[R-sig-eco] graphs in corner of the pane, Windows 10 is the difference
Clustering with mvpart and rpartpca, graphs fill the pane normally in
Windows 7 as copied into Word 7, But with the same script and datasets,
they only occupy the upper left-hand quarter of the pane in Windows 10
on SURFACE BOOK, again, as copied into a Word 7 document.
when I test graphic parameters on either computer, following suggestions
by another reader, I get:
> par("mfrow")
[1] 1 1
> par("plt")
[1] 0.1455083 0.8544917 0.2342400 0.8425600
>
I would like to re-set the graphics output in my Windows 10 computer so
that I getgraphs that fill the graphics pane in Word 7.
Mike Marsh
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[R-sig-eco] extent of graphic image in graph pane
I've been using mvpart with graphic output (a tree displaying env. data governing splits), augmented by rpart.pca, to explore environmental relations of vegetation data on my old Toshiba satellite computer with R version 2.15.0 (x32). I've loaded R version 3.3.3 (x64) on a Surface Book. Graphic images on the old computer appeared to extend to the limits of the graphic pane, but with the identical script in R 3.3.3, they are confined to the upper left corner of the pane as if the pane was ready to accept 3 more images of the same size. This makes it difficult to copy and paste a usable sized image into a Word document I have searched the graphics chapter of R-Intro without success. ___ 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 for significance of clusteredness from, hclust (vegan package)
hello Ansley, You might want to look at the multi-respnse permutation procedure (mrpp in R), which examines the difference among clusters, that is, how much greater similarity the elements of cluster have to each other as compared to the overall similarity of elements of the data. I am not well enough informed to advise on how the outputs of this test satisfy your requirements, but have used it myself to assess the degree of difference among clusters created by hclust and the "significance" of that difference. Mike On 10/5/2016 3:00 AM, r-sig-ecology-requ...@r-project.org wrote: > Re: [R-sig-eco] Adonis for significance of clusteredness from > hclust (vegan package) 2016.10.03. 21:52 keltez?el, Ansley Silva ?a: > Hello: > > I have created a dendrograms using hierarchical cluster analysis with the > vegan package (function: hclust). > > By visually observing the dendrogram, I have determined that there are 3 > main clusters if I "cut" the tree at the height 0.25 (please see the > dendrogram from the code). > I then created a new dataset, which is essentially the same as the > original, but I have added the categorical variable Group to represent > these 3 main clusters. > ST0 is group a, AP0 and AP100 is group b, and AP200 AP300 ST100 ST200 ST > 300 is group c. > I want to now if they are significantly different from each other. I > understand, from the output pasted below, that I can accept that there is a > significant effect of Group. Is this the only thing I can say from > Permanova? What would be the code for a follow up test to look at > pair-wise significant differences? > Thanks very much. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] R-sig-ecology Digest, Vol 90, Issue 6
