similar to: how do I make a correlation matrix positive definite?

Hi, Is there an R library that has the same functionalities of Splus7.0+ library correlatedData? I'd appreciate any input. Hakan Demirtas [[alternative HTML version deleted]]

Hi, Is there any R library/package that calculates tetrachoric correlations from given marginals and Pearson correlations among ordinal variables? Inputs to polychor function in polycor package are either contingency tables or ordinal data themselves. I am looking for something that takes marginal distributions and Pearson correlation as inputs. For example, Y1=(1,2,3) with P(Y1=1)=0.3,

Hi, Is there any Fortran compiler that generates R-compatible object files ? (For Windows based systems) Thanks, Hakan Demirtas Pennsylvania State University Department of Statistics [[alternate HTML version deleted]]

Hi, Is there any R library that is capable of handling polygamma function (Hurwitz zeta function also works)? I am aware of digamma(0 and trigamma(), but could not find more advanced versions. I'd appreciate any help. Hakan Demirtas

Didn't get any useful response to the following question. Trying again. -------------------------------------------------------------------------------- I can't seem to get computationally stable estimates for the following system: Y=a+bX+cX^2+dX^3, where X~N(0,1). (Y is expressed as a linear combination of the first three powers of a standard normal variable.) Assuming that E(Y)=0 and

1. Martin Maechler's comments should be taken as replacements for anything I wrote where appropriate. Any apparent conflict is a result of his superior knowledge. 2. 'eigen' returns the eigenvalue decomposition assuming the matrix is symmetric, ignoring anything in m[upper.tri(m)]. 3. The basic idea behind both posdefify and nearPD is to compute the

I am creating covariance matrices from sets of points, and I am having frequent problems where I create matrices that are non-positive definite. I've started using the corpcor package, which was specifically designed to address these types of problems. It has solved many of my problems, but I still have one left. One of the matrices I need to calculate is a cross-covariance matrix. In other

Hi, Suppose x and y are vectors whose elements are known. I know that there is a sinusoidal relation between them. In other words, y=a*sin(bx) where b is probably a function of pi. How do I find a and b in R? Hakan [[alternative HTML version deleted]]

Does lmer do three-level mixed-effects models? What forms of outcome variables can it handle (continuous, survival, binary)? I'd appreciate any help.

Hi, What R libraries should I use to implement mixed effects models with continuous time and discrete-time survival data? What if I have two crossed random effects? I'd appreciate any help. Regards, Hakan Demirtas [[alternative HTML version deleted]]

Dear fellow R Users: I am doing a Cholesky decomposition on a correlation matrix and get error message the matrix is not semi-definite. Does anyone know: 1- a work around to this issue? 2- Is there any approach to try and figure out what vector might be co-linear with another in thr Matrix? 3- any way to perturb the data to work around this? Thanks for any suggestions.

hello all, i'm trying to use QPmat, from the popbio package. it appears to be based on solve.QP and is intended for making a population projection matrix. QPmat asks for: nout, A time series of population vectors and C, C constraint matrix, (with two more vectors, b and nonzero). i believe the relevant code from QPmat is: function (nout, C, b, nonzero) { if (!"quadprog" %in%

Hi, I am trying to implement Higham's algorithm for correcting a non positive definite covariance matrix. I found this code in R: http://projects.cs.kent.ac.uk/projects/cxxr/trac/browser/trunk/src/library/Recommended/Matrix/R/nearPD.R?rev=637 I managed to understand most of it, the only line I really don't understand is this one: X <- tcrossprod(Q * rep(d[p], each=nrow(Q)), Q) This

I have a covariance matrix that is not positive semi-definite matrix and I need it to be via some sort of adjustment. Is there any R routine or package to help me do this? Thanks, Roger [[alternative HTML version deleted]]

Thank you for your reply. Do you have any idea of how to get rid of the errors? I tried Null function to calculate eigenvectors and nearPD to get approximate positive definite matrix first but they also had errors. -- View this message in context: http://r.789695.n4.nabble.com/error-code-1-from-Lapack-routine-dsyevr-tp4702571p4702639.html Sent from the R devel mailing list archive at

Hi. I am using R 2.3.1 on Windows XP. I had installed a library package into my first session and wanted the same package in my second session, so I went out to the CRAN mirror and tried to install the package, and got the following message: ********************************************************************* >utils:::menuInstallPkgs() trying URL

I apologise for not including a reproducible example with this query but I hope that I can make things clear without one. I am fitting some finite mixture models to data. Each mixture component has p parameters (p=29 in my application) and there are q components to the mixture. The number of data points is n ~ 1500. I need to select a good q and I have been considering model selection methods

Hello, I'm attempting to create a data frame with correlations between every pair of variables in a data frame, so that I can then sort by the value of the correlation coefficient and see which pairs of variables are most strongly correlated. The sm2vec function in the corpcor library works very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 =

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance through the R-help

Dear all, Has someone implemented in R (or any other language) Knol DL, ten Berge JMF. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika, 1989, 54, 53-61. or any other similar algorithm? Best regards Jens Oehlschl?gel Background: I want to factanal() matrices of polychoric correlations which have negative eigenvalue. I coded Highham 2002