similar to: Principal component analysis PCA

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, I did a pca with over 200000 snps for 340 observations (ids). If I plot the eigenvectors (called rotation in prcomp) 2,3 and 4 (e.g. plot (rotation[,2]) I see a strange "column" in my data (see attachment). I suggest it is an artefact (but of what?). Suggestion: I used prcomp this way: prcomp (mat), where mat is a matrix with the column means already substracted followed by a

Hi R-help folks, I have been doing some single SNP association work using snpMatrix. This works well, but produces a lot of false positives, because of population structure in my data. I would like to correct the p-values (which snpMatrix gives me) for population structure, possibly using principle component analysis (PCA). My data is complicated, so here's a simple example of what

Hi all I'm writing a direct llvm backend for gcjx a new java fronted for gcc. I'm now ready to tackle creating the structures to represnt classes I read the gcc 4.0 patches and it seems that the llvm struct is padded and aligned using the info from the gcc tree. In my case I don't have this information. I'm willing to intially let llvm align and pad the struct but its not clear

Dear R users, i would like to apply a PCA on image data for data reduction. The image data is available as three matrices for the RGB values. At the moment i use x <- data.frame(R,G,B)#convert image data to data frame pca<-princomp(x,retx = TRUE) This is working so far. >From this results then i want to create a new matrix from the first (second..) principal component. Here i stuck.

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) ##

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

hello R_team having perfomed a PCA on my fitted model with the function: data<- na.omit(dataset) data.pca<-prcomp(data,scale =TRUE), I´ve decided to aggregate two variables that are highly correlated. My first question is: How can I combine the two variables into one new predictor? and secondly: How can I predict with the newly created variable in a new dataset? Guess I need the

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

Hi, I am trying to do a PCA on my data but I keep getting the error message svd(x, nu=0) infinite or missing values >From the messages posted on the subject, I understand that the NAs in my data might be the problem, but I thought na.omit would take care of that. Less than 5% of my cells are missing data. However, the NAs are not regularly distributed across my matrix: certain cases and

On Tue, 20 Dec 2005, Mike Emmel wrote: > Hi all I'm writing a direct llvm backend for gcjx a new java fronted for gcc. Great! > I'm now ready to tackle creating the structures to represnt classes I > read the gcc 4.0 patches and it seems that the llvm struct is padded > and aligned using the info from the gcc tree. Yes. > In my case I don't have this information.

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

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Hi, I need to rbind two data frames. Each one has a header . after the rbind I would like to keep the header for each and have the two data frames separated by a line. Is this possible to do in R? For example weight_mean weight_sd.dev > F 14.33333 4.932883 > M 34.66667 10.692677 > > hight_mean hight_sd.dev > F 35.00000 7.071068 > M 34.66667 10.692677 --

Hi, I am conducting an association analysis of genotype and a phenotype such as cholesterol level as an outcome and the genotype as a regressor using multiple linear regression. There are 3 possibilities for the genotype AA, AG, GG. There are 5 people with the AA genotype, 100 with the AG genotype and 900 with the GG genotype. I coded GG genotype as 1, AG as 2 and AA as 3 and the p-value for the

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,

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.

Hello all, I have performed PCA on stacked wind vector data for 20 different spatial locations. In this case I therefore am in effect working out a PCA for 40 different stations (2 paired pieces of information [ie u and v vector information] for each station), so have 40 PCs. In my previous PCA (for none vector data) i have plotted the PC loadings onto a map of the area of concern, and would

Hi all, I have a 16 bit image (TIFF) and i want to analyse the pixels distribution. So, i obtain a matrix which values are between 0 and 2^16 -1. Now i would like to represnt this image with the fucntions rgb() and image(). I am not sure , but i think that only 256 colors are available. So is there a solution to represent all the palette of the colors or i have to limit the representations with

Hi All, this could be a simple question but I am looking into modifying a data frame using a "condition" without the need to loop over that data, would that be possible? I have tried the following > x<-c(4,5,6,6,8) > y<-c("a","b","b","b","c") > data<-data.frame(x,y) > data x y 1 4 a 2 5 b 3 6 b 4 6 b 5 8 c if