All, How can I save (rename as a vector?) the "where" output of plot assignment to groups from mvpart() to use as input to mrpp of the same data? Mike Marsh On 9/10/2015 3:00 AM, r-sig-ecology-requ...@r-project.org wrote: Send R-sig-ecology mailing list submissions to r-sig-ecology@r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-sig-ecology or, via email, send a message with subject or body 'help' to r-sig-ecology-requ...@r-project.org You can reach the person managing the list at r-sig-ecology-ow...@r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-ecology digest..." Today's Topics: 1. How to incorporate spatial autocorrelation inmultivariate GLM (Alexandre F. Souza) 2. Re: How to incorporate spatial autocorrelation in multivariate GLM (Tim Meehan) -- Message: 1 Date: Wed, 9 Sep 2015 09:25:12 -0300 From: "Alexandre F. Souza" To: Lista de discussao R-sig-ecology Subject: [R-sig-eco] How to incorporate spatial autocorrelation in multivariate GLM Message-ID: Content-Type: text/plain; charset="UTF-8" Dear friends, I would like to ask for some advice. I am embarking in the analysis of species occurrence date across biogeographic scales in South America. I am willing to try to jump from more traditional distance-based multivariate analysis (e.g., RDA on hellinger-transformed abundance data) to multivariate GLM as proposed by Warton (mvabund package) and also by Yee (VGAM package). However, distance-based methods have grown to incorporate spatial dependency through the development of MEM and AEM techniques, which model symmetric and asymmetric spatial relationships and can be included in the explanatory side of the analysis. Reading the multivariate GLM papers, however, I have not seen clear mention on how to control or include spatial autocorrelation. I am thinking of including MEM and perhaps AEM variables simply as co-variables added to the explanatory environmental variables in the multivariate GLM. Is this a step I will regret later on? Thanks in advance for any thoughts, All the best, Alexandre ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] get species within sites ordihull polys
Tim, I've used mvpart to cluster, and then rpart.pca the resulting regression clustering. mvpart requires a corresponding environmental data set. The pca plot has what you require, polygons like ordihull based on and showing plots (rows in your data), and vectors to named species. I have assumed that the distance from the centroid to each species corresponds to its importance in configuring the output, but I'm a novice andwould like more information on that. Mike Marsh On 9/26/2015 3:00 AM, r-sig-ecology-requ...@r-project.org wrote: get species within sites ordihull polys Date: Fri, 25 Sep 2015 18:58:19 + From: "Howard, Tim G (DEC)" To:"r-sig-ecology@r-project.org" Subject: [R-sig-eco] get species within sites ordihull polys Message-ID: Content-Type: text/plain; charset="us-ascii" All - Consider clusters of points in an NMDS with those clusters determined in some way (I'll use hclust below). Then consider plotting the species on that ordination. I'd like to automatically find which species are 'most associated' with each cluster. Perhaps that translates to finding those species that fall within an ordihull of each group. Before I stumble down into the world of the sp package and spatial overlaps perhaps this is already a part of vegan or another package. ###Example: library(vegan) data(dune) ord <- metaMDS(dune) # get some groups based on hclust dis <- vegdist(dune) clus <- hclust(dis, "average") plot(clus) rect.hclust(clus, 3) grp <- cutree(clus, 3) #plot the mds with the groups mdsPlot <- plot(ord, type="n", display = "sites") points(ord, display = "sites", col="red", pch=19) ordihull(ord, grp) #plot the species points(ord, display = "species", col = "blue", pch=19) ###End example This isn't the best example because species points don't fall in more than one of the black polygons. But, my question: Can I easily identify which blue points (species) fall within the polygon? Or can I easily identify which species are 'most important' (most abundant?) for defining each of the three groups? Thanks for any pointers Tim Howard ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Independence of vegetation samples
