similar to: Summary: Multidimensional scaling

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 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, 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

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

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",

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 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

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Dear R-Help, I am using the studres function from the MASS package to compute studentized deleted residuals in a oneway anova. I'm having trouble interpreting the results in situations where a factor level has only one observation. Sometimes studres yields an NaN and sometimes it produces a numeric value for cases where a factor level has only one observation. I would think it should

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

Greetings, I have a question pertaining to the concept of "environment variables" that is mentioned in the R documentation for "Startup" and also in the discussion of the Windows configuration of R in the recent book "An Introduction to R" authored by Venables, Smith, and the R Development Core Team (referred to as VS in this message). The Startup documentation and

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!

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) -

Hi, Everyone, I want to calculate the principal curve of a points set. First I read the points'coordinate with function "scan", then converted it to matrix with the function "matrix", and fit the curve with function "principal.curve". Here is my data in the file "bmn007.data": 0.023603 -0.086540 -0.001533 0.024349 -0.083877 -0.001454 .. ..

Hey, R-listers I am a new user of R and just found the package of PCURVE which can estimate principal curve for arbitrary dimensional data set. Now I have some 2-Dimensional data set X, which is stored as an Nx2 matrix in data.txt file and looks as following: -1.5551 2.4183 1.0051 1.0102 0.90644 0.82163 1.3248 1.7551 -1.3626 1.8568 -1.3554 1.8371 0.039396 0.0015521 -0.99622 0.99245 0.2314

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

Dear members of R-mailing list, I am using Sammon's Non-Linear Mapping in R as implemented in MASS package. I have two distance matrices (attached with the mail). For the first (/5HT_MACCS_dist.dat/) /sammon()/ works fine but for the second (/5HT_TGT_dist.dat/), I get an error message "configuration has duplicates". I checked both the matrices for duplicate values using/

Hello all, I'm working on a project that uses a SOM to generate feature maps from image data. Each image is a 100x75 pixels and RGBA, so 30,000 elements per image. I wanted to observe the structure of the pixel by pixel euclidean distance (where each image is a point in 30,000 dimensional space) of my image data. Sammon's projection seems appropriate for this, though I'm a bit

I've ported F. Murtagh's statlib package "multiv" to R, it can be found in the usual place on CRAN. Contents: bea Bond Energy Algorithm ca Correspondence Analysis supplc Supplementary Columns in Correspondence Analysis supplr Supplementary Rows in Correspondence Analysis flou

I've ported F. Murtagh's statlib package "multiv" to R, it can be found in the usual place on CRAN. Contents: bea Bond Energy Algorithm ca Correspondence Analysis supplc Supplementary Columns in Correspondence Analysis supplr Supplementary Rows in Correspondence Analysis flou