similar to: Contour plots of PCA loadings

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

Please could someone point me in the right direction as I appear to be having a total mental block with fairly basic PCA problem! I have a large dataframe where rows represent independent observations and columns are variables. I am wanting to perform PCA sequentially on blocks of nrows at a time and produce a graphical output of the loadings for the first 2 EOFs for each variable. I'm sure

Hello, Following on from a paper by Ludwig, Horel and Whiteman in 2004, I have been using PCA to identify surface wind patterns. In the paper they approach the problem using the "real vector approach", that is, they create paired (u,v) vectors for the wind data, in effect artificially doubling the number of variables, then perform the pca, subsequently repairing the output and plotting

I am wanting to plot pca loadings onto sites superimposed on a contour map. There are a maximum of 20 sites at which loadings might appear - however due to the nature of my data, missing data has meant that some stations have not been included in some of the pca. For example, I am performing pca for 5 different variables at 20 different sites for a number of different time intervals and want to

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

Hi, I am applying the following code to map pca loadings onto a 3d grid, my problem is this - the output only plots the spheres in the requested color (in this case "red") for the first argument. The sphere from the second argument appear as flat dark circles. Also the text3d() command only seems to work for a couple of the positions, with no text added in most cases. Could anyone offer

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

Hi, I am doing PCA on several columns of data in a data.frame. I am interested in particular rows of data which may have a particular combination of 'types' of column values (without any pre-conception of what they may be). I do the following... # My data table. allDat <- read.table("big_select_thresh_5", header=1) # Where some rows look like this... # PDB SUNID1

Hi, I have successfully completed a PCA and printed the loadings, however, numerous values are blank. I know that this means the values are just very small but not equal to zero. Is there a way to print out the loadings, including the very small values, I need them for graphing purposes. Thanks, Xan [[alternative HTML version deleted]]

Hi, The help file on princomp says that 'The signs of the columns of the loadings and scores are arbitrary, and so may differ between differnt programs for PCA, and even between different builds of R'. Does anyone know 1. Whether this applys to all functions like svd, eigen ... etc? 2. During a leave-one-out cross step, I notice the loadings also change sige when a different

Hi, Is it possible to recreate "smoothed" data sets in R, by performing a PCA and then reconstructing a data set from say the first 2/3 EOFs? I've had a look in the help pages and don't seem to find anything relevant. Thanks in advance, Laura Laura Quinn Institute of Atmospheric Science School of Earth and Environment University of Leeds Leeds LS2 9JT tel: +44 113 343 1596

Apologies for the mass mailing today! I am attempting to produce a contour plot for phsical data on a map matrix. I have a small number of data points which each has an (x,y) co-ordinate together with a corresponding value which I would like to cvreate a contour plot for. I have tried the following code: contour(data$x,data$y,data$value) but am told: Error in contour.default(data$x, data$y,

Hi, I was wondering if anyone knows of a way by which one can estimate values from a contour plot created by using the loess function? I am hoping to use the loess contour plot as a means of interpolation to identify the loess created values at points at pre-defined (x,y) locations. Could anyone point me in the right direction please? Thanks. Laura Quinn Institute of Atmospheric Science School

Hi R-community, I am doing a PCA and I need plots for different combinations of axes (e.g., PC1 vs PC3, and PC2 vs PC3) with the arrows indicating the loadings of each variables. What I need is exactly what I get using biplot (pca.object) but for other axes. I have plotted PC2 and 3 using the scores of the cases, but I don't get the arrows proportional to the loadings of each variables on

Hello can anyone help, I have been running the following script to obtain a PCA plot but the end result is rather disappointing as the points are very very small and there are no titles etc geochemdata<-read.csv(file.choose(),header=TRUE) names(geochemdata) library(vegan) bstick<-function(n, tot.var=1) rev(cumsum(tot.var/n:1)/n) geopca<-rda(geochemdata, scale=TRUE) geopca

Dear Folks I need to rotate PCA loadings and scores using R. I have run a pca using princomp and I have rotated PCA results with varimax. Using varimax R gives me back just rotated PC loadings without rotated PC scores. Does anybody know how I can obtain/calculate rotated PC scores with R? Your kindly help is appreciated in advance Francesca [[alternative HTML version deleted]]

Hello, I am trying to complete a PCA on a set of standardized ring widths from 8 different sites (T10, T9, T8, T7, T6, T5, T3, and T2). The following is a small portion of my data: T10 T9 T8 T7 T6 T5 T3 T2 1.33738 0.92669 0.91146 0.98922 0.9308 0.88201 0.92287 0.91775 0.82181 1.05319 0.92908 0.97971 0.95165 0.98029 1.14048 0.77803 0.88294 0.96413 0.90893 0.87957 0.9961 0.74926 0.71394 0.70877

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

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context:

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of