there. It's certainly
*possible*, and
I don't see any immediate reason why it would be wrong, but haven't
pondered the issue.
I see Jari replied as well while I was writing - as for vegan, the
ecodist function would
need to be heavily modified to do this. If I'm persuaded that there's
enough interest,
I could add it to the list.
Sarah
On Thu, Oct 1, 2009 at 1:20 PM, Steve Arnott arno...@dnr.sc.gov wrote:
Hello All,
1) Is it wrong to use skew-symmetric matrices - i.e. should I just forget
about the skew data and use absolute values to make all my matrices
symmetric? The original Mantel paper (1967, Cancer Research, 2: 209-220) does
talk about skew-symmetric matrices, but the published applications I've come
across only seem to use symmetric matrices.
2) If it is ok to use skew-symmetric matrices, do the mantel() and
mantel.partial() functions in 'vegan' (or related functions in other
packages, such as 'ecodist') handle them correctly? I've found that it is
possible to process skew data and generate results using these functions, but
I'm uncertain from the documentation whether the results are meaningful (i.e.
is the coding designed to handle such cases appropriately?)
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Re: [R-sig-eco] Fixing parameters in GLM and Mixed Effects Models
On 24/09/09 22:34 PM, Hamish hwil...@ucsd.edu wrote:
I want to force a =1 and b= -1, yielding:
N ~ 1*Mass + (-1)*Temp + c*Precipitation + {d,e,f,g}*DIET
I think you want to have
N ~ offset(Mass) + offset(-Temp) + Precipitation + DIET
Most *lm functions know offset. However, you must be very careful especially
with links other than identity. Moreover, you must be sure that this really
is what you're looking for: the above expression would be equivalent to
offset(Mass-Temp) and is Mass-Temp really something you think you want to
fix.
and then see what the values of the other parameters (c-g) turn out to be,
as well as the model fit when using the forced parameters in conjunction
with the free parameters.
Cheers, Jari Oksanen
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Re: [R-sig-eco] wascores() for metaMDS?
Gabriel, On 19/08/09 12:40 PM, gabriel singer gabriel.sin...@univie.ac.at wrote: Hi sig-ecology! Here comes a probably stupid question... I am looking for smart ways to include information about underlying variables in MDS plots. In other words, after having computed an ordination with isoMDS or metaMDS from a community table, I would like to add something like species coefficients/loadings as vectors to the plot of sites. As no species coefficients exist in this case, the best I could come up with so far is simply vectors calculated from correlation coefficients of the individual species with the site scores (on two MDS axes). The function metaMDS allows to compute species scores using the function wascores() I have now pondered for 2 days how these scores are calculated and what their precise meaning would be. Would these species scores be appropriate to show as vectors in the MDS? Thanks for any answer... I think these were documented... Please point out the unclear parts of the documentation so that I can correct those. The wascores are Weighted Averages Scores and they are calculated like weighted averages, or similarly as species scores in correspondence analysis. This means that (with some scaling) they show the centroid (barycentre) of the species occurrence in the ordination graph. It is not appropriate to present these as arrows which indicate a linear increase to the direction of the arrow instead of the centre of abundance. Therefore the species scores can (and as default in metaMDS, will) be presented as points. If -- for any reason that is none of my business -- you want to get vectors of species, you can fit species as vectors. This happens with metaMDS or isoMDS like this: library(vegan) data(dune) m - metaMDS(dune) # or m - isoMDS(vegdist(dune)) vec - envfit(m, dune) plot(m, dis=site) # or with isoMDS: ordiplot(m) plot(vec) I promised that I won't comment on this, but still I must say that I cannot find a reason to do so. Please note that you can also use ordisurf to fit nonlinear species responses if you think that species are not points nor arrows. Vegan tutorial (from the Web) gives an example. Cheers, Jari Oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Neighbor sampling is random?
E. C. Pielou may be one of the first who wrote about this. Check her book 'Mathematical ecology' (J. Wiley, 1977). Best wishes, Jari Oksanen -Original Message- From: Paulo Inácio de Knegt López de Prado Sent: 04.07.2009, 17:35 To: r-sig-ecology@r-project.org Subject: [R-sig-eco] Neighbor sampling is random? Dear r-eco-list users, This is not an R-question, but a statistical one, but maybe somebody can help. I had read that to set random points over an area and picking the nearest plant is not random sample, but I could not recover this article. Is that correct? Could you provide some basic reference? Thanks a lot Paulo -- Paulo Inácio de Knegt López de Prado Depto. de Ecologia - Instituto de Biociências - USP Rua do Matão, travessa 14, nº 321 Cid. Universitária, São Paulo - SP CEP 05508-900 11-30917599 (sala) 11-30917600 (Secretaria) ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Vegan DCCA axis length
On 13/01/2009, at 15:30 PM, David Giordano Armanini wrote: Hi everyone, happy new year. I am new to the list so apologise for uncorrect use. I am using the Vegan package version 1.16.9 and I am encountering problems on obtaining the length of axis of a DCCA/pCCA, I have read previous threads in which Gavin Simpson and Jari Oksanen states that are not available routines in vegan to retrieve this information. I am interested in the length of the DCCA/pCCA axis as I would like to perform a similar analyses to the one Lancaster et al. 1996 performed to measure community persistence in acified stream (Freshwater Biology 1996 36, 179–201) and to the similar one performed as well by Woodward (Freshwater biology 2002 47 1419-1435). Thus, my question is how can I retrieve the standard deviations of species turnover in a DCCA/pCCA? I can not use a simple DCA as I need to use both conditions (Site) and Constrains (years), thanks in advance, I have been involved in this discussion earlier, but here some points: 1. You cannot perform DCCA in vegan, and this is not easily changed. As far as I know, there are no other packages to perform this. This is due to a deliberate design decisions in the Fortran code for decorana(), and a re-design is needed. 2. You can perform pCCA in vegan, and there you have the so-called Hill scaling that should approximate the sd scaling. See docs for scores.cca for the scaling. 3. I don't think that the determination of the length of the axes makes much sense. Best wishes, Jari Oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Intepreting a plot from a constrained NMDS
