Re: [R-sig-eco] readOGR and Multiple incompatible geometries in rgdal
On Tue, Jan 20, 2015 at 3:05 PM, Patrick Giraudoux patrick.giraud...@univ-fcomte.fr wrote: Dear all, I have to cope with a MapInfo file (.TAB and associated files) with obviously muliple geometries in (e.g. points and lines) and readOGR (package: rgdal). When reading, I get the following error message: readOGR(.,PacoursIKA) Error in ogrInfo(dsn = dsn, layer = layer, encoding = encoding, use_iconv = use_iconv, : Multiple incompatible geometries: wkbPoint:wkbLineString Of course, the cause is obvious and I can manage reading the file from e.g. QGIS (a dialog box permits to select one of the two geometries) and then export to a single geometry shapefile. The latter can be read from R using readOGR. However, I would like to know if there is a work around within rgdal (eg an extra argument in readOGR) that could make the job done within an R environment. The workhorse in QGIS is gdal as in rgdal, so suppose something may be possible. Best, Patrick Patrick, I don't know the answer to your question but if you post it on the R-sig-Geo list (https://stat.ethz.ch/mailman/listinfo/r-sig-geo) you might get a satisfying answer faster. Cheers, Ivailo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Extract residuals from adonis function in vegan package
On Tue, Mar 18, 2014 at 5:02 PM, Alicia Valdés aliciavaldes1...@gmail.com wrote: ... However, what I attempt to do is to perform an indicator species analysis (ISA) with these residuals. I want to see if I can find species which are indicators for different environmental conditions, but first I would like to remove the differences in species composition due to the study region (which accounts in fact for a big part of the differences in species composition). I am using the packages indicspecies and labdsv for ISA but in none of the cases did I found a way of including this as, for example, a block variable, that's what I attempt to get the residuals. Alicia, following up on my previous comment, I think you might use the regions as a typology on which to base your ISA. So you'll get the characteristic species for each region and there is no need to account for differences in species composition among regions (moreover, I fail to understand why one might need to do so). If you take a look at the help page for multipatt() in indicspecies, you'll see that you need a community data table (your presence/absence matrix) and a site classification (your regions if these are not further classified into meaningful clusters; although you didn't provide more details on how the forest patches relate to the regions you wrote about) to run the analysis. HTH, Ivailo -- The cure for boredom is curiosity. There is no cure for curiosity. -- Dorothy Parker ___ 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 accommodate data with negative values for Canonical Correspondence Analysis in R using vegan Package
On Wed, Feb 26, 2014 at 2:43 PM, Rajendra Mohan panda rmp.iit@gmail.com wrote: ... I have temperature data with negative values which I am not able to include for my CCA ordination. ... Rajendra, I am curious -- why are you not able to include the negative values in the CCA ordination? -- The cure for boredom is curiosity. There is no cure for curiosity. -- Dorothy Parker ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] angular statistics
On Fri, Oct 18, 2013 at 6:16 AM, Michael Marsh sw...@blarg.net wrote: If you want a measure of exposure, i. e., heat, I suggest using the heatload transformation suggested by McCune and Grace (2002). Their assumption is that mid-afternoon, when the sun is in the southwest, is usually the warmest time of day. The formula at the end of Chapter 3 follows: heat load index=(1-cos(degrees-45))/2 McCune, Bruce and James B. Grace. 2002. Analysis of ecological communities. MJM Software Design. Gleneden Beach, Oregon. USA Thanks for the interesting discussion! I'd like to add that although I don't have the book, I found the radiation measures presented in the following paper: McCune, B. and D. Keon. 2002. Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science 13:603–606. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] PCNM function
On Wed, Aug 21, 2013 at 9:19 PM, Valerie Mucciarelli soccerm...@hotmail.com wrote: ... I receive this error: Error in moran.I.uni(eigenvector.mat[, i], mat.W, scaled = scaled, normalize = normalize, : could not find function boot Timing stopped at: 0 0 0.04 Hi Val, the error message says that it cannot find the boot function, so try install.packages(boot) and then library(boot) if you have it installed. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Offsets in Poisson or Neg. Bin regression