Reading David Warton's reply to Rajendra made me realize that my question (below) is related to his, and that I should specify the objectives of my investigation. I'm interested to see if there are separate, distinguishable plant communities/associations related to environmental variables, and if these relationships differ among the different vegetation life forms (e.g., shrubs, graminoids, herbaceous annuals or perennials). I've used mrpp() to test for closeness of relationships among clusters distinguished by hclust() and mvpart() clustering, and used manova() as a further test of the responses of life forms to different environmental variables. , Original question: Is there a method in R for testing for independence of vegetation samples, for example because of relative proximity of different samples? I would like to treat the 3 radially arranged transects of Jornada Line Point Index plots as different sample units. Mike Marsh Washington Native Plant Society ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Independence of vegetation samples
Is there a method in R for testing for independence of vegetation samples, for example because of relative proximity of different samples? I would like to treat the 3 radially arranged transects of Jornada Line Point Index plots as different sample units. Mike Marsh Washington Native Plant Society On 9/3/2015 3:00 AM, r-sig-ecology-requ...@r-project.org wrote: Send R-sig-ecology mailing list submissions to r-sig-ecology@r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-sig-ecology or, via email, send a message with subject or body 'help' to r-sig-ecology-requ...@r-project.org You can reach the person managing the list at r-sig-ecology-ow...@r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-ecology digest..." Today's Topics: 1. Re: Using multiple species data for gam (Rajendra Mohan Panda) 2. Fwd: Using multiple species data for gam (Rajendra Mohan Panda) 3. comparision of lsmean and significant interaction (Mehdi Abedi) -- Message: 1 Date: Wed, 2 Sep 2015 18:08:16 +0530 From: Rajendra Mohan Panda To: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] Using multiple species data for gam Message-ID: Content-Type: text/plain; charset="UTF-8" Dear All I find it difficult to run VGAM and MARS for multi-response data. In both the models, I get an error message "variable names are limited to 1 bytes". Is this due to my big data structure or else ? For your kind information, I have 1500 spp. on 434 site locations, and I want to see the impact of environment on community structure. I have to analyse how the Western Himalaya community behaviour differ from the Eastern Himalaya. I have been struggling to accommodate my data for model fitting since long, could you please give some insights on my idea and how can I tackle the error for successful model run. I always appreciate your valuable advise. Best Regards Rajendra M Panda School of Water Resources Indian Institute of Technology Kharagpur On 18 February 2015 at 09:32, Rajendra Mohan panda wrote: Dear Prof David Warton Thanks a lot for your nice introspection on my data. I appreciate your valuable comments. I am also trying to explore gamm or VGAM to match its suitability with data. Its fine. However, I am thinking to reduce my data structure by removing some of the species showing interspecific correlation. Honestly speaking I do not have thought of it. Can you please give more insights regarding this (interspecies correlation). I am also interested in studying species-environment relationship (not by CCA or RDA). Your kind comments are highly appreciated. With Best Regards Rajendra M Panda School of Water Resources Indian Institute of Technology Kharagpur, India On Wed, Feb 18, 2015 at 4:36 AM, David Warton wrote: Hi Rajendra and Greg, A couple of quick thoughts: Firstly, Rajendra