similar to: Maximum likelihood estimate of bivariate vonmises-weibull distribution

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate

Hello, Consider MCMC sampling with metropolis / metropolis hastings proposals and a density function with a given valid parameter space. How are MCMC proposals performed if the parameter could be located at the very extreme of the parameter space, or even 'beyond that' ? Example to express it and my very nontechnical 'beyond that': The von Mises distribution is a circular

Full_Name: Aziz Chaouch Version: 2.2.1 OS: XP/2000 Submission from: (NULL) (132.156.89.240) Hi, I'm not sure this is a "bug" but here is the problem: I'm using the function as.POSIXlt to convert character strings into time objects. I'm using date format as "YYYY/M/D HH:MM" such as as.POSIXlt("1999/6/7 13:30"). Most of the time, this works fine. However

Hi,Dear all R experts, As far as I know, fitdistr() is only to estimate the parameters in univariate distributions. I have a set of data (x,y) and I assume it follows a bivariate weibull distribution. Could someone tell me a function in R that is suitable for parameter estimation in multivariate cases? Thanks in advance! Cheers, YAN

"Jenny Stadt" <jennystadt at yahoo.ca> asked: > > I am struggling in a bivariate Weibull distribution although I > searched R-Site-Help and found suggestion with Copula. Seems the > maximum likelihood estimate is beyond what I can understand. > > My case is: given two known marginal distribution (both are Weibull), > and the correlation between them. How can I

Dear r-helpers, I want to show that time is flowing CCW in the following: require(circular) len <- 8 labl <- as.character(c(0, 1, 1, 1, 0, 0, 1, 0)) r <- circular(2*pi* (rep(c(1, 3, 6), each = 200)/len + rnorm(600, 0, 0.025))) r.dens <- density(r, bw = 25, adjust = 4, kernel = 'vonmises') plot(r, shrink = 2.5, axes = FALSE, ticks = FALSE, pch = 1, col =

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Hi All, I'd like to compute a kind of reliability index (RI) that would in a sense stand as a measure of reliability of a statistic (histogram etc) computed on a time serie with missing values. The final goal is that: RI=1 for a perfect reliability RI=0 for a total unreliability (no data at all as an extreme case...) The percentage of missing data is one indication: the more missing data,

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

Hi, I have a Ruby on Rails application. I am able to run it in my local system using Apache2 server. I would like to host my application so that all the systems in the private network 192.168.3.0/24 should be able to access the web-application. My system private-ip is 192.168.3.57. Please help me what all configurations do I need to do. Thank you Ajit -- Posted via

Hello, I installed RGui 2.5.1 and package DBI on Windows XP and tried to connect to Oracle database which is on a Linux server. When I tried to use dbDriver("Oracle"), I got an error as follows: > drv <- dbDriver("Oracle") Error in do.call(as.character(drvName), list(...)) : could not find function "Oracle" > Could anyone tell me how

Hall all, Where can I find the two sample Cramer-von Mises test in R package? Thank you. Legendy -- View this message in context: http://www.nabble.com/Two-sample-Cramer-von-Mises-test-tp20174229p20174229.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I'm curious if anyone has coded an Expectation-Maximization algorithm that could help me model some circular data I have. I'd like to model it as a mixture of uniform and Von Mises centered on 0, so the only free parameters is the mixing proportion and the kappa of the Von Mises. I couldn't find anything in the contributed packages that seemed to suit this purpose. Any

Does anyone know of some code, and examples that implement game theory/Nash equilibrium hypothesis testing using existing packages like igraph/statnet or similar? Perhaps along the lines of this article: Zhang, Y., Aziz-Alaoui, M. A., Bertelle, C., & Guan, J. (2014). Local Nash Equilibrium in Social Networks, *4*, 6224. Best, Chris Buddenhagen cbuddenhagen at gmail.com [[alternative HTML

I'm getting errors when running what seems to be a simple Weibull distribution function: This works: x <- c(23,19,37,38,40,36,172,48,113,90,54,104,90,54,157,51,77,78,144,34,29,45,16,15,37,218,170,44,121) rate <- c(.01,.02,.04,.05,.1,.2,.3,.4,.5,.8,.9) year <- c(100,50,25,20,10,5,3.3,2.5,2,1.2,1.1) library(MASS) x <- sort(x) tryCatch( f<-fitdistr(x, 'weibull'), error

Hello, I'm quite new to R and want to make a Weibull-regression with the survival package. I know how to build my "Surv"-object and how to make a standard-weibull regression with "survreg". However, I want to fit a translated or 3-parametric weibull dist to account for a failure-free time. I think I would need a new object in survreg.distributions, but I don't know how

Hi all I try to make a weibull survival analysis on R. I know make this on GLIM, and now I try to make the GLIM exercice GLEX8 on R to learning and compare the test. The variables are: time censor group bodymass In GLIM I make: $calc %s=1 $ to fit weibull rather than exponential $input %pcl weibull $ $macro model group*bodymass $endmac$ $use weibull t w %s $ Then, GLIM estimate an alpha for the

Hello What is the appropriate method to test if a given distribution is a weibull thank you meriema email meriema.belaidouni at int-evry.fr

Hello, is there a quick way of estimating Weibull parameters for some data points that are assumed to be Weibull-distributed? I guess I'm just too lazy to set up a Maximum-Likelihood estimation... ...but maybe there is a simpler way? Thanks for any hint (and yes, I've read help(Weibull) ;) Kaspar Pflugshaupt -- Kaspar Pflugshaupt Geobotanical Institute ETH Zurich, Switzerland

Dear list, dear Frank, I try to fit a Weibull survival regression model with package Design: sclear <- psm(sobj~V1+V2,dist="weibull") sobj is a one-dimensional survival object (no event indicators), V1 and V2 are factors. I get the following result: Parametric Survival Model: Weibull Distribution psm(formula = sobj ~ V1 + V2, dist = "weibull") Obs Events