similar to: Multidimensional scaling

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

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

Dear Help, Suppose I have a character vector. x <- c("Bob", "loves", "Sally") I want to combine it into a single string: "Bob loves Sally" . paste(x) yields: paste(x) [1] "Bob" "loves" "Sally" The following function combines the character vector into a string in the way that I want, but it seems somewhat inelegant.

Dear All, I am in the somewhat unfortunate position of having to reproduce the results previously obtained from (non-metric?) MDS on a "kinship" matrix using Statistica. A kinship matrix measures affinity between groups, and has its maximum values on the diagonal. Apparently, starting with a nxn kinship matrix, all it was needed to do was to feed it to Statistica flagging that the

Hi, I just encountered what I thought was strange behavior in MDS. However, it turned out that the mistake was mine. The lesson learned from my mistake is that one should plot on a square pane when plotting results of an MDS. Not doing so can be very misleading. Follow the example of an equilateral triangle below to see what I mean. I hope this helps others to avoid this kind of headache.

I have run into a general problem with subsetting of which the following is a simple example. Suppose that x <- c(5, NA, 7, 5, NA, 3) y <- c(1, 2, 3, 4, 5, 6) I want to extract the values of y for which x = 5, but y[x==5] yields > y[x==5] [1] 1 NA 4 NA I find that y[!is.na(x) & x==5] yields the desired result: > y[!is.na(x) & x==5] [1] 1 4 but I am wondering whether

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

Dear R Help, I am trying to create a boolean vector that is TRUE whenever a particular value occurs in a numeric vector, and FALSE otherwise. For example, suppose that > y <- c(5, 2, 4, 3, 1) > y [1] 5 2 4 3 1 and suppose that I want to find where 3 occurs in y. Then, the following yields the solution: > y == 3 [1] FALSE FALSE FALSE TRUE FALSE My problem arises when the

Dear R Users, I want to write a function that applies to the dataframe and variables that were used in a previous call to lm or aov. In order to do this, I need to write a function that applies to the output of lm or aov, and yields the names of the dataframe and variables that were used in the lm or aov analysis. For example, suppose that I give the command: aov.out <- aov( Rt ~

Dear R-Help, Suppose I have a character matrix, e.g., (ch.mat <- matrix(c('a','s','*','f','w','*','k','*','*','f','i','o'), ncol=3)) When I convert 'ch.mat' to a dataframe, the columns are converted to factors: (d1 <- data.frame(ch.mat)) mode(d1[,1]) is.factor(d1[,1]) To prevent

Dear R-Help, I'm using R version 1.6.0 on a Windows computer. I am trying to create a function that, among other things, constructs strings that refer to Windows files, e.g., I might want to construct a string like 'c:\work\part1.txt'. I have found that the following does not work. > paste("c:", "\", "work", "\", "part1.txt",

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, I'm trying to do a non-metric multidimensional scaling using isoMDS. However, I have some '0' distances in my data, and I'm not sure how to deal with them. I'd rather not drop rows from the original data, as I am comparing several datasets (morphology and molecular data) for the same individuals, and it's interesting to see how much morphological variation can be

Dear R-users, I am learning to use R 1.3.1 on a Pentium running Windows '98. I'm puzzled that several statistical procedures, t.test and chisq.test, do not appear to be available on R version 1.3.1. For example, if I type "t.test", I get the reply, "Object "t.test" not found". Is there a package that I have to load in order to have access to these

Hello I will be applying MDS (actually Isomap) to make a psychological "concept map" of the similarities between N concepts. I would like to scale to a large number of concepts, however, the resulting N*(N-1) pairwise similarities is prohibitive for a user survey. I'm thinking of giving people random subsets of the pairwise similarities. Does anyone have recommendations for this

Hi, I am looking for the best multidimensional configuration for my data (47*47 distance matrix). I ve tried classical metric (cmdscale) and non metric MDS (isoMDS, nmds) but it is now difficult to choose the best solution because of the uncertainties in the definitions of the "stress" function. So, same problem, several questions : 1. Statistical consideration : With

Dear R-helpers, I am running metaMDS in the vegan package, which uses isoMDS in MASS, to perform Nonmetric Multidimentional Scaling (NMDS). I have seen some authors report a p-value for the NMDS ordination based on randomization of the dataset. As I understand it this is meant to compare the stress in your dataset to multiple runs of randomized data. I do not see a way to perform such a test in

Dear All, I am struggling with the following problem: I am given a NxN symmetric matrix P ( P[i,i]=0, i=1...N and P[i,j]>0 for i!=j) which stands for the relative distances of N points. I would like use it to get the coordinates of the N points in a 2D plane. Of course, the solution is not unique (given one solution, I can translate or rotate all the points by the same amount and generate

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

R-help, Is there any package/function in R which can perform multi-dimensional scaling? Thank you in advance