On Wed, Jun 26, 2013 at 12:42 PM, Scott Foster scott.fos...@csiro.au wrote: Hi again Ivailo, Yes, the `offset' and the covariate are the same thing. Including them both simply alters the functional form of the linear predictor in your model. No, they are not collinear in the typical sense as there is only one parameter (linear form) between them -- the offset term does not have a parameter that will be estimated associated with it. For example, with log( effort) added as a linear covariate the log-link GLM is log( E(y)) = offset + beta * log( effort) + other_stuff = log( effort) + beta * log( effort) + other_stuff = beta_1 * log( effort) + other_stuff where beta_1=1+beta. If you test that beta==0 (which is not beta_1) then you are testing that the effect of effect is purely scaling (as per nomenclature before). This is the same as McCullagh and Nelder's testing to see if beta_1==1. Thanks for the pointer to McCullagh and Nelder -- I didn't know that they suggested that. Thanks a lot for the brilliant explanation, Scott! Now things make sense to me, and I'm interested what the modeling strategy would be if beta_1 turns out to be significantly 1. Would the option you mention below be viable alternative in that case? My depiction of the effect of effort as f( effort) is to allow for the possibility that the effect of effort may be non-linear on the link scale. A simple example is when f(effort) is a low-order polynomial. Departures from effort being a purely scaling term may extend beyond linearity. One may even want to consider regression splines or even more flexible GAMs. Having said all this though, it is my practice to be quite conservative with including effort as anything but a scaling variable (offset). It seems to me that there needs to be good reason before jumping to strong conclusions that may have no basis in the phenomenon under study. I imagine that the fishing-net example you mentioned earlier could be a case of a non-linear effect of effort -- wouldn't this warrant modeling the effort as being non-linear on the link scale? Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Offsets in Poisson or Neg. Bin regression
On Tue, Jun 25, 2013 at 2:02 PM, Scott Foster scott.fos...@csiro.au wrote: Hi Ivailo, Good question. Difficult to answer, which is probably why you haven't had any responses yet (that the list has seen). If you include an offset term with a log link function then you are assuming that the random variable (counts say) depend on the offset with a known relationship. Generally, this is precisely what you want to do -- for example standardising counts for the sampling effort taken to obtain those counts. However, in some situations it is conceivable that the sampling effort itself affects the count random variable. An example may be fish in a trawl net -- as the net gets full it becomes less and less efficacious. In this case you may expect that a single unit of effort change will have different effect when there has been lots of previous effort to when there hasn't. Thanks for commenting on that, Scott! Although both alternatives you mention above assume that the RV depends on either the offset or the sampling effort, but aren't these are essentially the same? If I thought that I was in the latter case, I may fit a model like log( E( count)) = log( effort) + f(effort) + other stuff. The function f(effort) can take any form, including beta*log(effort). In such a case a test of beta==0 is equivalent to testing if the effect of effort is purely scaling or if it is something else/sinister. General forms of f(effort) may tell you much more but may also be much more confusing. To choose between the two cases above (offset versus offset+covariate), I would base my choice largely on prior knowledge of the system under study. This is especially so if I don't have much data. My approach to modeling counts was primarily based on the widespread advise that varying effort should be considered by adding an offset to the model, but when I consulted the book by McCullagh and Nelder (1989), I found on pp. 206-207 hat they actually estimated the log(effort) term as being ~ 1. So started my confusion on the topic to offset or to estimate ;-) It never occurred to me, though, that the effort could be entered both as an offset *and* as a covariate into the model. As these two terms have good chances being collinear, I wonder how one can then separate their influence on the RV. I do not fully understand your idea regarding the form of the function f(effort) , but I get that if the coefficient of effort is estimated as == 0, then it should be concluded that effect of effort should be retained *only* as an offset to account for the scaling. Am I right? Thanks again for your elucidating comment, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Offsets in Poisson or Neg. Bin regression
