similar to: Visualising the effects of PCAs

Hi all, I have a file of average SWE observations for 40 years at over 4,000 points and am attempting to do a spatiotemporal analysis of the data using PCA, much like this paper did using snow depth: http://journals.ametsoc.org/doi/pdf/10.1175/1520-0442%281998%29011%3C0856%3ATCIRWS%3E2.0.CO%3B2 I have followed the code in the link below by my loadings are far too small (For example, the paper

Dear R users, I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green). model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4) Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533

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 all: Subjects were measured two times (t1 and t2) on different variables (v1 ... vn). Between t1 and t2 there was an experimental manipulation. I computed two PCAs for time-points t1 and t2. Is it possible to combine both PCAs in order to get only one set of eigenvectors? Due to the experimental manipulation measurement values on time points t1 and t2 changed for each subject. Many

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)]))

Dear R Help Community, I have a question and an answer (based on reading this forum and online research), but I though I should share both since probably there's a much better way to go about my solution. My question is specifically about how to best visualise multiple response contingency tables. What I mean by 'multiple response' is that the total number of responses per row of a

Hi everyone, I'd like to project two pcas onto one device window. I plot my first PCA: biplot(prcomp(t(cerebdevmat)), var.axes=FALSE, cex=c(1,.1), pc.biplot=TRUE) Now I'd like to project the features of another PCA onto this graph. Any suggestions? I know this is easily done in MatLab but haven't figured it out in R. Thanks, Tanya [[alternative HTML version deleted]]

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

I am wanting to plot a series of wind vectors onto a contoured area map for a series of weather stations (eg arrows showing wind speed/direction for a particular time snapshot), can someone please advise me how best to approach this? My desired end point is to be able to link a time series of such data together so that I will in effect have a "movie" displaying the evolution of these

I sent a mail last week asking for some advise in relation to displaying wind vectors on a contour map of a region. Whilst I have had some useful feedback relating to the second part of this question (namely how to animate a time series of still frames), I haven't recieved any advise on how I might create the still images of the spatially distributed wind vector data at any given time point.

My previous post about prcomp and princomp was done in some haste as I had long ago indicated to Kurt that I would try to have this ready for the June release, and it appeared that I would miss yet another release. I also need to get it out before it becomes hopelessly buried by other work. Brian Ripley kindly pointed out some errors, and also pointed out that I was suggesting replacing some

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on

Hi all, Version 0.2.4 of the googVis package has been released on CRAN and will be available from your local CRAN mirror soon. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google Since the last version a lot of work has been

Hi all, Version 0.2.4 of the googVis package has been released on CRAN and will be available from your local CRAN mirror soon. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google Since the last version a lot of work has been

Hi, If center=T (by default) in invoking prcomp, that is, prcomp (x) where x is a matrix with the observations are in rows and the variables are in column, is this equivalent to scale(t(x),center=T,scale=F)?where?x is a matrix with?the observations are in rows and the variables are in columns? Additionally, could you advise when the variables should mean centered (center = T in prcomp) before the

Martin, I fully agree. This becomes an issue when you have big matrices. (Note that there are awesome methods for actually only computing a small number of PCs (unlike your code which uses svn which gets all of them); these are available in various CRAN packages). Best, Kasper On Thu, Mar 24, 2016 at 1:09 PM, Martin Maechler <maechler at stat.math.ethz.ch > wrote: > Following from

Having a look at clang's source, you can find in "lib/CodeGen/BackendUtil.cpp" the functions where clang builds the passes to emit code. The optimization passes used are there and you can simulate them via the "opt" utility, by running each pass one at a time. LLVM also declares standard module/function passes on include/llvm/Support/StandardPasses.h. Have a look and see

Dear R-list users, I'm new to principal components and factor analysis. I thought this method can be very useful for me to find relationships between several variables (which I know there is, only don't know which variables exactly and what kind of relation), so as a structure detection method. Now, I'm experimenting with the function prcomp from the mva package. In my source code

Hi, I have the following data about courses (504) in a university, two attributes about the proportion of resources used (#resources_used / #resources_available), namely the average and the standard deviation. Thus I have: [1] n=504 rows [2] 1 id column and 2 attributes Here's a sample of the data: courseid,average,std 12741,1,0 17161,1,0 12514,1,0