similar to: aggregating variables with pca

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 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 all, I'm new to PCA in R, so this might be a basical thing, but I cannot find anything on the net about it. I need to make a PCA plot with two response variables (df$resp1 and df$resp2) against eight metabolites (df$met1, df$met2, ...) and I don't have a clue how to do... and I've only used the simplest PCAs before, like this: pcaObj=prcomp(t(df[idx, c(40:47)]))

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

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

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) ##

The script below successfully produces a biplot of the data but the 'site names' (rows) and the names of the 'response variables' (columns) are shown as simple numerals (rather than the column and row names). How might I 'enforce' the use of the row/column names used in the datafile (section of datafile shown below)? Can anyone help, please? Section of datafile sample a b

A bit of a newbee to R and factor rotation I am trying to understand factor rotations and their implementation in R, particularly the GPArotation library. I have tried to reproduce some of the examples that I have found, e.g., I have taken the values from Jacksons example in "Oblimin Rotation", Encyclopedia of Biostatistics

[Moderator's Note: This message needed manual interaction by me, since the attachment originally was declared as ``application/octet-stream'' even though it was only plain text. We do not allow octet-stream (aka binary!) attachments on our mailing list -- for virus/spam filtering reasons. -- MM] We have also encountered the problem Douglas

rm(list=ls()) yx.df<-read.csv("c:/MK-2-72.csv",sep=',',header=T,dec='.') dim(yx.df) #get X matrix y<-yx.df[,1] x<-yx.df[,2:643] #conver to matrix mat<-as.matrix(x) #get row number rownum<-nrow(mat) #remove the constant parameters mat1<-mat[,apply(mat,2,function(.col)!(all(.col[1]==.col[2:rownum])))] dim(yx.df) dim(mat1) #remove columns with numbers of

Hi Fellow R enthusiasts I have managed to reshape my data using a much shorter script than before. Woohoo However now I have new problems. The code is below. There are no problems with the create matrix section. The problem code is highlighted in bold. I am trying to do PCA on the data. Here are the errors. Error1 code : OGSscaled = rangescale(OGS) error message : Error in dim(newX) <-

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

Good day all. This is to thank all those who have helped in fixing this problem. Starting with a text book was indeed a problem, however, that gave me a clue of what I was looking for. This, with your contributions added to other materials I got on the net, put me on the right track. Thank you so much. Warmest regards Ogbos On 31 January 2010 14:07, S Ellison <S.Ellison@lgc.co.uk> wrote:

I do not understand, from a PCA point of view, the option center=F of prcomp() According to the help page, the calculation in prcomp() "is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix" (as it's done by princomp()) . "This is generally the preferred method for numerical accuracy"

Hello all, I'm currently using R to do PCA Analysis, and was wondering if anyone knew the specific R Code that could limit the output of the PCA Analysis so that you only get the Principal Component features as your output and none of the extraneous words or numbers that you don't want. If that was unclear, let me use linear regression as an example: "lm(y~x)" is the normal

Dear useRs: I'm not sure if it's the correct place to ask but I'll try it out. I've been reading about how to perform Principal Component Analysis (PCA) in microarrays (see [1]) and there's something that I don't get it. Basically it's related with performing PCA over data sets which number of variables is greater than the number of samples. For example in the paper

Hi all I have produced a biplot for a PCA (see attached pdf) that I ran however the names of the variables which are placed at the end of the arrows overlap and are thus unreadable. Similarly some of the numbered points overlap. I was wondering if there was a way to edit the biplot to move the label names and if not what the best alternative is. Thanks Anna pca<-biodata[,3:10]

Hi, I want to include a vector representing the sites - environmental data correlation in a PCA. I currently use prcomp (no scaling) to perform the PCA, and envfit to retrieve the coordinates of the environmental data vector. However, the vector length is different from the one obtained in CAnoco when performing a species - environmental biplot (scaling -2). How can I scale the vector in order to

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

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