the method that is applicable to your data really depends on the research question - what is it that you are trying to achieve. It is always hard to offer help on what analysis method is suited to a question without knowing the original research objective. The gamm function for example might be useful to you if you are primarily interested in predictive modelling, and also if you think that you have a common nonlinear response to environmental variables with some "noise" around this pattern for different spp (which can be represented as random effects). You could alternatively use this function to fit a separate smoother for each spp but that would be a pretty complicated model and few would have sufficient data to justify that level of model complexity. VGAM y Thomas Yee offers and option in between these two. Secondly, something you need to worry about with this type of data is interspecies correlation - for various reasons (including species interaction), it is widely thought and even better often observed that species are correlated in abundance (or presence/absence, whatever) even after accounting for environmental predictors. This makes the problem multivariate. If you care about making joint inferences across species and you don't account for correlation between species you can get things quite wrong. The gamm function I think could handle residual correlation, but not the way you specified it, and it would have a lot of trouble, unless you have only a handful of species and quite decent abundance data on each. On the other hand if you are just making predictions separately for each spp then you don't need to worry too much about this. All the best David David Warton Professor and Australian Research Council Future Fellow School of Mathematics and Statistics and the Evolution & Ecology Research Centre The University of New South Wales NSW 2052 AUSTRALIA phone (61)(2) 9385-7031 fax (61)(2)
[R-sig-eco] biplot question: "Error in 1L:n : argument of length 0"
I am trying to obtain biplots of NMDS results, ideally like the rpart.pca() result with mvpart. Can someone easily tell me why I get this error: Error in 1L:n : argument of length 0 from this script, and graphical output (below) lacking labels for either sites or species? x and y matrices are shown below script. modifying "y" by removing NaN rows did not change output. Thanks!, Mike Marsh sw...@blarg.net -- Q.wd<-as.data.frame(read.table(file.choose(),header=T)) > #text dataset is Q.WD.foliar.revised.txt > library(vegan) > Q09shrub.min<-vegtab(Q09shrub,min=2) > Q09shrub.std<-decostand(Q09shrub.min, method="max") > Q09shrub.dist<- dist(Q09shrub.std) > Q09shrub.ward<-hclust(Q09shrub.dist,method="ward",members=NULL) > NMDS.Q09shrub<-metaMDS(Q09shrub) Wisconsin double standardization Run 0 stress 0.08176961 Run 1 stress 0.1011811 Run 2 stress 0.07507627 ... New best solution ... procrustes: rmse 0.04824229 max resid 0.1222105 Run 3 stress 0.1011877 Run 4 stress 0.07507602 ... New best solution ... procrustes: rmse 0.0001471213 max resid 0.0002974951 *** Solution reached > x<-plot(NMDS.Q09shrub, "sites") > y<-plot(NMDS.Q09shrub, "species") > biplot(x,y) Error in 1L:n : argument of length 0 > ordicluster(NMDS.Q09shrub, Q09shrub.ward) > > x $sites NMDS1 NMDS2 9Q05-ST -0.7781618 -0.05087985 9Q06-VS 0.6462465 -0.41377399 9Q12-ST -0.3126179 -0.80107514 9Q15ECS -0.8780461 0.20632944 9Q15WST -0.7672079 0.8850 9Q16-VS 0.8652229 0.56684307 9Q19-ST 0.1374252 -0.41322443 9Q24-ST 0.3140148 -0.12115606 9Q26-ST -1.0044059 -0.15339828 9Q29-DS 0.9608797 0.35394121 9Q33-VS 0.8166505 -0.06138349 $species NULL attr(,"class") [1] "ordiplot" > y $sites NULL $species NMDS1 NMDS2 ACMA3 NaN NaN AMAL2 -0.05672919 -0.81171544 ARRI2 0.84801299 0.24651319 ARTR2 -0.93597835 0.02417604 ARTR4 -1.01337771 -0.05343535 GRSP NaN NaN CHVI8 -0.74187745 -0.02949452 ERBL2 NaN NaN ERNA10 -0.61157997 0.47464632 NEST5 0.87499085 -0.04070596 PREM -0.90401815 1.25730322 PRVI NaN NaN PUTR2 0.16079095 -0.36676073 RICE -0.35775047 -0.46386377 ROWO NaN NaN SYAL2 NaN NaN SYOR2 -0.36836460 -1.13451214 attr(,"class") [1] "ordiplot" > ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] report out by t.test
