similar to: sinusoidal relationship

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, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

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 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

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

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]]

Hi All, I have been reading about general purpose GPU (graphical processing units) computing for computational statistics. I know very little about this, but I read that GPUs currently cannot handle double-precision floating points and also that they are not necessarily IEEE compliant. However, I am not sure what the practical impact of this limitation is likely to be on computational

Hello, I will try to explain the problem, sorry if it will be a little long... I'm using R to analyze results of cyclic mechanical testing, like this: - apply quasi-sinusoidal load - measure quasi-sinusoidal vertical and horizontal deformations (quasi-sinusoidal load means that load "should be" sinusoidal, but testing machine puts in some noise...) I enclose a sample of data at

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

Dear Martin, I have been playing a lot with the glkerns() function in the "lokern" package for "automatic" smoothing of time-series data. This kernel smoothing approach of Gasser and Mueller seems to perform quite well for estimating the function and its derivatives (first and second derivatives). In fact, this is one of the best methods based on my simulation studies for

Hi, I am currently trying to execute the following command: f<-factanal(factors=k$Components$nparallel,covmat=m,n.obs=2287,rotation="varimax",control=list(opt=list(method=c("BFGS")))) but keep getting the error: L-BFGS-B needs finite values of 'fn' I can't figure out what I am doing wrong here, why isn't optim being told to use BFGS instead of L-BFGS-B...

I've seen with other software the capability for the optimizer to switch algorithms if it is not making progress between iterations. Is this capability available in optim()? Thanks, Stephen Collins, MPP | Analyst Health & Benefits | Aon Consulting [[alternative HTML version deleted]]

Hi all, could someone send me examples of the EM Clustering algorithm and/or some pdf describing it? Thanks, Douglas Sousa.

Hi, I am trying to solve this equation prob = exp(-3.33 + 0.0102*x)/(1+exp(-3.33 + 0.0102*x)). I want to write a function where I call the function and enter the 'prob' value and the output should be the 'x'. Im not sure how to write this. I have a basic structure but im not sure if its correct. calc <- function(prob){ prob <- exp(-3.33+0.0102*x)/(1+exp(-3.33 + 0.0102*x))

Hi guys Is there a way in R to simulate/generate random numbers from a negative skewed and fat tailed distribution ? I would like to simulate a set of (discrete) data. Regards, Carlos Carlos http://www.nabble.com/file/p25783889/graph.png graph.png -- View this message in context: http://www.nabble.com/Simulate-negative-skewed%2C-fat-tailed-distribution-tp25783889p25783889.html Sent from the

I am currently using the package 'adapt' for multivariate integration. However this package seems to be removed from CRAN (It is still referred to in the help file for integrate(stats) though). I assume it has been deprecated for a reason? Is there an alternative for multivariate numerical integration? ----- Jeroen Ooms * Dept. of Methodology and Statistics * Utrecht University Visit

Dear: I am trying to work out the regularised incomplete beta function in R. I searched google on this and found UCS library. But I can not find this in R packages. Does anyone have use this before or how to insert UCS in R? Many Thanks! Xin Xin ------- Dr.Xin Shi Senior Lecturer in Statistics Manchester Metropolitan University Business School Aytoun Building Aytoun Street Manchester M1