similar to: Non-metric Multidimensional Scaling in R (Tobias Verbeke)

Friends, I have noticed that many publications that use RF report variable importance as a function of mean decrease in accuracy rather than mean decrease in gini. Am I correct that the mean decrease in accuracy is just the mean decrease in gini divided by 100? Thanks, Helen Mills Poulos Yale School of Forestry

All, What purpose does the getTree function have in Random Forest? Can you graph it as you can in rpart and can it be interpreted in the same way? Helen Mills Poulos Yale School of Forestry

Hello all, I am currently working with rpart to classify vegetation types by spectral characteristics, and am comming up with poor classifications based on the fact that I have some vegetation types that have only 15 observations, while others have over 100. I have attempted to supply prior weights to the dataset, though this does not improve the classification greatly. Could anyone supply some

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,

Hello everyone, I'm working with R 2.4.1 on a PC running with XP. Trying to run isoMDS as follows: Gquad.mat <- Gquads[4:10] # extracts only the metric data variables Gquad.dist <- dist(Gquad.mat) Gquad.mds <- isoMDS(Gquad.dist) plot(Gquad.mds$points, type = "n") text(Gquad.mds$points, labels = as.character(Gquads$Quadname), cex = .5) The data.frame

Hello, I would like to perform an NMDS on the following: I have two independent variables, which are sites and treatments. I have 6 sites which are peatlands. I collected 5 replicates (at the same time) from each of the sites. I used each of the replicates in a treatment. There were 4 treatments. 2 were controls and the other two were a simulated 30 day drought and a simulated 60 day drought.

A student of mine wants to use R to do some nonmetric multidimensional scaling. According to the R FAQ, there's a package called pcurve that computes multidimensional scaling solutions, but I was not able to locate it the contrib page (I am a Windows user with R version 1.4.1). Can anyone tell me whether it is possible to do nonmetric multidimensional scaling with R, and if so, how? John

Dear R list Would anyone be able to tell me whether it is possible to do "enhanced multidimensional scaling" (enhanced MDS) in R? In other words, something that goes beyond "cmdscale" by iteratively improving the fit between observed dissimilarities and inter-object distances, using the KYST algorithm (Kruskal, 1964). I have found several implementations of non-metric MDS

Hi Does anyone know if there's a package I can use to do Multidimensional scaling ? Thank's EJ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

I have a dissimilarity dataset with the form: 1 1 dissimilarity value 1 2 ... 1 3 1 4 2 2 2 3 2 4 ... I would like to do nonmetric multidimensional scaling with this data, but I am having trouble using this format. I would like to either find a function that accepts this format or find a way to easily convert this format to a matrix for use with existing functions. Thanks!

Hi all, Does anyone know if there's R code available for doing Hybrid Multidimensional Scaling (or Semi-Strong HMDS, e.g. Belbin 1991 J. Veg. Sci. 2:491-)? I've found only commercial software that does it. Thanks, Anni

I sent a query to R-Help about the availability of nonmetric multidimensional scaling (MDS) algorithms in R. I would like to thank Tony Rossini, Jonathan Baron, Sundar Dorai-Raj, and Brian Ripley for helpful replies. The gist of the replies is that isoMDS in the MASS library provides Kruskal's method for nonmetric MDS, sammon in the MASS library provides Sammon's nonlinear mapping method

Dear UserR's, The smacof package (see also our PsychoR repository on http://r-forge.r-project.org/projects/psychor/) is uploaded on CRAN. This package provides the following approaches of multidimensional scaling (MDS) based on stress minimization by means of majorization (smacof): - Simple smacof on symmetric dissimilarity matrices - smacof for rectangular matrices (unfolding models) -

Dear R-list members, I need an help with the Multidimensional Scaling analysis (MDS). So far I used the cmdscale() command in R, but I did not get the perceptual map I would love to see, and I would like to know if it is possible to get it using R, and if yes how. I also had a look to the functions isoMDS() and sammoc() but with no luck. I summarize the experiment I performed, and I would ask you

Hi there, I'm trying to do a regression experiment on a multidimensional dataset where both x and y in the model are multidimensional vectors. I'm using R version 2.9.2, updated packages, on a Linux box. I've tried gausspr(), ksvm() and rvm(), and the models are computed fine, but I'm always getting the same error message when I try to use predict(): "Error in

Hello, Your way is much better than to mess with the dim attribute, like I did. But, "If you can create a data.frame or matrix that has the indices" Actually, it must be a matrix, indices can't be of type list. A way to avoid loops/apply altogether, and much faster, is the one creating K3 (K is the result from the op.) n <- 20 t2 <- system.time({ K2 <-

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit

Hi, anyone knows about any functions in R can get multidimensional integration not over a multidimensional rectangle (not adapt). For example, I tried the following function f(x,n)=x^n/n! phi.fun<-function(x,n) { if (n==1) { x }else{ integrate(phi.fun, lower=0, upper=x, n=n-1)$value } } I could get f(4,2)=4^2/2!=8, but failed in f(4,3)=4^3/3! Thanks Best, Lynette

Hello, I apologize for eventual double-posting. I am trying to integrate a 2-dimensional function that already calls the function adapt. More precisely, I am calling adapt(2,lower=c(-100,-100),upper=c(100,100),functn=function(s){1-exp(-50*Unc enteredGauss(c(-10,10,-10,10),60,s)}) where UncenteredGauss is given by the following code in R: