search for: autotransform

Hello everyone! metaMDS(cm, distance = "euclidean", k = 2, trymax = 50, autotransform =TRUE, trace = 1, plot = T) (cm is a similarity matrix, in which values are positive integers or 0) I use this command to run NMDS on my matrix "cm". But the stress is very high after analysis. About 14. Actually, there is no improvment comparing with using isoMDS. cd<-dist(cm...

...Sometimes dramatically, selecting variables that the first time were not significant. I do not get what could 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", "altitu...

am at my wit's end. I am not sure what is wrong with this data matrix. It is sparse because it is a matrix of species, but I have looked at the row totals and column totals and they are positive. rmetaMDS(x.d) Error in if (autotransform && xam > 50) { : missing value where TRUE/FALSE needed What is wrong? And in the future how in God's name do I easily diagnose whatever problem there is with this data. I am in debt to anyone who figures this one out. Thanks Stephen -------------- next part -------------- An embed...

...erations for metaMDS, then when I plot the result, does it give me result from best solution? How do I know that. Can you plot the Stress by Iteration number? parth.mds <- metaMDS(WorldPRSenv, distance = "bray", k = 2, trymax = 100, engine = c("monoMDS", "isoMDS"), autotransform =TRUE, wascores = TRUE, expand = TRUE, trace = 2) plot(parth.mds, type = "p") Thanks in advance, Kumar -- Section of Integrative Biology University of Texas at Austin Austin, Texas 78712, USA [[alternative HTML version deleted]]

.... The problem is that, when trying this methods, I immediately get problems due to the identity between several of mycases or the presence of NA. Typical error messages are *"Error in isoMDS(DistB, k = 3) : zero or negative distance between objects 1 and 2"* or *"Error in if (any(autotransform, noshare > 0, wascores) && any(comm < 0)) { : missing value where TRUE/FALSE needed* *In addition: Warning message:* *In Ops.factor(left, right) : < not meaningful for factor"* Do you think Principal coordinate analysis on a binary distance matrix is a decent strategy? Thank...

...DS plot and I would like to suppress the labels for the loading vectors. Is this possible? Alternatively, how can I avoid overlap? Many thanks for the help. Example code: #perform NMDS using metaMDS() function spe.nmds<-metaMDS(data, distance='bray',k=2 , engine = "isoMDS", autotransform=F, trymax=1000) #calculate the loading (i.e., variable weights) on each NMDS axis vec.sp<-envfit(spe.nmds$points, data, perm=1000, choices=c(1:2)) #plot data in ordination ordiplot(spe.nmds, choices=c(1:2), type='text', display="sites", xlab="Axis 1", ylab="Ax...

...educed version of the dataset (randomly select 1000 of the 25071 output values). I would like to look at similarities/dissimilarities between the 12 model runs. When I use metaMDS on the full dataset, I get a warning message: Warning message: In metaMDS(MDSdata, distance = "bray", k = 2, autotransform = FALSE) : Stress is (nearly) zero - you may have insufficient data I don't think I have insufficient data, with 12 x 25071 data points, and when I reduce the dataset to only 1000 values per model run (so only 12 x 1000) I don't get this warning (though the final stress is now only just...

...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 ecological attributes. I have tr...

...d log likelihood. The relevant part of my R script is: ######################### BEGIN MDS ANALYSIS ######################### #load data data <- read.table("Ecoli33_Barry.dis", header = TRUE, row.names = 1) #perform MDS Scaling mds <- metaMDS(data, k = Dimensions, trymax = 20, autotransform =TRUE, noshare = 0.1, wascores = TRUE, expand = TRUE, trace = FALSE, plot = FALSE, old.wa = FALSE) ######################### BEGIN EM ANALYSIS ######################### #Use the points determined by MDS to perform EM clustering. #Allow only the unconstrained models. Sometimes, constrained models...

...as? #NMDS step1<-read.delim2("day20.txt", row.names=1) library(clusterSim) step2<-data.Normalization(step1,type="n10") step3<-asin(sqrt(step2))*57.3 step4<-t(step3) library(vegan) step5<-data.matrix(vegdist(step4,method="bray")) step6<-metaMDS(step5, autotransform=FALSE) plot(step5) Warning message: In ordiplot(x, choices = choices, type = type, display = display, : Species scores not available #PerMANOVA step1<-read.delim2("day20.txt", row.names=1) library(clusterSim) step2<-data.Normalization(step1,type="n10") step3<-asin(...

I am at my wit's end. I am not sure what is wrong with this data matrix. It is sparse because it is a matrix of species, but I have looked at the row totals and column totals and they are positive. metaMDS(x.d) Error in if (autotransform && xam > 50) { : missing value where TRUE/FALSE needed What is wrong? And in the future how in God's name do I easily diagnose whatever problem there is with this data. I am in debt to anyone who figures this one out. Thanks Stephen -- Let's not spend our time and resources...

metaMDS(cm, distance = "euclidean", k = 2, trymax = 50, autotransform =TRUE, trace = 1, plot = T) (cm is a similarity matrix, in which values are positive integers or 0) I use this command to run NMDS on my matrix "cm". But the stress is very high after analysis. About 14. Actually, there is no improvment comparing with using isoMDS. cd<-dist(c...

...ST9_MF..CC.3 1 0 0 1 ST9_MF..CC.4 1 0 0 1 FX9_MF..CH.1 1 1 0 0 FX9_MF..CH.2 1 0 0 0 This is the code I have thus far: datum <- read.csv(q, header=F) datum2 <- datum[,2:ncol(datum)] rownames(datum2) <- datum[,1] fit <- metaMDS(datum2, distance = "jaccard", k = 3, trymax=20, autotransform=F) fit. <- data.frame(sample=rownames(datum2), fit$points) p <- qplot(X1,X2, col=sample, data=fit.) print(p) dev.off() Thanks for your help! -- View this message in context: http://r.789695.n4.nabble.com/Manipulating-rownames-for-nMDS-tp3542663p3542663.html Sent from the R help mailing...

...10.61520 ... New best solution ... procrustes: rmse 0.005646588 max resid 0.009332332 Run 4 stress 10.61520 ... New best solution ... procrustes: rmse 0.0001009763 max resid 0.0001754211 *** Solution reached > mdsg.mds.ALT<- metaMDS(mdsg, distance="euclidean", k=3, trymax=50, autotransform=FALSE) Run 0 stress 3.020817 Run 1 stress 0.00673153 ... New best solution ... procrustes: rmse 0.09349157 max resid 0.2443702 Run 2 stress 0.008861757 ... procrustes: rmse 0.08275172 max resid 0.1932439 Run 3 stress 0.006908548 ... procrustes: rmse 0.0447681 max resid 0.1093436 Run 4 str...

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data