similar to: prcomp function

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hi, I was wondering if function eigen() does something different from the function call eigen() in SAS. I'm in the process of translating a SAS code into a R code and the values of the eigenvectors and eigenvalues of a square matrix came out to be different from the values in SAS. I would also appreciate it if someone can explain the difference in simple terms. I'm pretty new to both

Hi ... Please could you help with probably a very simple problem I have. I'm completely new to R and am trying to follow a tutorial using R for Force Distribution Analysis that I got from ... http://projects.eml.org/mbm/website/fda_gromacs.htm. Basically, the MDS I preform outputs a force matrix (.fm) from the force simulation I perform. Then, this matrix is read into R and prcomp is

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

Given a matrix B, where B=A'A, how can I find A? In other words, if I have a matrix B which I know is another matrix A times its transpose, can I find matrix A? Thanks, Mike

Hello, Does somebody knows if there exists a function which solves a set of equation, say f(vars), for the variables vars (similar to Solve in mathematica). The functions I am considering are of the form f(t) ~ A*exp(B*t), where A and B are matrices. Thanks Thomas

Dear mailing list, I have following array: X2 Y2 [1,] 422.7900 6.0 [2,] 469.8007 10.5 [3,] 483.9428 11.0 [4,] 532.4917 25.5 [5,] 596.1942 33.5 [6,] 630.8496 40.5 [7,] 733.2996 45.0 [8,] 946.4779 32.0 [9,] 996.8068 35.5 [10,] 1074.3310 23.0 I do afterwards the following: plot.new() plot.window(xlim=c(min(X1)-50,max(X1)+50),

Hello, It might be a trivial question but I just wanted to find out the relationship between sdev and proportion of variance generated by prcomp. I got the following result from my data set ???????????????????????????? PC1????? PC2????? PC3 Standard deviation???? 104.89454 15.40910 9.012047 Proportion of Variance?? 0.52344? 0.01130 0.003860 Cumulative Proportion??? 0.52344? 0.53474 0.538600

Dear All, The help for prcomp, under "Value" says: sdev: the standard deviation of the principal components (i.e., the eigenvalues of the cov matrix, though the calculation is actually done with the singular values of the data matrix). The way I read it, it implies that the sdev are the eigenvalues, but I think that sdev is actually the square root of the

In the manual page for prcomp(), it says that sdev is "the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)." ?However, this is not what I'm finding. ?The values appear to be the standard deviations of a reprojection of

Dear R helpers, is there a function or way within R to solve A'A=S for A, where all matrices have p x p order and S is a variance-covariance matrix? Thank you, Ralf Engelhorn

It is not clear to me that one can. If the singular value decomposition of A is the triple product P d Q', then the singular value decomposition of A'A=S is Q d^2 Q'. The information about the orthonormal matrix P is lost, is it not? ********************************************************** Cliff Lunneborg, Professor Emeritus, Statistics & Psychology, University of Washington,

Hello, Can you get eigenvalues in addition to eigevectors using prcomp? If so how? I am unable to use princomp due to small sample sizes. Thank you in advance for your help! Rebecca Young -- Rebecca Young Graduate Student Ecology & Evolutionary Biology, Badyaev Lab University of Arizona 1041 E Lowell Tucson, AZ 85721-0088 Office: 425BSW rlyoung at email.arizona.edu (520) 621-4005

The help for prcomp on R 1.0.0 states that the component sdev of the return value is the eigenvalues of the cov matrix. Am I completely mistaken, or should this be the _square root_ of the eigenvalues? Also, the documentation is not very clear about how tol is used to omit components. (The _code_ is clear, though. :-) -- B/H

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 ---

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

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

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

Hello all, I wish to compute site scores using PCA (prcomp) on a matrix with missing values, for example: Drain Slope OrgL a 4 1 NA b 2.5 39 6 c 6 8 45 d 3 9 12 e 3 16 4 ... Where a,b... are sites. The command > pca<-prcomp(~ Drain + Slope + OrgL, data = t, center = TRUE, scale = TRUE, na.action=na.exclude) works great, and from

Dear all, >From the documentation of biplot.prcomp: scale: The variables are scaled by 'lambda ^ scale' and the observations are scaled by 'lambda ^ (1-scale)' where 'lambda' are the singular values as computed by 'princomp'. >From the source code of prcomp: lam <- x$sdev[choices] n <- NROW(scores) lam <- lam * sqrt(n)