search for: pcamethod

I've been using the pcaMethods to develop a scores matrix ======================================= data(iris) pcIr <- pca(iris[,1:4], method="nipals", nPcs=3, cv="q2") test <- scores(pcIr) ======================================== What I'm looking to do is to use lattice's barchart to plot the...

Hi All, I have tried to find an answer within documentation, but I cannot: o How can call a class "slot" without knowing the name a priori? E.g., let's say I use the "pcaMethods" library to create a "pcaRes" object. How can I call parts of that object without using the specific names of the object in the call? example code: library(pcaMethods) myMatrix <- matrix(runif(1e4), ncol=100) ## silly, but sufficient for example myPCA <- pca(myMatrix) ##...

Hi, I'm trying to perform a Principal Component Analysis on meteorological data with 10 predictors. I use the library pcaMethods to obtain a lot of information (loadings, scores, mean, standard deviation, etc). How can I obtain the scores (new PC variables) for new values of weather predictors using the results of PCA analysis (loadings, mean value, standard deviation) and without performing everytime the PCA analysis...

First of all I should say this email is more of a general statistics questions rather than being specific to using R but I'm hoping that this may be of general interest. I have a dataset that I would really like to use PCA on and have been using the package pcaMethods to examine my data. The results using traditional PCA come out really nicely. The dataset is comprised of a set of questions on dog behaviour answered by their handlers. The questions fall into distinct components which may biological sense and the residuals are reasonable small. Now the problem....

Thanks for all your great answers. The app I?m working on is indeed an exploratory data analysis tool for gene expression, which requires a bunch of bioconductor packages. I guess for now, my best solution is to divide my app into modules and load/unload packages as the user switch from one module to another. This brought me another question: it seems that unload package with the

Hi, I am not sure if this should go to r-help or r-dev list. I have looked at some archives of R libraries but cannot seem to see a project that focuses on the new matrix factorization techniques that are showing up in the literature. I have made a list of them: https://sites.google.com/site/igorcarron2/matrixfactorizations they include Robust PCA, Dictionnary Learning, Sparse PCA and are

Hello, I have a matrix with 267 columns, all rows of which have at least one column missing (NA). All three methods i've tried (pcs, princomp, and prcomp) fail with either "Error in svd(zsmall) : infinite or missing values in 'x'" (latter two) or "Error in cov.wt(z) : 'x' must contain finite values only" The last one happens because of the check if

> biplot standardGeneric for "biplot" defined from package "stats" function (x, ...) standardGeneric("biplot") <environment: 0x0d4444d8> Methods may be defined for arguments: x Use showMethods("biplot") for currently available ones. > > > showMethods("biplot") Function: biplot (package stats) x="ANY"

Hi all, I found that the PCA gave chaotic results when there are big changes in a few data points. Are there "improved" versions of PCA in R that can help with this problem? Please give me some pointers... Thank you! [[alternative HTML version deleted]]

...ny more (or am I just going blind?). Is there somewhere I can snag the source packages used for the PPA? Should I just download and run cran2deb myself to obtain them? Also, what's the best way to contribute back to you? FYI, the first packages on my hitlist are (mostly from bioconductor): pcaMethods (should be easy) bitseq (I just packaged the standalone version - https://launchpad.net/~tbooth/+archive/ppa1/+packages) samr biomaRt (probably a PITA, not looked yet) Cheers, TIM -- Tim Booth <tbooth at ceh.ac.uk> NERC Environmental Bioinformatics Centre Centre for Ecology and Hydrol...

I have recently upgraded to Ubuntu Gutsy and, for the first time, am using a 64-bit installation. After failing miserably to install R from source, not a problem for me in the past with a 32-bit install, I went the route of using the Debian Etch build. This went smoothly, but I am unable to update my numerous R and BioConductor packages, getting non-zero exit status errors on each package. Is

Dear list members; The code given below corresponds to the PCA-NIPALS (principal component analysis) algorithm adapted from the nipals function in the package chemometrics. The reason for using NIPALS instead of SVD is the ability of this algorithm to handle missing values, but that's a different story. I've been trying to find a way to improve (if possible) the efficiency of the code,

...ct: Re: [R] efficient code - yet another question To: "steven wilson" <swpt07 at gmail.com> Cc: r-help at r-project.org Message-ID: <c968588d0809291012sd695d50o26142f7d3b4db90b at mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 You should also have a look at the pcaMethods package (on Bioconductor). I did some code optimization for the NIPALS algorithm in that package which sped up the algorithm by at least a factor of two. Kevin Wright On Wed, Apr 30, 2008 at 10:56 PM, steven wilson <swpt07 at gmail.com> wrote: > Dear list members; > > The code...