I test differences between frequency of hits of exotic annual forbs in plots on two sites, Q and WD. > Q<-c(13,0,10,2,0,0,1,0,0,1,5) > WD<-c(0,0,1,0,0,0,0,0,0,0,1) > t.test(Q,WD) Welch Two Sample t-test data: Q and WD t = 1.9807, df = 10.158, p-value = 0.07533 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3342006 5.7887460 sample estimates: mean of x mean of y 2.9090909 0.1818182 The p-value is greater than 0.05, thus does not reach the 95% confidence level, yet the difference in means is reported as not equal to 0. Am I encountering a one-sided versus two sided comparison that I don't understand, or is ther another explanation? Mike Marsh ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] R-sig-ecology Digest, Vol 71, Issue 3
Gian, Your question, "how can I extract the names of the species (and even their abundances) that are common and the species that are not common between the different samples in my dataset?" is a common question in ecological literature about what are called "Indicator Species". These are species with occurrence usually in only one of several habitat types and high abundance in that habitat. You can use that term as a search term to find out more, but I'd refer you to Jon Bakker's compilation in R of a function to provide what he calls Indicator Values. See: Bakker, J.D. 2008. Increasing the utility of Indicator Species Analysis. Journal of Applied Ecology 45:1829-1835. and Dufrêne, M. & Legendre, P. (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs 67:345-366. Mike Marsh On 2/4/2014 3:00 AM, r-sig-ecology-requ...@r-project.org wrote: Now my >question is, how can I extract the names of the species (and even their >abundances) that are common and the species that are not common between the >different samples in my dataset? > ___ 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
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 Mike Marsh On 10/16/2013 3:00 AM, r-sig-ecology-requ...@r-project.org wrote: Send R-sig-ecology mailing list submissions to r-sig-ecology@r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/r-sig-ecology or, via email, send a message with subject or body 'help' to r-sig-ecology-requ...@r-project.org You can reach the person managing the list at r-sig-ecology-ow...@r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-ecology digest..." Today's Topics: 1. angular statistics (Peter Nelson) 2. Re: angular statistics (Holland, Jeffrey D) 3. Re: angular statistics (Don McKenzie) 4. Re: angular statistics (Peter Nelson) 5. Re: angular statistics (Donald McKenzie) -- Message: 1 Date: Tue, 15 Oct 2013 09:59:38 -0700 From: Peter Nelson To: r-sig-ecology@r-project.org Subject: [R-sig-eco] angular statistics Message-ID: <0c3c26ea-5599-4570-b205-5feecb70b...@cfr-west.org> Content-Type: text/plain; charset=us-ascii I want to include the exposure (measured in degrees, for example, East-facing is 90) of various coastal sites in GLM and CCA analyses. Is there an appropriate transformation that I can apply to these measurements that will allow me to do this? I've found plenty of information on comparing headings, calculating means, etc, but nothing on how exposure might be used as a continuous independent variable. Treating exposure as a categorical variable (East, Southwest, etc) seems like a fallback option, but then there is just as much of a 'difference' between SE and E sites as there is between SE and NW sites! Thanks, Pete -- Message: 2 Date: Tue, 15 Oct 2013 17:10:43 + From: "Holland, Jeffrey D" To: "R-sig-ecology@r-project.org" Subject: Re: [R-sig-eco] angular statistics Message-ID: <30a9cce0a986f74c837d6f87f9c581861367e...