similar to: Spectral decomposition

I've just started using R and I would like to know how to do a pie chart; The "pie" comand does not exist in the R I am using, why? Thanks, Marcos -------------- next part -------------- An HTML attachment was scrubbed... URL: https://stat.ethz.ch/pipermail/r-help/attachments/20010816/b44c5388/attachment.html

Hello! I have a dataset with variables in string format (ages class and sex) which I want to recode as numerical variables. How can I accomplish this? Is there any way to do that automatically? Could someone help me? Thanks in advance, Marcos -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

All of my resources for numerical analysis show that the spectral decomposition is A = CBC' Where C are the eigenvectors and B is a diagonal matrix of eigen values. Now, using the eigen function in R # Original matrix aa <- matrix(c(1,-1,-1,1), ncol=2) ss <- eigen(aa) # This results yields back the original matrix according to the formula above ss$vectors %*% diag(ss$values) %*%

Hi all, A good new year for everybody. Could somebody help me on a question? The Singular Value Decomposition of a matrix A gives A = U * D * t(V) I A is a M X N matrix, U is the left singular matrix (M X N), D is a diagonal singular values matrix (N X N) and V is the transpose right singular ortogonal matrix (N X N). By taking the first l columns of V, with gives a (l X l) matrix, i know

hello, i want to compute the top k eigenvalues+eigenvectors of a (large) real symmetric matrix. since it doesn't look like any top-level R function does this, i'll call LAPACK from a C shlib and then use .Call. the only LAPACK function i see to do this in R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr can also return a partial eigendecomposition. why is dsyevr not

hello, i want to compute the top k eigenvalues+eigenvectors of a (large) real symmetric matrix. since it doesn't look like any top-level R function does this, i'll call LAPACK from a C shlib and then use .Call. the only LAPACK function i see to do this in R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr can also return a partial eigendecomposition. why is dsyevr not

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

> Date: Wed, 30 Jun 1999 11:12:24 +0200 (MET DST) > From: Torsten Hothorn <hothorn at amadeus.statistik.uni-dortmund.de> > > yesterday I had a little shock using qr (or lm). having a matrix > > X <- cbind(1,diag(3)) > y <- 1:3 > > the qr.coef returns one NA (because X is singular). So I computed the > Moore-Penrose inverse of X (just from 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

Hello all, I would like to use the train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2)

I am working with a dissolved oxygen dataset. continuous readings are taken at 15 minute intervals and we have been recording these data at 12 stations along the savannah river for two years now. The longest set of readings that are continuous without interuption is 53 days. I would like to look at the power spectral density at each of these sites (most likely one day will be the overridding

Hello to R uers, I am wondering if there is an easy way to perform a cross-power spectral density estimation of ?two timeseries (x and y) using the Welch's method. Both packages "bspec" and "oce" provide a function to calculate the PSD with the Welch's method, but only for a timeserie. Thank you in advance. Regards, Pascal

Hi everybody, I'm a beginner R user and I'm trying to use the package "sapa" to estimate the spectral density function of several time series using the SDF function. For each time series, I want to calculate the density function at two temporal resolutions (daily and monthly). The monthly values calculated as a mean of the daily values. I first create a ts object for both series

Dear Signal Processing Expert, I would like to generate a random stationary signal of gaussian probability density function to simulate narrow band noise at the output of an IF amplifier. I know the receiver's system temperature (Ts) and IF bandwidth (B) therefore I assume that my narrow band noise mean power equals KTsB watts and therefore the power spectral density No=KTs per Hz. Do you

Dear all, Is there any spectral analisys functionality available for R version 3.4.3? Series() functionality doesn't work in this version. Regards Danniel [[alternative HTML version deleted]]

Dear Stefan, In the paper "Improved Forward-Adaptive Prediction for MPEG-4 Audio Lossless Coding", a non-linear compander is applied to the parcor coefficients prior to quantization. This compander is designed in order to minimize quantization error, especially for magnitudes close to unity. If you determine the typical distribution of magnitudes of the LPC coefficients, you could

Dear list, I had some confusion regarding what function too use in order too relate results from spec.pgram() too a chi-square distribution. The documentation indicates that the PSD estimate can be approximated by a chi-square distribution with 2 degrees of freedom, but I am having trouble figuring out how to do it in R, and figuring out what specifically that statement in the documentation

Dear List-Members, I would like to draw the amplitudes of different frequencies from a time series as shown in the attached figure. Does anybody has an idea how to do it? Best wishes Thomas -------------- next part -------------- A non-text attachment was scrubbed... Name: milankovich.pdf Type: application/pdf Size: 93605 bytes Desc: not available Url :

Hello! I would like to ask if there is in R a function that estimates the spectral density function of a stochastic series at frequency zero by the "plug-in method", advocated by Andrews in his paper "Heteroscedasticity and Autocorrelation Consistent Covariance Matrix Estimation", Econometrica, 59,817-858. I saw R has functions that employ Andrews' plug-in method using an