On Tue, Jun 18, 2013 at 11:10 AM, Matias Ledesma matutet...@hotmail.com wrote: Philip and Alain, Thank you for your assistence, So, that mean that the fuction offset its only possible if there is a relationship between the damaged number of embryos and the total number of embryos per amphipod as you explained? As I'm facing a similar problem, I'd like to know as well if a variable should be passed as an offset to the formula only when it influences the outcome in some (linear) way. Does it make sense to include the exposure variable in the model as a regular input first, and if it's coefficient is around 1 to be taken as an indicator that it is better that variable to be included in the model as an offset? Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] quantifying directed dependence of environmental factors
On Thu, Mar 7, 2013 at 9:20 PM, Sarah Goslee sarah.gos...@gmail.com wrote: ... There's a fair bit of literature on Mantel-based path analysis, and other similar dissimilarity-based approaches. SEM can be used with composition as well, although not (I think) with the intermediate step of calculating dissimilarities. Besides journal articles employing those techniques, I like both of these: J. B. Grace, Structural Equation Modeling and Natural Systems, Cambridge University Press, Cambridge, UK, 2006. B. Shipley, Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference, Cambridge University Press, Cambridge, UK, 2000. I recently stumbled on a great book on path modelling using PLS (with R) that is freely downloadable at http://is.gd/BxqIEL Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Defining Different Groups with Nonparametric Data (vegan package)
On Sun, Nov 25, 2012 at 6:32 PM, Charles Jones cnjo...@vt.edu wrote: Professor Oksanen: Thanks for the reply and sorry for the confusion! (I'm still trying to wrap my head around the multivariate lingo.) 1.) The scores from my NMDS analysis are non-normal (tested using the multivariate Shapiro-Wilks test.) 2 and 3) I am using these scores as input for the Cluster Analysis (Ward's Method) to define several different groups. One of the underlying assumptions associated with Wards algorithm is that the input is normal. So, the question is, is it okay to ignore that assumption (normality of input data) to define the groups? Since I used the MMRP test (nonparametric) to show there is a difference between the groups, it seems like this is reasonable. However, I wasn't quite sure. Thanks Again! Nate Jones Nate, IIRC Ward's method assumes multivariate normality of each cluster formed by multivariate observations (as it treats cluster analysis as an ANOVA problem and so it is sensitive to outliers), but as this method tends to create rather small clusters I would suggest you to try some of the other algorithms available in R to test the stability of the clustering obtained by Ward's linkage. However, I wonder why do you need to get trough the NMDS step -- can't you directly cluster the cases in your dataset? Something like: distances - dist(USArrests, method = euclidean) # we need Euclidean distances here fit - hclust(distances, method=ward) # Ward's method plot(fit) # plot dendrogram grps - cutree(fit, k=5) # suppose you can interpret 5 clusters rect.hclust(fit, k=5, border=red) # add red boxes around the 5 clusters in dendogram Then, you could experiment with mmrp() and different grouping indexes (obtained from different cuts trough the dendrogram), but I am not sure if this wouldn't be regarded as circular reasoning. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Post-hoc test after rm-ANOVA
On Fri, Sep 14, 2012 at 1:55 PM, Nils Gülzow nils.guel...@gmx.de wrote: Dear community, I have a general statistic question. I have done a rm-Anova with ezAnova package. My rm-Anova was a mixed within-and-between, design, containing two factors (each has two levels) for between and the sampling days for within. The ezAnova worked fine and now I would like to perform a Post-hoc test (like TukeyHSD) to get the significant differences between the factors over time. Is it simply using the TukeyHSD to test the differences between the factors or how can I get the significant difference between the factors over time? Thanks in advance, Nils Nils Gülzow Dear Nils, if you'd like to stick the the ezAnova package, then the ezPlot() function produces error bars that might help you to visually evaluate post-hoc comparisons. You can always run TukeyHSD() on an aov() object or use glht() from the multcomp package if you have a lm() object. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] option --enable-R-shlib
On Fri, Jun 15, 2012 at 7:05 AM, Michel Rapinski mrapi...@uottawa.ca wrote: Hello, How can I check if my the R program that I've installed is compiled with the option --enable-R-shlib? Michel, You can check this by using the command ldd /usr/lib/R/bin/exec/R. If libR.so (the shared R library) appears in the resulting list, then you have R built with --enable-R-shlib. And, if need be, how do I enable that option? If needed, you can enable this option during the build process by passing the option, e.g. ./config --enable-R-shlib, just before make. Hope this helps, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] option --enable-R-shlib
