similar to: How to define a proper prior for an orthogonal matrix?

Hey, R-listers I was recommended to try using smooth.spline function for estimating 2-Dimensinal curve given a data set. So will you please tell me where to get this R function? Or which package provides this function? Thanks for your point. Fred

Hi, R-listers I tried to plot several graphs in a sigle x-y coordinate settings, like the following: |(y) s | ****** s | ***** s | sssssssssssssssssss |_______________________________(x) where "*" and "s" denote two diffrent plots. However, when I used plot(data1); % data1 is the data points of "*"

Hey, R-listers, I have a question about determining the orthogonal basis vectors. In the d-dimensinonal space, if I already know the first r orthogonal basis vectors, should I be able to determine the remaining d-r orthognal basis vectors automatically? Or the answer is not unique? Thanks for your attention. Fred

Dear R-listers, I have a dxr matrix Z, where d > r. And the product Z*Z' is a singular square matrix. The problem is how to get the left inverse U of this singular matrix Z*Z', such that U*(Z*Z') = I? Is there any to figure it out using matrix decomposition method? Thanks a lot for your help. Fred

Hey, I want to draw several plots sequently, but have to make them dispaly in one figure. So how to achieve this? Thanks. Fred

I'm rusty, but not *that* rusty here, I hope. If W (=Z*Z' in your case) is singular, it can not have inverse, which by definition also mean that nothing multiply by it will produce the identity matrix (for otherwise it would have an inverse and thus nonsingular). The definition of a generalized inverse is something like: If A is a non-null matrix, and G satisfy AGA = A, then G is called

Dear Fred, If x1 and x2 are *not* normally distributed, you can use independent component analysis (ICA) which is based on the idea that x will be "more normal" than either x1 and x2 following the central limit theorem. See package(fastICA) by JL Marchini, C Heaton, and BD Ripley for details. HTH Thomas > -----Original Message----- > From: Feng Zhang [mailto:f0z6305 at

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

Hey, all Will you please tell me how to generate multiple square orthogonal matrices for data transformation usage? Thanks. Fred

Hey, all Now I am using EM algorithm to do some optimization. Within that E-step, I have to calculate some multivariate integration given some parameter values Theta and function form of a probability density function f. So I want to know if there are some package in R to do such numerical integration given such function form and parameter values? Thanks for your kind support on this. Have a

hi, R users here is my problem, i want to make a clusterring tree, than do the searching through the tree using branch and bound method. the code to make the tree, but i dont know how to do the searching part. thanks for any helping... d <- dist(data, method = "euclidean") h1 <- hclust(d, method="ward") str(h1) plot(h1) mark -- View this message in context:

hi, R users i have encountered a problem, i want to make a clusterring tree, than do the searching through the tree using "branch and bound" method. here is the code to make the tree, thanks for any ideas... d <- dist(data, method = "euclidean") h1 <- hclust(d, method="ward") plot(h1) mark -- View this message in context:

Hi all, May i know does R has packages or code to run "Bayesian Relative Survival Analysis"? I have look through Bayesian Survival Analysis(2001) by Joseph George Ibrahim<http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Joseph+George+Ibrahim%22>, Ming-Hui Chen<http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Ming-Hui+Chen%22>, Debajyoti

Dear R listers, Just a simple question. If we say an nxm matrix (n>m) is full rank of m, does this mean that this matrix has linearly independent columns? They are the same definition or needs some proof? Thanks for your answer. Fred [[alternative HTML version deleted]]

Hey, all Do you how to calculate the trigamma function, that is d**2(log(gamma(x))) / dx**2. The second-order derivative of log(Gamma(x))? I cannot find it in the R package, and somebody knows who or where to get such one? Thanks. Fred -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

Hi, I have a situation where I have a set of pairs of X & Y variables for each of which I have a (fairly) well-defined PDF. The PDF(x_i) 's and PDF(y_i)'s are unfortunately often rather non-Gaussian although most of the time not multi--modal. For these data (estimates of gas content in galaxies), I need to quantify a linear functional relationship and I am trying to do this as

Assume F(x) is the cdf of stardard normal c.d.f, and want to get the integration of F(x)^2 over (-infinite, +infinite). So whats the value of this integration? And is there some function to achieve this? Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hey, R-listers I want to get some statistical books on Cumulants for studying. So will you please give me some suggestions on these related books? Thanks for your point. Fred

Hello, How I can compute in R the orthogonal complement of one matrix? If A (n x m ) matrix of full column rank (n>m), its orthogonal complement is denoted by A_ . A_ is n X (n-m) matrix of full column rank and such that A'A_=0. I need to compute A_. How I can compute A_ in R? Best Regards, /Florin -- Florin G. Maican Ph.D. candidate, Department of Economics School of