similar to: Attempt to customise the "plotpc()" function

Displaying 20 results from an estimated 2000 matches similar to: "Attempt to customise the "plotpc()" function"

2010 Dec 22
3
Estimate "between-axes" vs "within-axes heterogeneity of multivariate matrices
Hi! My question(s) in the end might be silly but I am no expert on this, so here it goes: Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered PCA being in some cases useful. They clearly explain that "it is the case" when multi-dimensional data display distinct clusters (which have zero, or near-zero, projections in some subset of the axes) and the task is
2010 Mar 11
3
Define column names to a series of data.frames
Greets to the list! I am aware that this topic has been discussed several times. And I've read quite some related posts [1]. Yet, can't seem to give a solution to my problem. I have 6 data frames consisting of 6 rows x 7 columns put together from other data.frames. Something like: a b c d e f g v1 # # # # # # # v2 # # # # # # # v3 # # # # # # # v4 # # # # # # # v5 # # # # # # # v6
2010 Apr 21
1
Cross-checking a custom function for separability indices
Hi list! I have prepared a custom function (below) in order to calculate separability indices (Divergence, Bhattacharyya, Jeffries-Matusita, Transformed divergene) between two samples of (spectral land cover) classes. I need help to cross-compare results to verify that it works as expected (since I don't know of any other foss-tool that will give me quickly some results). Does anybody
2010 Jun 30
3
Factor Loadings in Vegan's PCA
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
1998 Aug 26
0
prcomp & princomp - revised
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
2002 Oct 29
0
patch to mva:prcomp to use La.svd instead of svd (PR#2227)
Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---
2002 Oct 29
0
PCA with n << p (was R-1.6.0 crashing on RedHat6.3)
[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
2009 May 23
1
create vectors within a double loop
Hi R-list. This is my first post. I'll try to be as precise as possible with the difficulty I have to "get things done". I have a hard time trying to construct a double "for" loop and create within the inner loop new objects (in this case vectors). I posted this question in a non-directly related with pure R-problems list (in grass-stats). In addition, I think I
2009 Nov 25
1
which to trust...princomp() or prcomp() or neither?
According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402
2006 May 17
2
prcomp: problem with zeros? (PR#8870)
Full_Name: Juha Heljoranta Version: R 2.1.1 (2005-06-20) OS: Gentoo Linux Submission from: (NULL) (88.112.29.250) prcomp has a bug which causes following error Error in svd(x, nu = 0) : infinite or missing values in 'x' on a valid data set (no Infs, no missing values). The error is most likely caused by the zeros in data. My code and temporary workaround: m = matrix(... ...
2013 Jan 23
0
na.omit option in prcomp: formula interface only
Dear r-devel list, dear Ben I came across a post of Ben Bolker from Feb 2012 (see below) on handling NA values in prcomp(). As I faced the same issue and found Ben's suggestions interesting, I was wondering whether this led to further discussions I might have missed? I understand handling NA values is far from trivial, but would it be possible to add a warning in the documentation, and/or
2004 Jan 15
2
prcomp scale error (PR#6433)
Full_Name: Ryszard Czerminski Version: 1.8.1 OS: GNU/Linux Submission from: (NULL) (205.181.102.120) prcomp(..., scale = TRUE) does not work correctly: $ uname -a Linux 2.4.20-28.9bigmem #1 SMP Thu Dec 18 13:27:33 EST 2003 i686 i686 i386 GNU/Linux $ gcc --version gcc (GCC) 3.2.2 20030222 (Red Hat Linux 3.2.2-5) > a <- matrix(rnorm(6), nrow = 3) > sum((scale(a %*% svd(cov(a))$u, scale
2012 Feb 09
0
na.omit option in prcomp: formula interface only
This is a wishlist/request for discussion about the behaviour of the na.action option in prcomp, specifically the fact that it only applies to the formula interface. I had a question from a friend (who is smart and careful and generally R's TFM, although like all of us he misses things sometimes) asking why the na.action= argument didn't seem to be doing anything in prcomp (i.e. one
2016 Mar 22
3
Memory usage in prcomp
Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD
2016 Mar 22
3
Memory usage in prcomp
Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD
2016 Mar 24
0
summary( prcomp(*, tol = .) ) -- and 'rank.'
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
2004 Mar 04
1
prcomp: error code 1 from Lapack routine dgesdd
Dear all I have a big matrix of standardized values (dimensions 285x5829) and R fails to calculate the principal components using prcomp() with the following error message: pc <- prcomp(my.matrix) Error in La.svd(x, nu, nv, method) : error code 1 from Lapack routine dgesdd Is the matrix too big? I'm using R-1.8.1 under Unix (Solaris8) and Linux(Suse 8.2). I tried to perform a principal
2006 Jun 16
2
bug in prcomp (PR#8994)
The following seems to be an bug in prcomp(): > test <- ts( matrix( c(NA, 2:5, NA, 7:10), 5, 2)) > test Time Series: Start = 1 End = 5 Frequency = 1 Series 1 Series 2 1 NA NA 2 2 7 3 3 8 4 4 9 5 5 10 > prcomp(test, scale.=TRUE, na.action=na.omit) Erro en svd(x, nu = 0) : infinite or missing values in 'x'
1998 Apr 24
1
Warning: ignored non function "scale"
I've been working on a revised version of prcomp and princomp. Below is my current draft of prcomp, which is marginally different from V&R. I've added center and scale as optional arguments. However, scale causes the following: > zi _ prcomp(iris[,,2]) Warning: ignored non function "scale" because scale is both a variable and a function. Is there any way to avoid this
2010 Jun 28
2
Note on PCA (not directly with R)
Dear all, I am looking for some interactive study materials on Principal component analysis. Basically I would like to know what we are actually doing with PCA? What is happening within the dataset at the time of doing PCA. Probably a 3-dimensional interactive explanation would be best for me. I have gone through some online materials specially Wikipedia etc, however what I need a "movable