similar to: Problems with principal components analysis PCA with prcomp

Dear friends, I am having some doubts about the amount of memory that is being used by R in my Mac (MacBook Pro, 2Gig). Is there a way to increase the amount of memory used? When I type: > mem.limits() the result is: nsize vsize NA NA and I can't change it, tough my computing in R isn't using all the memory at it's disposal. Best regards, Carlos -- Carlos GUERRA

Good morning to you all, I have a problem with a set of matrices that I want to compare. I want to see the similarity between them, and to be able to extract the differences between them. They have all the same number of columns and rows, and correspond presence absence data: for example: m1 <- matrix(c(1,0,0,0,1,0,1,1,1,1,1,1), 3,4) m2 <- matrix(c(1,0,1,0,1,0,0,1,0,1,0,1), 3,4) I

Hi all, I think I may be confused by different people/programs using the word rotation differently. Does prcomp not perform rotations by default? If I understand it correctly retx=TRUE returns ordinated data, that I can plot for individual samples (prcomp()$x: which is the scaled and centered (rotated?) data multiplied by loadings). What does it mean that the data is rotated from the

Dear useR's, I wanted to load the package GeoXP in my computer but I couldn't because this warning message appeared: > require(GeoXp) Carregando pacotes exigidos: GeoXp Carregando pacotes exigidos: tcltk Loading Tcl/Tk interface ... Erro em fun(...) : couldn't connect to display ":0" Al?m disso: Warning message: pacote 'GeoXp' foi compilado na vers?o do R 2.4.1

Hi all, I am attempting to run PCA on a matrix (nrow=66, ncol=84) using 'prcomp' (stats package). My data (referred to as 'Q' in the code below) are separate river streamflow gaging stations (columns) and peak instantaneous discharge (rows). I am attempting to use PCA to identify regions of that vary together. I am entering the following command:

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

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:

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

Hey guys, I am new to R and apologize for the basic question - I do not mean to offend. I have been using R to perform PCA on a set several hundred objects using a set of 30 descriptors. From the results generated by prcomp(), is there a way to print a matrix showing the contributions of the original variables to each PC? My hope is to identify which of the original 30 variables are the most

I've just starting using the prcomp function, and I want to be able to extract individual principal components (e.g. PC1, PC2) in vector format. I haven't been able to find any documentation that explains how to do this (or even if it is possible). Any help on the subject would be greatly appreciated. Many thanks Deirdre Toher Teagasc National Food Centre

Hello All, The basic premise of what I want to do is the following: I have 20 "entities" for which I have ~500 measurements each. So, I have a matrix of 20 rows by ~500 columns. The 20 entities fall into two classes: "good" and "bad." I eventually would like to derive a model that would then be able to classify new entities as being in "good

Hi, I am trying to run PCA on a set of data with dimension 115*300,000. The columns represnt the snps and the row represent the individuals. so this is what i did. #load the data code<-read.table("code.txt", sep='\t', header=F, nrows=300000) # do PCA # pr<-prcomp(code, retx=T, center=T) I am getting the following error message "Error: cannot allocate vector of

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

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

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,

HI all, I am trying to compute the EOF of a matrix using prcomp but unable to get the expansion co-efficients. is it possible using prcomp or are there any other methods thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Hi, I do have a file that has 500000 columns and 40 rows. I want to apply PCA on that data and this is what I did h1<-read.table("Ccode.txt", sep='\t', header=F) # reads the data from the file Ccode.txt h2<-prcomp(na.omit(h1),center=T) but I am getting the following error "Error in svd(x, nu = 0) : 0 extent dimensions" I appreciate if someone can help

Hi Listers, I am trying to reflect a PCA biplot in the x-axis (i.e. PC1) but am not having much success. In theory I believe all I need to do is multiply the site and species scores for the PC1 by -1, which would effectively flip the biplot. I am creating a blank plot using the plot command and accessing the results from a call to rda. I then use the calls to scores to obtain separate site and

Hi there, I've a training dataset and a test dataset. My aim is to visually allocate the test data within the calibrated space reassembled by the PC's of the training data set, furthermore to keep the training data set coordinates fixed, so they can serve as ruler for measurement for additional test datasets coming up. Please find a minimum working example using the wine dataset below.

Hello R-ers, I'm trying to do a weighted principal components analysis. I couldn't find any such option with princomp or prcomp. Does anyone know of a package or way to do this? More specifically, the observations I'm working with are averages from populations of varying sizes. I thus need to weight the observations by sample size. Ideally I could apply these weights at the cell