@wpvexcmbx01.purdue.lcl> Content-Type: text/plain; charset="us-ascii" Hello Pete, You could include the sine and cosine of the angles. A good book on this kind of analysis: Fisher, N.I. 1993. Statistical Analysis of Circular Data. Cambridge Univ. Press. To make this closer to exposure, perhaps you could first "rotate" the compass so that 360' is facing the direction of maximum exposure, and back-transform later? Just a thought. Cheers, Jeff Jeffrey D. Holland (765) 494-7739 Assoc. Prof. of Landscape Ecology & Biodiversityjdhollan #at# purdue.edu Dept. of Entomology, Purdue University Smith Hall B17, 901 W. State St., West Lafayette, IN 47907 -Original Message- From: r-sig-ecology-boun...@r-project.org [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of Peter Nelson Sent: Tuesday, October 15, 2013 1:00 PM To: r-sig-ecology@r-project.org Subject: [R-sig-eco] angular statistics I want to include the exposure (measured in degrees, for example, East-facing is 90) of various coastal sites in GLM and CCA analyses. Is there an appropriate transformation that I can apply to these measurements that will allow me to do this? I've found plenty of information on comparing headings, calculating means, etc, but nothing on how exposure might be used as a continuous independent variable. Treating exposure as a categorical variable (East, Southwest, etc) seems like a fallback option, but then there is just as much of a 'difference' between SE and E sites as there is between SE and NW sites! Thanks, Pete ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- Message: 3 Date: Tue, 15 Oct 2013 11:45:14 -0700 From: Don McKenzie To: Peter Nelson Cc: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] angular statistics Message-ID: Content-Type: text/plain There is precedent in the ecological literature for using a cosine transformation IF you have reason to believe that your predictor varies continuously and symmetrically in its effects around a circle. For example, if due east were the "most" exposure, and due west the least, with due north and south being roughly equal, you could create a new predictor called "east.exposure" with (most basically) east.exposure = cos(exposure - PI/2) Many more complicated extensions
[R-sig-eco] graphics window fails to contain all points with ordicluster
To any who looked at this dumb question, I found the problem by looking at the hclust output. Two symbols were exactly superimposed in ordicluster. Thanks Mike I up-graded to R version 2.15.0. Now script that I had run successfully in version 2.12 cannot complete an identify-ordicluster in this version. My vegetation transect data is sub-setted to treat different life forms separately. A call for all the vegetation graphs successfully, with all 22 rows (points) shown, as does one of the sub-sets, but the others only display 20 points, so that the identify function cannot be completed. ACMA3 AMAL2 ARRI2 ARTR2 ARTR4 GRSPCHVI8 CRDO2 ERBL2 ERNA10 HODIKRLA2 MARE11 NEST5 PESIPIPOPOTR5 PREMPRVI PUTR2 RICEROWOSADOC SYAL2 SYOR2 WOODS SH ACHNA ACTH7 AGCRAGROS2 BROMU ELEL5 ELYMU FEIDFESTU LECI4 LUSP4 PG PHPR3 POA POBUPOCU3 POPRPOSEPSSP6 SCIRP HESPE11 BRHO2 BRTECARI2 VUOCVULPI AG ACMI2 AGGRALAC4 ALLIU ALSC2 ANDI2 ANFL2 ANMI3 ANST2 ANTEN AQFOARCUARHO2 ARSPARFR ARLUASSPASLE5 ASLYASPU9 ASRE6 ASTRA BACA3 BAHOBASA3 CAMA5 CADRCATH4 CHDOCIAR4 CLLI2 COUMCRAC2 CRATCRBA3 CRMO4 CREPI CRMUDENU2 DOPUERCA14 ERFI2 ERLIERPO2 ERPU2 ERDOEREL5 ERHE2 ERIGE2 ERIOG ERSP7 ERST4 ERTH4 ERLA6 FRPU2 HADI2 HEPU6 HYCA4 IRMILERE7 LIGL2 LIPA5 LITHO2 LIRU4 LOCA4 LODILOGE2 