similar to: princomp with more coloumns than rows: why not?

To my knowledge, this has not been reported previously, and doesn't seem to have been changed in R-devel or R-patched. If M is a matrix with coloumn names, and mod <- prcomp(M) # or princomp then predicting a single observation (row) with predict() gives the error Error in scale.default(newdata, object$center, object$scale) : length of 'center' must equal the number of

I am having a similar problem understanding the data structure of the "yarn" dataset described in the "[R] data structure for plsr" posts. I have spectroscopic data I'd like to run through a PLSR and have read the tutorial series, but still do not understand the data format required for the code to process my data. My current data structure consists of a .csv file read into

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

Hi All, would appreciate an answer on this if you have a moment; Is there a function (before I try and write it !) that allows the input of a covariance or correlation matrix to calculate PCA, rather than the actual data as in princomp() Regards Glenn [[alternative HTML version deleted]]

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

I have a decent sized matrix (36 x 11,000) that I have preformed a PCA on with prcomp(), but due to the large number of variables I can't plot the result with biplot(). How else can I plot the PCA output? I tried posting this before, but got no responses so I'm trying again. Surely this is a common problem, but I can't find a solution with google? The University of Dundee is a

hi R help group, I have installed PLS package in R and use it for princomp & prcomp commands for calculating PCA using its example file(USArrests example). But How I can use PLS for Partial least square, R square, mvrCv one more think how i can import external file in R. When I use plsr, R2, RMSEP it show error could not find function plsr, RMSEP etc. How I can calculate PLS, R2, RMSEP, PCR,

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

Some webpage has described prcomp and princomp, but I am still not quite sure what the major difference between them is. Can they be used interchangeably? In help, it says 'princomp' only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as

Dear all, perhaps I am using princomp.formula and prcomp.formula in a way that is not documented to work, but then the documentation just says: formula: a formula with no response variable. Thus, to avoid a lot of typing, it would be nice if one could use '.' and '-' in the formula, e.g. > library(DAAG) > res <- prcomp(~ . - case - site - Pop - sex, possum)

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

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:

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

Hello fellow Rers, I have a no-doubt simple question which is turning into a headache so would be grateful for any help. I want to do a principal components analysis directly on a correlation matrix object rather than inputting the raw data (and specifying cor = TRUE or the like). The reason behind this is I need to use polychoric correlation coefficients calculated with John Fox's

Hi all, I have been using princomp() recently, its very useful indeed, but I have a question about how to specify the rows of data you want it to choose. I have a set of variables relating to bird characteristics and I have been using princomp to produce PC scores from these. However since I have multiple duplicate entries per individual (each bird had a varying number of chicks), I only want

When I look through archives at https://stat.ethz.ch/pipermail/r-help/2003-October/040525.html I see this: Liaw, Andy wrote: >In the `Detail' section of ?princomp: > >princomp only handles so-called Q-mode PCA, that is feature extraction of >variables. If a data matrix is supplied (possibly via a formula) it is >required that there are at least as many units as variables. For

Hi R People: When performing PCA, should I use prcomp, princomp or fast.prcomp, please? thanks. Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at gmail.com

Hi, I am a new user of R, and apologize beforehand for the simplistic nature of this question: I ran prcomp on a data set with 4 variables, and am able to see the summary information (variance contribution, rotation matrix, plots, etc.). However, I'd also like to extract the actual values of the principal components (PC) corresponding to each sample. I've looked in the help, on-line