On Fri, Jun 15, 2012 at 6:20 PM, Michel Rapinski mrapi...@uottawa.ca wrote: Hi Ivailo, Do I run this command in R? Or in terminal? Maybe I should have added that I have a mac, I don't know if that makes a difference. Sorry, Michel, I missed to mention that ldd needs to be run in the terminal, and don't forget to provide the correct path to the R executable. You can check http://v.gd/ZPswag for further details on how to build R under Mac OS X. Good luck, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] species richness
On Wed, May 30, 2012 at 10:42 PM, Linda Bürgi patili_bue...@hotmail.com wrote: Hi, I am interested in testing whether increasing the number of plant species sampled increases the number of herbivore species I find, irrespective of the number of herbivores collected. To do that, I was thinking of fixing the number of herbivores collected (e.g. 100) and randomly pulling 99 samples of 100 herbivores from all possible combinations of 2 plant species, 3 plant species, 4 plant species, etc. This should then yield a curve with number of plant species on the x axis and average number of herbivore species found on the y axis, always for a sample of 100 herbivores. Does such a function already exist? My data (see below) is in matrix form with columns representing herbivore species (14) and rows representing plant species (14), with the numbers in the cells representing number of specimens collected per herbivore and plant species combination. I’m not quite sure how to tackle this…. Thanks! Linda Hi Linda, your question whether increasing the number of plant species sampled increases the number of herbivore species seems to require a contingency table and a corresponding test. I am not sure, however, how to treat the requirement irrespective of the number of herbivores collected as usually the number of species increases with the number of individuals sampled. Perhaps to test additionally if the proportion of herbivores sampled is independent of the proportion of hon-herbivores in the sample(s)? Hope this helps, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] QIC (quasi-likelihood information criterion) in gee
On Thu, May 10, 2012 at 3:51 PM, Wagner Tassinari wtassin...@yahoo.com.br wrote: Hi, I'd like to know how extract the QIC (quasi-likelihood information criterion) in GEE model (using gee or geeglm commands) in R. Hi Walter! I think this - http://is.gd/myQ6IZ - could help. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Testing difference between diversity indices with vegan::oecosimu
On Thu, Apr 26, 2012 at 12:17 AM, Kay Cichini kay.cich...@gmail.com wrote: Hello all, I'd like to test if total diversity differs between two communities. For each community several samples were taken and abundances collapsed over groups to compute total diversity for each group. I tried to use vegan::oecosimu to test non-randomness of my statisitc (difference in Simpson-Diversity indices of collapsed abundances) - however, I am not quite sure if I oversee posssible pitfalls: library(vegan) data(dune) # a grouping variable: gr - gl(2, nrow(dune)/2) divdiff - function(x) abs(diversity(colSums(x[gr == 1, ]), simp) - diversity(colSums(x[gr == 2, ]), simp)) # testing function: divdiff(dune) oecosimu(dune, divdiff, r2dtable, nsimul = 1999) # oecosimu with 1999 simulations # simulation method r2dtable # alternative hypothesis: true mean is not equal to the statistic # statistic z 2.5% 50% 97.5% Pr(sim.) # statistic 0.00275 -0.20996 0.00013 0.00280 0.01 0.98 Dear Kay, I am not sure about any possible pitfalls with your approach, but I have tested the same data using the randomisation functions of the rich library, and found that neither the Simpson diversity nor the simple species richness differ significantly among the defined groups. Here are the results following your example: library(rich) # prepare data one - as.data.frame(dune[gr == 1, ]) two - as.data.frame(dune[gr == 2, ]) data - list(one, two) # compare cumulative species richness c2cv(com1=data[[1]],com2=data[[2]],nrandom=1999) #$res # #cv1 27. #cv2 28. #cv1-cv2 -1. #p 0.4220 # N.S. #quantile 0.025 -4. #quantile 0.9754. #randomized cv1-cv20.0225 #nrandom1999. # compare the Simpson diversity simp.one - diversity(dune[gr == 1, ], simp) simp.two - diversity(dune[gr == 2, ], simp) c2m(pop1=simp.one,pop2=simp.two,nrandom=1999,verbose=FALSE) #done. #$res # #mv1 8.630e-01 #mv2 8.773e-01 #mv1-mv2-1.439e-02 #p 2.440e-01 # N.S. #quantile 0.025 -3.456e-02 #quantile 0.975 3.351e-02 #randomized mv1-mv2 3.899e-04 #nrandom 1.999e+03 # The possible pitfalls might be hidden under the different results ;-) Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ 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 geographical coordinates of sampling points following (by bearing and distance) a georeferenced one?
