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Dear R users, I am doing MCMC using Metropolis-Hastings. My model is bivariate-log-normal and the prior for variance-covariance is wishart distribution. I am wondering if there are some simple codes about how to get the density of Wishart distribution in my case ? Thanks in advance. Meihua [[alternative HTML version deleted]]

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Hello everyone I was wondering how would one simulate from a Normal Wishart Distribution in R. A normal inverted Wishart distribution is denoted by NIW (M,C,d,S), where X/(Sigma) ~ N( M,C,(Sigma) ) -> a matrix normal distribution, (Sigma) -> column dispersion matrix (Sigma) ~ IW (d,S) -> inverted Wishart distribution Thanks a lot ! Best Shantanu [[alternative HTML version

If so, I''ve had trouble finding it. Can anyone help?

I have an upcoming JASA paper with an iid sampling algorithm for Bayesian Generalized Linear models (e.g., Logit, Poisson Regression, and Conditional Logit models with multivariate normal priors). At this point, I have implemented the algorithms in C and hope to make the functions and corresponding source code available through an R package. I have successfully created the code necessary to

Hi, I am not sure if this make sense at all. I'd like to sample a matrix, which follows a wishart / inverted wishart distribution. However, the (1,1) element of this matrix should always be equal to 1. How can I handle it in R? Any suggestion is greatly appreciated. Thanks a lot. Sonia [[alternative HTML version deleted]]

R users- In moving from R 2.0.0 to R 2.1.0 in Windows, I have encountered a problem with the "riwish" command in the package "MCMCpack". I've searched the documentation and can't seem to figure it out. For example: Define a matrix: > lam <- matrix(c(.00233,-.00057,-.00057,.00190),2,2) and then use the riwish command to generate a random inverse-Wishart

Hi Thanks for replying my question! What really interested me is that the package providing some complex form sampling, such as wishart, multinomial, dirichlet. And others for example conditional beta distribution confining the random variable in the interval (a, b). Since these concept are widely used in the baysian, I wonder whether somebody has already written this package. Thanks! Best

Has anyone got/seen Python code to read puppet reports? I added a bunch of these: class PuppetReport(yaml.YAMLObject): yaml_tag = u''!ruby/object:Puppet::Transaction::Report'' def __init__(self, host, logs, metrics, records, time): self.host = host self.logs = logs self.metrics = metrics self.records = records self.time = time However, the Python YAML

Hello, I am a chemical student and I make use of 'lme/lmer function' to handle experiments in split-plot structures. I know about the mcmcsamp and I think that it's very promissory. I would like knowing "the concept behind" of the mcmcsamp function. I do not want the C code of the MCMCSAMP function. I would like to get the "pseudo-algorithm" to understanding that

Hola erreros Estoy rizando el rizo con esto de las matrices sparse y he encontrado la forma de hacerlo en python, que va rapidísimo. Por tanto tengo una matriz sparse en un objeto python con esta info: <138493x26744 sparse matrix of type '<type 'numpy.int64'> Quiero hacer lo mismo con R pero me he topado con la lentitud de los bucles para rellenar la sparse (también por mi

On 04/06/18 13:51, Ulf Volmer wrote: > On 06.04.2018 18:25, m.roth at 5-cent.us wrote: >> CentOS 7 box. As there's no package in any of the repos, we're trying to >> install scikit-learn in the user's space. It refuses. My late try was, >> after d/l a .whl from last year, hoping that would work with the numpy >> package in the regular repos, I did a pip

I want to generate a valid variance-covariance matrix. One way could be to generate some random sample from multivariate normal distribution and then calculate cov. matrix. Another way could be to sample from wishart distribution itself. However both cases need a valid i.e. PD covariance matrix. As I need to generate that covariance matrix only, I am not interested those two methods. Can anyone

Dear useRs, I have written a function that implements a Bayesian method to compare a patient's score on two tasks with that of a small control group, as described in Crawford, J. and Garthwaite, P. (2007). Comparison of a single case to a control or normative sample in neuropsychology: Development of a bayesian approach. Cognitive Neuropsychology, 24(4):343?372. The function (see

Respected R Users, I looking for help with generating theoretical confidence regions for any of non-symmetric bivariate statistical distributions (bivariate Chi-squared distribution<Wishart distribution>, bivariate F-distribution, or any of the others). I want to to used it as a benchmark to compare a few strategies constructing confidence regions for non-symmetric bivariate data. There is

Hello, I don't know if this question ought to go here, or rather on R-devel, so please bear with me. I'm interfacing to R via RPy (rpy.sf.net) and an embedded Python interpreter. This is really quite convenient. I use this approach to calculate the correlation coefficient of 1 independent dataset (vector) with 4 dependent vectors. It'd be nice if that could be done in 4 parallel

Hello, I don't know if this question ought to go here, or rather on R-devel, so please bear with me. I'm interfacing to R via RPy (rpy.sf.net) and an embedded Python interpreter. This is really quite convenient. I use this approach to calculate the correlation coefficient of 1 independent dataset (vector) with 4 dependent vectors. It'd be nice if that could be done in 4 parallel

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

Valeri Galtsev wrote: <snip> >>> On Fri, 6 Apr 2018 17:25 , <m.roth at 5-cent.us> wrote: >>> >>>> CentOS 7 box. As there's no package in any of the repos, we're trying >>>> to install scikit-learn in the user's space. It refuses. My late try was, >>>> after d/l a .whl from last year, hoping that would work with the numpy

I am receiving the above error ( full r session output below) the script runs OK in windows. and "genotypes" and "ploidy" are both correct arguments any suggestions would be most welcome Nevil Amos MERG/ACB Monash University School of Biological Sciences > library(Geneland) Loading required package: RandomFields Loading required package: fields Loading required