similar to: Principal Components for matrices with NA

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 scores something like below, but expanded to all the scores

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)

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,

Hello list, Is there any way to perform a block comment in R? In C++, anything in between a /* and */ is considered a comment, and it allows programmers to comment out chunks of code for testing and debugging. Is there such a feature in R? Cheers, Wee-Jin

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

Hello everybody, I am trying to understand the Advance routing and Traffic control. In the examples section of the how to an example script is given. Section 15.8.2 (the ultimate setup for Your internet connection) The example says download is 800 and upload is 220 ###### uplink # install root CBQ tc qdisc add dev ppp0 root handle 1: cbq avpkt 1000 bandwidth 10mbit Maybe I am

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

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

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

> 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]]

Dear R users, I have been doing rotation on Principal components analyse, with varimax function and promax. Following those changes, I cannot find now how to get the spatial evolution and the variance. Indeed, with the function princomp that has been used to get the principal components, arguments such as $scores and $sdev were available to get the spatial evolution and the variance, with

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

Hi, I am interested in using functional principal component analysis (Functional PCA). However, in the help of fda package, it does not have an example of pca.fd function. Does anyone have the example of pca.fd function? Thank you very much. Sincerely yours, Nantachai

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]]

Dear all, I have a dataset representing several geographical points (locations), each one having a specific value after performing a PCA. Now I'm trying to understand how to separately represent on a geographic map (of Europe) the pattern of PC1,2, ecc.with colors (e.g.heatmap) I have to add that the dataset includes relatively few points. Therefore, I would also like to interpolate the values

May be a simple question, but not understanding why in princomp I get different results for loadings and summary for my eigenvectors and eigenvalues. When I use pc.cr$loadings using the USArrests dataset the proportion of variance is equal for each of the components, but when summary(pc.cr) is used the proportion of variance is showing different proportions. My question is why do they differ? I

Hello I'm writing an example PCA analysis for some students. I've done PCA on a 2-column matrix to show it at the most simple form (a rotation of two axes). I can't, however, figure out how to plot the rotated axes as lines over top of the original data plotted on the x-y. Does anyone know how to do this? It will be a good way to show how the rotated axes describing the most

Dear all, I am trying to find the PCs of a spatial data set (single variable). I want to calculate the PCs at each Lat-Lon location. The* 'princomp'* command gives the approximate standardized data (i.e* pca$scores*), stranded deviation ..etc. I tried* 'pca$loadings'*also, but it giving value 1 all time. Then I tried manually(without using* princomb*

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,