On Fri, Apr 6, 2012 at 10:26 PM, Tammy Wilson t...@aggiemail.usu.edu wrote:
Trigonometry will work for projected coordinate systems like UTM:
Try this:
# number of points
n = 30
# reference location
ref.pt = cbind(1,2)
# generate some random directions
# This can be your list of your bearings converted to radians
dir - runif(n,0,2*pi )
# generate some random distances
# This can be your list of distances in map units (e.g. m)
dist - runif(n,0.5,10)
#empty matrix for coordinates
coords.mat = cbind(rep(0,n),rep(0,n))
#using trigonometry to calculate object location
for (i in 1:n){
coords.mat[i,1] = ref.pt[1]+(sin(dir[i])*dist[i])
coords.mat[i,2] = ref.pt[2]+(cos(dir[i])*dist[i])
}
plot (coords.mat)
points(ref.pt,col=2)
Best,
Tammy
Thank you for the code, Tammy! Meanwhile Michael Summer pointed my
attention the the geosphere package and the destPoint() function seems
to do exactly what I was
searching for.
Cheers,
Ivailo
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[R-sig-eco] How to calculate geographical coordinates of sampling points following (by bearing and distance) a georeferenced one?
Der fellow R-users, I have several sampling sites and in each site the starting plot (i.e. the first one that have been sampled) is georeferenced with a GPS but the following ones are just described by their distance and bearing in relation to the previous one. Is there a quick way to calculate (by using some of the spatial R-packages) the geographic coordinates of the sapling plots at each location that have not been explicitly georeferenced? Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] adonis: error in rowSums
On Wed, Feb 8, 2012 at 4:10 PM, Caroline Wallis c.wal...@worc.ac.uk wrote: I am trying to use the 'adonis' function in the 'vegan' package to assess differences in water depth and water velocity between areas of a river channel categorised by surface flow type (6 types in total, unequal sample sizes). Sample Data (LB): SFT Depth Vel BSW 0.18 1.2 BSW 0.16 1.03 BSW 0.16 0.98 BSW 0.22 0.53 BSW 0.11 0.668 BSW 0.14 0.432 BSW 0.12 0.391 BSW 0.16 0.647 BSW 0.2 0.903 BSW 0.3 0.594 BSW 0.37 0.429 The dependent data was used in data frame format, rather than a dissimilarity matrix. Using the call 'adonis(formula=SFT~Depth*Vel,data=LB,permutations=999,method=canberra,strata=NULL)' I get the following error: Dear Caroline, on a second thought I realized that you might want to flip the variables in your adonis() call so that Depth and Vel are the dependent variables and SFT the predictor. Something like: # create a dataframe containing the dependent variables; sampledata is the data frame you posted a sample of dep.var - data.frame(sampledata[,2:3]) # extract the predictor variable pred.var - sampledata[,1] # perform MANOVA model - adonis(dep.var ~ pred.var, permutations = 999,method = canberra, strata = NULL) Hope this helps, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Comparison of gam and gamm fits
On Wed, Feb 8, 2012 at 11:17 AM, Thackeray, Stephen J. s...@ceh.ac.uk wrote: Dear all, My apologies, the figure seems to have been removed from the mail! Hope this attachment makes it instead... Steve Dear Steve, attachments are generally stripped off the message, so try hosting the figure somewhere and post the link instead. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] adonis: error in rowSums
On Wed, Feb 8, 2012 at 4:10 PM, Caroline Wallis c.wal...@worc.ac.uk wrote:
I am trying to use the 'adonis' function in the 'vegan' package to assess
differences in water depth and water velocity between areas of a river
channel categorised by surface flow type (6 types in total, unequal sample
sizes).
Sample Data (LB):
SFT Depth Vel
BSW 0.18 1.2
BSW 0.16 1.03
BSW 0.16 0.98
BSW 0.22 0.53
BSW 0.11 0.668
BSW 0.14 0.432
BSW 0.12 0.391
BSW 0.16 0.647
BSW 0.2 0.903
BSW 0.3 0.594
BSW 0.37 0.429
The dependent data was used in data frame format, rather than a dissimilarity
matrix.
Using the call
'adonis(formula=SFT~Depth*Vel,data=LB,permutations=999,method=canberra,strata=NULL)'
I get the following error:
Error in rowSums (x, na.rm=TRUE)
'x' must be an array of at least two dimensions
I examined the adonis code to find 'x'. It first appears at the permutation
stage:
if (missing(strata))
strata - NULL
p - sapply(1:permutations, function(x) permuted.index(n,
strata = strata))
tH.s - lapply(H.s, t)
tIH.snterm - t(I - H.snterm)
f.perms - sapply(1:nterms, function(i) {
sapply(1:permutations, function(j) {
f.test(tH.s[[i]], G[p[, j], p[, j]], df.Exp[i], df.Res,
tIH.snterm)
However I'm no closer to understanding what 'x' is or how to correct the
error. If anyone could offer any advice or help I'd be very grateful.
