similar to: Help: Strange MDS behavior

Hi all, I'm trying to perform an MDS of some data that I have. When I use cmdscale everything is fine and I get some interesting results however, the tends to be low. What I wnat to do is compare this with the Non-Metric MDS using isoMDS or sammon. However, when I try using these I get the following message. Error in isoMDS(x.dist) : zero or negative distance between objects 2 and 4

Hello All, I'm having some trouble figuring out what the clearest way to plot my k-means clustering result on an my existing MDS. First I performed MDS on my distance matrix (note: I performed k-means on the MDS coordinates because applying a euclidean distance measure to my raw data would have been inappropriate) canto.MDS<-cmdscale(canto) I then figured out what would be my optimum

Hi, I want to know whether there is any function in R to find STRESS after using cmdscale and estimating the coordinates, I have written these functions to find stress (for p =1,2,3,4,5) stress<-rep(0,5) for(p in 1:5) { datahat<-cmdscale(d,p) deltahat<-as.matrix(dist(datahat)) a<-0 b<-0 for(i in 1:n) { for(j in 1:n) { a<-d[i,j]^2+a b<-(d[i,j]-deltahat[i,j])^2+b } }

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 am a new R user. i have a question about Mahalanobis distance.actually i have 300 rows and 7 columns. columns are different measurements, 300 rows are genes. since genes can classify into 4 categories. i used dist() with euclidean distance and cmdscale to do MDS plot. but find out Mahalanobis distance may be better. how do i use Mahalanobis() to generate similar dist object which i can use

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

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

What are the limitations on size of matrix for MDS functions? steve -- Steve Antos Priva-Technologies 847-640-9020 x3114 cell (540)409-1231 [[alternative HTML version deleted]]

Dear all, My data includes almost three thousand people who rank ten categories into three variables. The simple example below is almost same except I have many missing values. x <- cbind( sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) , sample( LETTERS[1:10] , 3000 , replace = TRUE ) ) I try to figure

i know cmdscale and isoMDS inR can do classical and non-metric MDS.but i want to konw if there is packages can carry on "individual differences scaling" and "multidimensional analysis og preference"?both method are important one,but i can not find any clue on how to do it using R. anyone can help? thank you!

Hello I am using the following command but not able to text the values on the graph can someone please make suggestions for improvement #here is the command loc_mds <- cmdscale(dist.r, k = 7, eig = TRUE) loc_mds$eig sum(abs(loc_mds$eig[1:2]))/sum(abs(loc_mds$eig)) sum((loc_mds$eig[1:2])^2)/sum((loc_mds$eig)^2) x <-loc_mds$points[,1] y <-loc_mds$points[,2] plot(x, y,

?for doesn't return anything help.search("for") doesn't return anything- Is the for loop so prevelant in computer programing that the documentation is implicit or is R paradigm to discourage the use of the for loop. I will post data probably tonight, but here is my problem. I have preformed an MDS on a set of data. I have the scores of the four axes that are the optimal

Dear Sirs and madams :-) I am trying to teach myself multidimensional scaling. To that effect I have collected a survey asking people to rank 10 philosophers and politicians according to their preference. I have collected 61 answers. The data is organized in ten columns and 61 rows. the columns are "choice_1", "choice_2", "choice_3" etc. The cells is the name of the

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 am trying to create 3 mds plots: one with axis 1 vs axis 2, one with axis 2 vs axis 3, and one with axis 1 vs axis 3. When inputting my code, I only end up with one diagram and when inputting mdsg.mds$dims, the program returns 2 for 2 dimensions. How can I create the other two plots? Any help would be greatly appreciated, Calla Carbone The table I use is number of each taxa on each

Hi, this is a second try to post this to the R-help mailing list. The first one has been rejected because of a too large attachment. Now I ask this without attaching the data. If you want to reproduce the results, please contact me directly to get the data. (First mail, rejected:) > Attached there is a 149*149 dissimilarity matrix; it is a file obtained by >

Sorry to bother you kind folks again with my questions. I am trying to learn as much as I can about all this, and I will admit that I don't have the proper background, but I hope that someone can at least point me in the correct direction. I have created a test matrix for what I want to do: s1 s2 s3 s4 s5 s1 10 5 0 8 7 s2 5 10 0 0 5 s3 0 0 10 0 0 s4 8 0 0 10 0 s5 7

The following problem occurs in R 1.4.0 and 1.3.1 for Windows95, but not in R 1.2.0 for Windows95. The problem does not occur in R 1.4.0 for Linux PC, Linux Alpha and HP-UX. Sometimes, the type of 'Size' of an object of type 'dist' changes from integer into double. Running cmdscale on such a 'dist' object gives invalid results. I don't know what should be considered

Hi, I have a set of nodes and a dissimilarity matrix for them, as well as a csv file in which the diss matrix has been converted to [node_1, node_2, dissimilarity] format. I would like to visualize this as a graph in Euclidean space (that is, similar nodes clumped together in clusters), rather than the seriation visualization given by dissplot(). I am using Network WorkBench for my

I'm having trouble with the ordisurf function in the vegan package. I have created an ordination plot (cmdscale) of 60 samples based on Bray-Curtis dissimilarities, and would like to overlay various soil edaphic characteristics as possible clues to the clustering I observe in my plot. However, I find that ordisurf creates a surface on the plot that is a perfect, even gradient - and