similar to: Functional Principal Components Analysis

Dear R gurus, I have some climatic data for a region of the world. They are monthly averages 1950 -2000 of precipitation (12 months), minimum temperature (12 months), maximum temperature (12 months). I have scaled them to 2 km x 2km cells, and I have around 75,000 cells. I need to feed them into a statistical model as co-variates, to use them to predict a response variable. The climatic

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

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) ##

I have data set with binary responses. I would like to conduct polychoric principal component analysis (pPCA). I know there are several packages used in PCA but I could not find one that directly estimate pPCA and graph the individuals and variables maps. I will appreciate any help that expand these reproducible scripts. #How to conduct polychoric principal component analysis pPCA using #either

Hello, I need to do a principal component analysis with EQUAMAX-rotation. Unfortunately the function principal() I use normally for PCA does not offer this rotation specification. I could find out that this might be possible somehow with the package GPArotation but until now I could not figure out how to use this in the principal component analysis. Maybe someone can give an example on how to do

Hi all, I am wondering in R, suppose I did the principal component analysis on training data set and obtain the rotation matrix, via: > pca=prcomp(training_data, center=TRUE, scale=FALSE, retx=TRUE); Then I want to rotate the test data set using the > d1=scale(test_data, center=TRUE, scale=FALSE) %*% pca$rotation; > d2=predict(pca, test_data, center=TRUE, scale=FALSE); these two

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 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 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 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:

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the documentation, but I get a message error I can't

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?

At the cost of breaking the thread I'm going to change your subject and replace 'Principle' by 'Principal'. I just can't stand it any longer... OK, here is how I would solve your other problems. First put > wh <- c("USA", "New Zealand", "Dominican Republic", "Western Samoa", "Cook Islands") > ind

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Dear Forum, I have a class variable 1 (populations A-E), and two other class variables, variable 2 and variable 3. What I want is to see if the combination of var 2 and var 3, will give me a pattern that allows to distinguish populations. I found several packages like ade4, with pcaiv function and factoMineR. but there are not working. Using the ade4 package, when I try to build the pca: pca1

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

Hi. I have been looking at the PCAproj function in package pcaPP (R 2.4.1) for robust principal components, and I'm trying to interpret the results. I started with a data matrix of dimensions RxC (R is the number of rows / observations, C the number of columns / variables). PCAproj returns a list of class princomp, similar to the output of the function princomp. In a case where I can

fda 1.2.3 ======================== Version 1.2.3 of the fda package has just been released. This version adds to previous versions a script to create most of the figures of chapter 6 of "Applied Functional Data Analysis" by Ramsay and Silverman. Other changes offer simpler calls to time warping / registration and functional principal component functions. The fda package supports the

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*

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