similar to: Problem with clusplot

Hello, I have a large data set that has more columns than rows (sample data below). I am trying to perform a partitioning cluster analysis and then plot that using pca. I have tried using CLUSPLOT(), but that only allows for 'princomp' where I need 'prcomp' as I do not want to reduce my columns. Is there a way to edit the CLUSPLOT() code to use 'prcomp', please? #

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

Hello, I have a large data matrix (68x13112), each row corresponding to one observation (patients) and each column corresponding to the variables (points within an NMR spectrum). I would like to carry out some kind of clustering on these data to see how many clusters are there. I have tried the function clara() from the package cluster. If I use the matrix as is, I can perform the clara

Dear people, I used clusplot to plot a partition result. The partition result was from pamk with a distance object as input. Then I applied cmdscale on the same distance object for coordinates to make another scatterplot. My problem is this: the coordinates from the cmdscale calculation, though with the same shape, were different in scale and rotation from the scatter plot yielded by clusplot.

Dear Friends, require(cluster) x <- rbind(cbind(rnorm(10, 0, 0.5), rnorm(10, 0, 0.5)), cbind(rnorm(15, 5, 0.5), rnorm(15, 5, 0.5))) plot(pp <- pam(x, 2), which.plots = 1) How can I extract the coordinates used in the plot? _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology Postal Address: P.O.Box 400400, Charlottesville, VA 22904-4400

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

The following code in clusplot.default (package cluster) is in error: x1 <- cmdscale(x, k = 2, eig = TRUE) var.dec <- sum(x1$eig)/sum(diag(x1$x)) if (var.dec < 0) var.dec <- 0 if (var.dec > 1) var.dec <- 1 x1 <- x1$points x1 has components with names "points" and "eig", not "x", so

Hi, I am puzzeled with a differing result of princomp in R and FACTOR in SPSS. Regarding the amount of explained Variance, the two results are the same. However, the loadings differ substantially, in the unrotated as well as in the rotated form. In both cases correlation matrices are analyzed. The sums of the squared components is one in both programs. Maybe there is an obvious reason, but I

I created a clusplot from PAM results. It represents how signals have been classified. Signals are identified by a numerical label. My trial distance matrix is made up of 10 rows, one for eacjh signal. I assigned the signals iidentifiers as rownames of the distance matrix. rwn [1] "1104" "1332" "2057" "2425" "2483" "2530"

kalish at psy.uwa.edu.au wrote: > > I'm a newbie at R, and can't get libraries to really work. > I did this: > > library(help = mva) > cancor Canonical Correlations > cmdscale Classical (Metric) Multidimensional Scaling > dist Distance Matrix Computation > hclust Hierarchical Clustering

I am a relative novice with R and am having some difficulty using 'clusplot' (package Cluster). I have performed PCA analysis (using vegan) on a large set of morphometric measurements and revealed up to 4 principal components. To examine the grouping of the data I have used PAM followed by clusplot to visualise the clusters. My problem is that I would like to see the clusters plotted on

Dear all, I've a correlation matrix with rows and columns headings. I've two questions: 1) How can i import it in R, setting first row as row heading and first column as column heading? 2) Which is the best principal component anlysis package in R? Thanks in advance -- View this message in context: http://r.789695.n4.nabble.com/Help-with-PCA-tp4239756p4239756.html Sent from the R help

Full_Name: Gavin Simpson Version: 2.5.0 OS: Linux (FC5) Submission from: (NULL) (128.40.33.76) Screeplots are a common plot-type used to interpret the results of various ordination methods and other techniques. A number of packages include ordination techniques not included in a standard R installation. screeplot() works for princomp and prcomp objects, but not for these other techniques as it

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

Dear R user, I am a biochemist/bioinformatician, at the moment working on protein clusterings by conformation similarity. I only started seriously working with R about a couple of months ago. I have been able so far to read my way through tutorials and set-up my hierarchical clusterings. My problem is that I cannot find a way to obtain information on the rooting of specific nodes, i.e. of

Hi there, I want to do classify some 2-dimensional points into four clusters by pam() in the cluster package. However, I encountered some problems. 1. How can I change the "xlab" and "ylab" instead of the default "Component 1" and "Component 2"? When I put "xlab" option in the function, it always says "formal argument "xlab"

Dear R-list members, I am using the cluster package and by the generation of plot.partition I ran into the problem that an alternative title overlaps the default title. > plot.partition(clara.14,which.plot=2,stand=TRUE, main="Silhouette plot of 14 clusters") The manual states that all optional arguments for clusplot.default may also be supplied to plot.partition(). Altering the

Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you

G'Day R users! Following an ordination using prcomp, I'd like to test which variables singnificantly contribute to a principal component. There is a method suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called "bootstrapped eigenvector". It was asked for that in this forum in January 2005 by J?r?me Lema?tre: "1) Resample 1000 times with replacement entire

I have a data set with 6 variables and 251 cases. The people who supplied me with this data set believe that it falls naturally into three groups, and have given me a rule for determining group number from these 6 variables. If I do scaled.stuff <- scale(stuff, TRUE, c(...the design ranges...)) stuff.dist <- dist(scaled.stuff) stuff.hc <- hclust(stuff.dist)