similar to: principal component plots

Hi, I run the example for princomp for R211 I got the following error for biplot > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <http://pc.cr> <- princomp(USArrests)) # inappropriate Erreur dans cov.wt(z) : 'x' must contain finite values only > princomp(USArrests, cor = TRUE) # =^=

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

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 everyone, I am doing PCA with labdsv package. I was trying to create a biplot graphs in order to observe arrows related to my variables. However when I run the script for this graph, the console just keep saying: *Error in nrow(y) : element 1 is empty; the part of the args list of 'dim' being evaluated was: (x)* could please someone tell me what this means? what i am doing

Dear friends, I am beginning to use R software in my academic research and I'm having some problems regarding the use of PCA. I have a table with 24445 rows and 9 columns, and I used the function prcomp() to do the analysis. Working with an example?: x<-read.table("test.txt", header=T) row.names(x)<-x[,1] x<-x[,-1] require(stats) pca<-prcomp(x, scale=T) names(pca) ##

Hi , To my knowledge, there're two functions that can do principal component analysis, princomp and prcomp. I don't really know the difference; the only thing I know is that when the sample size < number of variable, only prcomp will work. Could someone tell me the difference or where I can find easy-to-read reference? To access the first PC using princomp:

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

As of R 1.7.0, princomp no longer accept matrices with more coloumns than rows. I'm curious: Why was this decision made? I work a lot with data where more coloumns than rows is more of a rule than an exception (for instance spectroscopic data). To me, princomp have two advantages above prcomp: 1) It has a predict method, and 2) it has a biplot method. A biplot method shouldn't be too

Hi, Can anyone explain me the differences in Q and R mode in Principal Component Analysis, as performed by prcomp and princom respectively. Regards L?vio Cipriano

Dear All, It might be a simple question. But I could not find the answer from function “prcomp” or “princomp”. Does anyone know what are the codes to get coefficient and scores of Principal component analysis in R? Your reply will be appreciated! Best Zunqiu [[alternative HTML version deleted]]

Dear R buddies, I’m trying to run Principal Component Analysis, package princomp: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html. My question is: why do I get different results with pca = princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I standardize variables in my matrix? Best regards, Blaž Simčič [[alternative HTML version deleted]]

Hi, I have been using the prcomp function to perform PCA on my example microarray data, (stored in metric text files) which looks like this: 1a 1b 1c 1d 1e 1f ...................................................4r 4s 4t g1 1.2705 1.2766 ...........................................................2.0298 g2 0.1631

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'm trying to make a screeplot including the Cumulative Proportion of the Variance, something that can easily be done in S-Plus with 'screeplot(pc.object,cumulative=T)'. How can I access the Proportion of Variance in an princomp object and how could I get the Cumulative Proportion of the Variance on the screeplot? Many thanks in advance, Jan:-) --

Hi everyone, I am relatively new to R, and I need to perform the principal components analysis of a data matrix. I know that there are a bunch of methods to do it (dudi.pca, princomp, prcomp...) but I have not managed to find a method that can return the residuals obtained by retaining X principal components of the original data, as this MATLAB function can do: http://is.gd/6WeUFF Suggestions?

Hi, I have a problem with making PCA plots that are readable. I would like to set different sympols instead of the numbers of my samples or their names, that I get plotted (xlabs). How is this possible? With points, i don´t seem to get the right data plotted onto the PCA plot, as I do not quite understand from where it is taken. I dont know how to plot the correct columns of the prcomp

Just installed R-1.7.0 (with recommended libraries) on RedHat 8.0. At R console, I can do > library (MASS) > just fine. However, if I put a line 'library(MASS)' into ~/.Rprofile, it fails to load, R : Copyright 2003, The R Development Core Team Version 1.7.0 (2003-04-16) .... Type `q()' to quit R. Error in get(x, envir, mode, inherits) : variable "biplot" was

Dear R users, I'm trying to cluster 30 gene chips using principal component analysis in package mva.prcomp. Each chip is a point with 1,000 dimensions. PCA is probably just one of several methods to cluster the 30 chips. However, I don't know how to run prcomp, and I don't know how to interpret it's output. If there are 30 data points in 1,000 dimensions each, do I have to

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

I've noticed that the arguments and result list of prcomp in the mva package (with 61.1) are not quite the same as in the Blue Book and in Splus. Is this intentional or can I change it? If I change it who should I send the code to? Paul Gilbert -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read