On 8 Dec 2008, at 2:53, stephen sefick wrote: A way that may work is use the axis scores as y in a regression- this seems like it would let you interpret how the communities are distributed in species space. Just a thought NEVER do this! The rotation is not determined in NMDS. If you rotate the NMDS solution you will always have the very same solutios, but the correlations with the axis scores will change. The whole point of having envfit/vectorfit in vegan is that you don't need to calculate the correlations with the axes. The axis rotation is determined in eigenvector ordination, but that does not mean that the direction is meaningful. You can see this with envfit/vectorfit in unconstrained eigenvector ordination, but also in constrained ordination: very rarely the fitted vectors or biplot arrows are parallel to axes. Only if axes are parallel to axes (and go along the axes), then it would be meaningful to look a the relationship of axis and something else. And indeed, there is no constrained NMDS in vegan. It is unconstrained with an interpretation through vector fitting. cheers, jari oksanen Stephen Sefick On Sat, Dec 6, 2008 at 11:58 AM, Manuel Spínola [EMAIL PROTECTED] wrote: Dear list members, Is there any reference or document on how to interpret a constrained non-metrical multidimensional scaling using ecological data? By constrained I mean after fitting environmental covariables, using, for example, the envfit function in the vegan package. Is it possible to interpret the resulting plot in the same way that a constrained ordination, for example CCA? Thank you very much in advance. Best, Manuel -- Manuel Spínola, Ph.D. Instituto Internacional en Conservación y Manejo de Vida Silvestre Universidad Nacional Apartado 1350-3000 Heredia COSTA RICA [EMAIL PROTECTED] [EMAIL PROTECTED] Teléfono: (506) 2277-3598 Fax: (506) 2237-7036 ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- Stephen Sefick Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being mammals. -K. Mullis ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Question on height for hclust function
On 17 Nov 2008, at 21:40, Leigh Fall wrote: I've run a cluster analysis with Jaccard distance and Ward's method. The clustering height (located on the left of the dendrogram) is not scaled to the distance function because the height values range from 0 to 3.5 in increments of 0.5. In Oksanen's Vegan tutorial, the examples of the cluster dendrograms show height values that appear to be the Bray distance values (with various linkage methods) because the height values are less than 1. I'm not sure what my height values reflect. Is the clustering height associated with Wards? Can the the height values be rescaled to the Jaccard values? The height values at the vertical axis depend on the clustering method: they are the fusion levels your particular method uses. For single linkage these are the shortest distances between clusters, for complete linkage they are the maximum distances among clusters (cluster diameters after fusion) etc. For Ward's method they are the values of Ward's criterion. Now you only need to check how Ward's criterion is defined... Bray and Jaccard are in similar range, and the choice between these indices has a negligible effect on the scales. The choice of clustering method has a huge impact. cheers, Jari Oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] output from ordisurf
On Thu, 2008-10-23 at 14:44 -0500, Christopher Chizinski wrote: Is there a way to output the X,Y,Z values for the contour lines from ordisurf? For example, I am using this statement: tmp-with(enviro,ordisurf(fish.nmds,enviro$Temp,choices=c(1,2),add=TRUE)) names(tmp) [1] coefficients residuals fitted.values [4] familylinear.predictors deviance [7] null.deviance iter weights [10] prior.weights df.null y [13] converged sig2 edf [16] hat boundary sp [19] nsdf VeVp [22] mgcv.conv gcv.ubre aic [25] rank gcv.ubre.dev method [28] smoothformula cmX [31] model control terms [34] ptermsassignoffset [37] data df.residual min.edf [40] call I would like to get these XYZ values or find a way to calculate them from the output for inclusion into SigmaPlot. It is probably an easy solution but I could not find what I needed. Dear Christopher Chizinski, I assume you mean the ordisurf() function in the vegan package. If you mean something else, this message is not appropriate and you may stop reading. Function ordisurf (of vegan) returns visibly an object of function gam of the mgcv package (like documented). You can use all mgcv:::gam tools to handle this object, including predict.gam (of mgcv) which can be used to predict new values of surface (z) for any values of the coordinates (x, y). These results can be exported to SigmaPlot or anywhere. If you look inside the function ordisurf, you can see how predict.gam is used to find the predicted value of the surface for a grid of axis values. I changed the ordisurf function in http://r-forge.r-project.org/projects/vegan so that it now adds the data used by the contour() function in a new item called grid in the result. The returned data in grid contain vectors x and y for the grid values on the axis, and matrix z for the fitted surface values for each combination of x and y vectors. The values of z outside the convex hull of observed site are marked as NA. You can directly export these items or reuse them to draw the surface with the contour() command. The grid item is available from vegan working version 1.16-3 (revision 532) in R-Forge, and Windows binary is available (probably) from tomorrow morning (Central European Standard Time). Best wishes, Jari Oksanen -- Jari Oksanen [EMAIL PROTECTED] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
