similar to: Parameterization of Inverse Wishart distribution available in MCMCpack and bayesm libraries

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

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

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

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]]

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]]

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

Hi all, ########################################## dof=c(1,2,4,8,16,32) Q5=matrix(rt(100,dof),100,6,T,dimnames=list(NULL,dof)) par(mfrow=c(2,6)) apply(Q5,2,hist) myf=function(x){ qqnorm(x);qqline(x) } apply(Q5,2,myf) ########################################## These looks ok. However, I would like to achieve more. Apart from using a loop, is there are fast way to 'add' the titles to be

We have an occasional problem which manifests with multiple processes being created for a particular user. For example, for a user "xy004": xy004 8463 0.0 0.0 6.02M 0K ?? IW 11:48:59 0:02.02 smbd xy004 9426 0.0 0.0 5.99M 0K ?? IW 11:52:18 0:03.68 smbd xy004 10433 0.0 0.0 5.81M 0K ?? IW 12:17:20 0:00.85 smbd xy004

I need to fit a t copula with fixed degree of freedom let's say 4. I do not want to estimate the dof together with correlation matrix optimally. Instead fix the dof to 4 and only estimate the correlation matrix in the optimization routine. Is anyone aware of such estimation method in R. The packages and functions that I know of can't do this estimation. I searched online but

Hi everybody, trying to get the Dovecot antispam_plugin to work and I must be doing something wrong, because as soon as it is enabled with a certain backend, imap logins do not work anymore (the session is immediately closed after a successful login). Interestingly, pipe and spool2dir are working (that is, the session won't be closed), dspam-exec and crm114-exec are not. If this happens,

I was writing a simple plugin a while ago which just sets the paint values on inactive windows. The plugin is very simple and the main part is below. When I load this plugin it blocks dbus and fuse plugins so that they only reply to requests when the active window changes. Is there anything obvious here that would cause that? static Bool inactivePaintWindow (CompWindow *w, const

Hello, I'm trying to convert a character column in several dataframes to lower case. ### # # Sample data and 'spp' column summaries: # dput(ban.ovs.1993[sample(row.names(ban.ovs.1993), 20), 1:4]) ban.ovs.93 <- structure(list(oplt = c(43L, 43L, 38L, 26L, 35L, 8L, 39L, 1L, 34L, 50L, 10L, 29L, 31L, 24L, 18L, 12L, 27L, 49L, 28L, 51L), rplt = c(NA_integer_, NA_integer_,

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

Dear R users, I would like to make my R code for MCMC faster. It is possible to integrate C code into R but I think C is too complicated for me. I would need a C introduction only for MCMC and I do not know if such a thing exists. I was thinking of Python (and scipy). Where could I read about its integration into R ? How developed are the statistical packages in Python ? I could not find a

Hi, I'm trying to fit a marginal model via GEE but I'm getting strange results and few problems. If I set the working correlation as exchangeable I'm getting the same fitting when I set as independent. Comparing to SAS results it shouldn't happen. If I try to use another working correlation (like AR-M or stat_M_dep), R just exits without giving any error message. Another doubt

Samba not responding to broadcast calls from networked PCs. Short: My Redhat Linux 8.0 box is not replying to udp broadcasts, and I can't figure out why. Network: The offending Redhat 8.0 Linux Box (//DS) attempting to run Samba 2.2.7 Firewall Redhat 7.3 (//IW) successfully running Samba 2.2.4 Win98 (//MS) Win2k (//SL) <Not running at the time of these tests> Very long: I've

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

I tried toy problems and there doesn't seem to be a basic problem between lm() and ts objects: X = data.frame(x=c(1,2,7,9), y=c(7,2,3,1)) lm(y ~ x, X) X <- lapply(X, function(x) ts(x, frequency=12, start=c(1994,7))) lm(y ~ x, X) and this works fine - whether you do an lm() before or after making ts objects, it's okay. But I have a situation where things aren't okay.

Hi, I''m posting a question hoping someone will know the answer off hand thereby reducing my search time. :-) With USDT probes, the tracepoint is only installed by libdtrace itself, never by the drti ioctl. So whenever I run a program with an USDT probe, no tracepoint is installed. Only after I run the dtrace command the tracepoint is actually installed on the victim process. My question

Folks; I am having a problem with the cv.glm and would appreciate someone shedding some light here. It seems obvious but I cannot get it. I did read the manual, but I could not get more insight. This is a database containing 3363 records and I am trying a cross-validation to understand the process. When using the cv.glm, code below, I get mean of perr1 of 0.2336 and SD of 0.000139. When using a