LOGOLOGRLOMA3 LOMAT LOQU2 LOTR2 LUAR6 LULE2 LULE3 LUPIN LUSU5 MAAQ2 MALVA MAVUMELO4 MOODNOTR2 ONAC ORFAPEGAPEGL4 PENST PERIPESPPHCHPHHAPHHOPHLO2 PHSPPOGL9 RAGLRUSASAIN4 SCLISEIN2 SENEC SIMESTLA7 STMI13 STTE2 TAHOTRGR7 TRMA3 VINU2 VITR3 ZIPA2 ZIVEPF AGHE2 AMLYAMMEAMSIN CAAN14 CAMI2 CIRSI CLPECLRU2 COPA3 COGR4 COLI2 CRYPT CRPTDEPIDESCU DRVE2 EPBR3 EPILO EPMI GAAP2 IDSCMAEXMAGR3 MEAL6 MIGRMIMUL MOFOMOLI4 OEAN3 PHLIPLSC2 PLMA4 POMIAF ARCTI CANU4 CARDU CEDI3 CETE5 CHTE2 CIINCONVO DESO2 ERCI6 HOUMLASEMYST2 SAKASIAL2 SONCH TAOFTARAX TRDUVERBA VETHFIXER Q05-09 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 Q06-09 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
[R-sig-eco] graphics window fails to contain all points with ordicluster
I up-graded to R version 2.15.0. Now script that I had run successfully in version 2.12 cannot complete an identify-ordicluster in this version. My vegetation transect data is sub-setted to treat different life forms separately. A call for all the vegetation graphs successfully, with all 22 rows (points) shown, as does one of the sub-sets, but the others only display 20 points, so that the identify function cannot be completed. Here is my script, with one subset (Perennial.Forb) that plots successfully, and another (shrub) that does not. My dataset (richness data) is attached. Q08.Q09 <-as.data.frame(read.table(file.choose(),header=T)) library(labdsv) library(vegan) Shrub <-Q08.Q09[c(1:11,22:32),1:27] Perennial.Forb <-Q08.Q09[c(1:11,22:32),55:169] All.Life.Forms <- Q08.Q09[c(1:11,22:32),] Perennial.Forb.min<-vegtab(Perennial.Forb, min=2) Perennial.Forb.std<-decostand(Perennial.Forb.min, method="max") Perennial.Forb.dist<- dist(Perennial.Forb.std) NMDS.Perennial.Forb <-metaMDS(Perennial.Forb) Perennial.Forb.ward<-hclust(Perennial.Forb.dist,method="ward",members=NULL) plot(Perennial.Forb.ward) plot(NMDS.Perennial.Forb, "sites", main="Ordicluster overlaid on NMDS of Q 08-09 Perennial Forb") identify(plot( NMDS.Perennial.Forb,"sites"), "sites") ordicluster(NMDS.Perennial.Forb, Perennial.Forb.ward) text(0.5,-.6,labels="Q 08-09, richness, Perennial Forb") shrub.min<-vegtab(Shrub, min=2) shrub.std<-decostand(shrub.min, method="max") shrub.dist<- dist(shrub.std) NMDS.shrub <-metaMDS(Shrub ) shrub.ward<-hclust(shrub.dist,method="ward", members=NULL) plot(shrub.ward) plot(NMDS.shrub, "sites", main="Ordicluster overlaid on NMDS of Q 08-09 Shrub") identify(plot( NMDS.shrub,"sites"), "sites") ordicluster(NMDS.shrub , shrub .ward) text(-0.9,-.9, labels="Q 08-09,richness, Shrub") ACMA3 AMAL2 ARRI2 ARTR2 ARTR4 GRSPCHVI8 CRDO2 ERBL2 ERNA10 HODIKRLA2 MARE11 NEST5 PESIPIPOPOTR5 PREMPRVI PUTR2 RICEROWOSADOC SYAL2 SYOR2 WOODS SH ACHNA ACTH7 AGCRAGROS2 BROMU ELEL5 ELYMU FEIDFESTU LECI4 LUSP4 PG PHPR3 POA POBUPOCU3 POPRPOSEPSSP6 SCIRP HESPE11 BRHO2 BRTECARI2 VUOCVULPI AG ACMI2 AGGRALAC4 ALLIU ALSC2 ANDI2 ANFL2 ANMI3 ANST2 ANTEN AQFOARCUARHO2 ARSPARFR ARLUASSPASLE5 ASLYASPU9 ASRE6 ASTRA BACA3 BAHOBASA3 CAMA5 CADRCATH4 CHDOCIAR4 CLLI2 COUMCRAC2 CRATCRBA3 CRMO4 CREPI CRMUDENU2 DOPUERCA14 ERFI2 ERLIERPO2 ERPU2 ERDOEREL5 ERHE2 ERIGE2 ERIOG ERSP7 ERST4 ERTH4 ERLA6 FRPU2 HADI2 HEPU6 HYCA4 IRMILERE7 LIGL2 LIPA5 LITHO2 LIRU4 LOCA4 LODILOGE2 LOGOLOGRLOMA3 LOMAT LOQU2 LOTR2 LUAR6 LULE2 LULE3 LUPIN LUSU5 MAAQ2 MALVA MAVUMELO4 MOODNOTR2 ONAC ORFAPEGAPEGL4 PENST PERIPESPPHCHPHHAPHHOPHLO2 PHSPPOGL9 RAGLRUSASAIN4 SCLISEIN2 SENEC SIMESTLA7 STMI13 STTE2 TAHOTRGR7 TRMA3 VINU2 VITR3 ZIPA2 ZIVEPF AGHE2 AMLYAMMEAMSIN CAAN14 CAMI2 CIRSI CLPECLRU2 COPA3 COGR4 COLI2 CRYPT CRPTDEPIDESCU DRVE2 EPBR3 EPILO EPMI GAAP2 IDSCMAEXMAGR3 MEAL6 MIGRMIMUL MOFOMOLI4 OEAN3 PHLIPLSC2 PLMA4 POMIAF ARCTI CANU4 CARDU CEDI3 CETE5 CHTE2 CIINCONVO DESO2 ERCI6 HOUMLASEMYST2 SAKASIAL2 SONCH TAOFTARAX TRDUVERBA VETHFIXER Q05-09 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
