search for: zerodist

Hi all, I got the >"chfactor.c", line 130: singular matrix in function LDLfactor() Error in predict.gstat(g, newdata = newdata, block = block, nsim = nsim, : LDLfactor< Error, probably because there are some point pairs with zero distance in the matrix. My question is, how can i delete these duplicate nodes from the data set? Thanks, m [[alternative

...000000 1.000000000 1.000000000 NaN 1.000000000 1.000000000 NaN :::: D3R1A NaN 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 NaN 1.000000000 1.000000000 NaN I searched all the details of metaMDS where it is suggested to avail the argument 'zerodist' So I tried both X.dist1 <- metaMDSdist(X, method="bray",zerodist = "ignore") X.dist2 <- metaMDSdist(X, method="bray",zerodist = "add") But for both the cases I still have those NaN values in some distances. I have read, one of the discussion says...

...-I/usr/share/R/include -fpic -g -O2 -c pip2.c -o pip2.o gcc -std=gnu99 -I/usr/share/R/include -fpic -g -O2 -c sp_xports.c -o sp_xports.o gcc -std=gnu99 -I/usr/share/R/include -fpic -g -O2 -c surfaceArea.c -o surfaceArea.o gcc -std=gnu99 -I/usr/share/R/include -fpic -g -O2 -c zerodist.c -o zerodist.o gcc -std=gnu99 -shared -o sp.so Rcentroid.o gcdist.o init.o pip.o pip2.o sp_xports.o surfaceArea.o zerodist.o -L/usr/lib64/R/lib -lR installing to /home/ssefick/R/x86_64-pc-linux-gnu-library/2.11/sp/libs ** R ** data ** demo ** inst ** preparing package for lazy loading Error: packa...

...pip2.o gcc -std=gnu99 -I/usr/share/R/include -DNDEBUG -fpic -O3 -pipe -g -c sp_xports.c -o sp_xports.o gcc -std=gnu99 -I/usr/share/R/include -DNDEBUG -fpic -O3 -pipe -g -c surfaceArea.c -o surfaceArea.o gcc -std=gnu99 -I/usr/share/R/include -DNDEBUG -fpic -O3 -pipe -g -c zerodist.c -o zerodist.o gcc -std=gnu99 -shared -o sp.so Rcentroid.o gcdist.o init.o pip.o pip2.o sp_xports.o surfaceArea.o zerodist.o -L/usr/lib/R/lib -lR installing to /home/terjej/R/x86_64-pc-linux-gnu-library/2.15/sp/libs ** R ** data ** demo ** inst ** preparing package for lazy loading Error in setCl...

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) Presently I try to run the function metaMDS (vegan) using an existing dissimilarity-matrix. As I would like to start with this matrix I thought I could just give the matrix using the x= -argument Test<-metaMDS(x=Dist.Gower) Fehler in inherits(comm, "dist") :

Hi, I am stuck in one problem when doing nonmetric multidimensional scaling. I use the function 'metaMDS' in the package 'vegan' to work on the presence/absence community data. The problem is when two samples are identical (dissimilarity = 0), metaMDS cannot work with zero dissimilarity. I don't want to delete the duplicates as they are true samples from different locations.

...d be the problem or if is normal given the permutations are different. # the NMDS ordination gap_flor_NMDS_chord <- metaMDS(gaps_flor, distance = "euclid", k = 2, trymax = 20, autotransform =TRUE, noshare = 0.1, wascores = TRUE, expand = TRUE, trace = 1, plot = FALSE, old.wa = FALSE, zerodist = "add") # the environmental variables to use with enfit explain1<- site[c("Tipo", "Gap.size", "Gap diameter (m)", "Size.cat", "altitud", "Slope (%)", "Exp.deg", "form","formcont", "largo...

...for stress levels in the different dimensions). I then used envfit to correlate a predictor dataset of environmental variables with the NMDS results, using the following code. >Fish<-as.data.frame(read.csv("Fish.csv",header=TRUE, sep = ",")) >Fish.mds<-metaMDS(Fish,zerodist = "add",k=3,trymax=20) >Predictors<-as.data.frame(read.csv("Predictors.csv",header=TRUE, sep = ",")) >Fish.fit <- envfit(Fish.mds$points, Predictors, k=3, 1000, na.rm = TRUE) >Fish.fit The output of Fish.fit was as follows (table truncated):...

...been using R for a while now but I've got a problem with metaMDS (in the vegan package) that I can't quite figure out. I have a set of proportion data (from 0-1, rows sum to 1) that I apply metaMDS to using the command: nMDS.set=metaMDS(sqrt(test.set),distance="euclidean",k=3,zerodist="add",autotransform=FALSE) I am using a squared-chord distance metric here. It gives me a great ordination that appears to represent my ecological gradients well. What I want to do is passively add new points to the ordination in an effort to "predict" visually certain eco...

...ecies presence/absence between a pair of island? Should the two islands be specifically located or close to each other?) Thank you Elaine Code # NMDS library(MASS) library(vegan) island.NMDS <- metaMDS(island,k=2, distfun = betadiver, distance = "sim",trymax=100,zerodist="add") plot(island.NMDS, type = "n") #Simpson Dissimilarity Index library(betapart) island.dist<-beta.pair(island, index.family="sor") class(island.dist) [[alternative HTML version deleted]]

Hi I have just (finally) started to poke around in R and wanted to analyse a stream fish dataset with 28 sites and 18 species. When trying to follow the Vegan manual to run nmds from distance measures calculated by the vegdist function it turns out that I have two sites (streams) with the exactly the same four species (I have used pres-abs data in this case). When I try to run isoMDS I get an

Dear R-helpers, I know how to use unique to select unique rows, e.g. unique.rows<-unique(dataframe) but I would like to select those rows that are unique only only TWO of my dataframe's columns (so, two rows with the same value on these two columns would not be kept, even if they had different values in other columns). For example, I have a dataframe with 10 columns, two of which are

...their own. FWIW, here is an example of my code for plotting convex hulls around 3 different feeding groups, which works. comm.mds is the ordination of the community matrix comm.mat, FD.mat is the species attributes matrix. comm.mds<- metaMDS(comm.mat, distance = "euclidean", k = 2, zerodist = "add", noshare = 0) plot(comm.mds, type="p", display="species") with(FD.mat, ordihull(comm.mds.t, feeding, show.groups = "1", col = "yellow", lty = 1, lwd=3)) with(FD.mat, ordihull(comm.mds.t, feeding, show.groups = "2", col =...

Hi, I am currently working on some data and feel that NMDS would return an excellent result. With my current data set however I have been experiencing some problems and cannot carry out metaMDS. I have tried with a few smaller data sets which I created for practice sake and this has worked fine. I think it is the set up of my data set that is causing me trouble. I have 18 columns and 18 rows,

Hi all, >str(df) `data.frame': 31837 obs. of 3 variables: $ x : num 410683 410700 410720 410740 410324 ... $ y : num 43136 43126 43123 43125 42709 ... $ wz: num -101.1 -94.9 -93.3 -94.5 30.8 ... >library(gstat) >g<-gstat(id="rv",form=wz~1,loc=~x+y,data=df,model=mat,nmax=500,set=list(average=1)) >str(g) List of 3 $ data :List of 1 ..$ rv:List of 10 ..