I also tried transposing the data but this generated a different error!
Regards,
Caroline Wallis
Dear Caroline,
you need to provide a community table (i.e. species x samples data
frame) to adonis, but you have provided a single categorical variable.
Therefore I am not sure if the adonis() function would be appropriate
for the analysis you're trying to perform.
Cheers,
Ivailo
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Re: [R-sig-eco] question concerning nmds.min
2012/2/2 Gian Maria Niccolò Benucci gian.benu...@gmail.com: Hi all, I am trying to run a nmds with function nmds() Once I run the following code I wonder how to look to the R2 and stress value of the chosen configuration. iris.nmds - nmds(iris.md, mindim=2, maxdim=2, nits=50) iris.nmin - nmds.min(iris.nmds) Which among the 50 configuration the functions chose? How to look to its stress and R2 values? If I type iris.nmin I only got the X1 and X2 coordinates... Thanks in advance, Gian Dear GIan, try the following: library(vegan) data(dune) sol - metaMDS(dune) sol Call: metaMDS(comm = dune) global Multidimensional Scaling using monoMDS Data: dune Distance: bray Dimensions: 2 Stress: 0.1183195 Stress type 1, weak ties Two convergent solutions found after 5 tries Scaling: centring, PC rotation, halfchange scaling Species: expanded scores based on 'dune' Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ 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 concerning nmds.min
2012/2/2 Gian Maria Niccolò Benucci gian.benu...@gmail.com: ... Which among the 50 configuration the functions chose? How to look to its stress and R2 values? Forgot about the R-squared - according to the vegan FAQ 1 - stress^2 transforms nonlinear stress into quantity analogous to squared correlation coefficient. Function stressplot displays the nonlinear fit and gives this statistic. HTH, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] glmmADMB v 0.7.1
On Thu, Jan 12, 2012 at 7:09 PM, Christopher Rota ctr...@mail.missouri.edu wrote: Hi Mollie, Thanks for your help. I had the same thought and ran this model with my categorical variables as factors, with the same result. I use the dummy variables simply because it gives me more control over how I interpret my intercept parameter (I'm not sure if / how one can control which categorical variable is the intercept in glmmadmb). Thanks again, Chris Dear Chris, if I remember correctly, although 32-bit Windows can be set-up to use more that 4GB RAM it cannot provide more than ~2GB to any process. Perhaps you need to try running your model on a 64-bit machine. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] fitting multiple variance structures in gamm
On Thu, Jan 5, 2012 at 11:30 AM, Marc Taylor marchtay...@gmail.com wrote: Dear r-sig-ecology group, I am trying to fit a variance structure to two different covariates at once using varComb (nlme package) in a gamm (mgcv package) model. When I fit the same type of variance structure to each covariate individually, soolutions were found and the improvements were significant. However the fitting of both covariates at once does not find a solution and results in an error. Does anyone have any suggestions for me? Below is an example from my script. Many thanks in advance, Marc I set up the combined variance structure in this way: varstrComb - varComb( varConstPower (form = ~ COV1 ),varConstPower (form = ~ COV2 ) ) Then fit the model: gamm(fmla, data=db, weights=varstrComb, method=REML) I receive the following Error message: Error in solve.default(-val) : Lapack routine dgesv: system is exactly singular Dear Marc, as the error message says, it appears that the covariance matrix is singular and.cannot be inverted. You should check your covariates to see if one of these is not a function of the other. Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Nestedness analyses in Vegan
On Mon, Nov 28, 2011 at 8:32 PM, Ricardo Solar rrso...@gmail.com wrote: Hi people; I'm thinking about a solution to show that a sample coming from 3 types of killing solutions are nested. However, when I tried to do this with nestedness function in Vegan I couldn't specify the groups. I guess it's impossible at all, so my question is: is there any other analysis where I can show this nestedness pattern? Att, RSolar Dear Ricardo, as you didn't specify your data structure (and that is important for successfully performing a nestedness analysis), we have to guess it. In in any case it should be entered as community data - i.e. site x species incidence matrix. An interesting aspect of your question is how you should organise the community data - will you have three separate matrices and compare the nestedness metric among them or will you combine these in one common matrix to study (common) species responses to the three killing solutions. I don't have an answer for that, but perhaps someone on the list might provide further insight... Cheers, Ivailo -- UBUNTU: a person is a person through other persons. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
