similar to: R CMD check

[Sent to R-help by mistake. Sorry] I have been checking my package coda with the current R-devel using "R CMD check". As usual, this has uncovered a large number of errors and inconsistencies in my documentation, for which I am very grateful. The only problem I have is with code/documentation mismatches when I have written a method for a generic function, e.g. * checking for

I have an mcmc object and I''m trying to plot the quantiles of the variables - and not as a function of the iterations as in cumuplot. I cannot seem to find the right combination of indexing to access the variables; after which I''m sure I can plot all the statistics I could hope for. Any hints for accessing the mcmc object would be appreciated. =Dave [[alternative HTML

The document R-exts contains the following example of using mathematics in R documentation. \deqn{p(x) = {\lambda^x\ \frac{e^{-\lambda}}{x!}} {p(x) = lambda^x exp(-lambda)/x!} There is a syntax error in there, but that's not my point. The problem is that using "R CMD Rd2dvi" I find that putting the alternate forms of the equation on top of each other doesn't work.

Hi, I want to compute the quantiles of Chi^2 distributions with different degrees of freedom like x<-cbind(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975, 0.99, 0.995) df<-rbind(1:100) m<-qchisq(x,df) and hoped to get back a length(df) times length(x) matrix with the quantiles. Since this does not work, I use x<-c(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975,

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

Dear R users: I am using the package coda (the last verison in CRAN) to analyse the output from a MCMC Bayesian analysis. And I get unconsitented results. I have export the chain using the read.table function and after I have transformed this data frame to an mcmc object using the mcmc function. I am interested in three variables, when I use the function effectiveSize I have these figures:

I have been running a Gibbs Sampler to estimate levels of efficiency in the Louisiana Shrimp Industry. I created a matrix (samp) where I stored the results of each iteration for 86 variables. I run 10,000 iterations. So, the matrix samp is 10,000 x 86. I want to use the gelman-rubin test to check for convergence. To do that, I need at least two chains. If I run second chain with different starting

Dear all, I am running a mixed model using lmer. In order to obtain CI of individual coefficients I use mcmcsamp. However, I need advice which values that are most appropriate to present in result section of a paper. I have not used mixed models and lmer so much before so my question is probably very naive. However, to avoid to much problems with journal editors and referees addicted to

Hola. Tengo un problema con el formato de salida de un objeto "numeric", que los expresa en formato científico o exponencial. > range(total$ImpTotal) [1] 5.66 806907887.10 > Valores <- quantile(total$ImpTotal, c(0, 0.01, 0.02, 0.025, 0.03, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.96, 0.97, 0.975, 0.98, 0.99), na.rm=TRUE) > class(Valores) [1] "numeric" >

Hello R users I am quite new to bootstrapping. Now, having some data x, ---- R: set.seed(1234) R: x <- runif(300) ---- I want to bootstrap simple statistics, mean and quantiles (.025, .975). Currently, I run a loop ---- R: res <- as.data.frame(matrix(ncol = 3, dimnames = list(NULL, ... c("M", "Lo", "Hi")))) R: for (i in 1:100) { ... y <-

I am an economist who decided it's high time that I learned some Bayesian statistics. I am following An Introduction to Modern Bayesian Econometrics by T. Lancaster. The book recommends using BUGS, but I wonder if there are any alternatives which are free software and fully integrated to R (which I have been using for more than two years for numerical computations.) I would like to learn

i am having problem running my own data. yesterday it was working just fine. today it is not. this is the code i was using as an example to follow. this code ALSO worked just fine yesterday, and is no longer working at all. i suspect it is a problem with either my computer or the software, at this point. if THIS won't even run.... something is wrong. i can assure you this isn't

The function 'wilcox.test' in R and S gives (almost) identical results (see below). 'qwilcox' however, does not: > qwilcox(p,5,5) p: 0.025 0.975 -------------------- R> 3 22 S> 18 37 I originally wanted to ask a questions, but then I found the answer. Given the confusion I run into, I wonder if this experience is worth reporting. The

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

Dear All, What is the preferred way to get Bayesian analysis results (such as those from MCMCpacki, MCMCglmm, and DPpackage) into LaTeX table automatically? I have been using the "apsrtable" package and similar functions in "memisc" package, but neither seems to handle MCMC output directly. Many thanks. Shige

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 =

Hi Does anyone know of any R coding/functions for MCMC approaches? I am currently using BUGS but I wonder if the bazaar has produced anything? I think I am pushing BUGS to it's limit and possibly past it at the moment. John -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Greetings - For a number of reasons, I'm moving from CODA to BOA - and I have one or two really basic, boa-newbie questions. While I have the 'menu-driven' version of boa working fine (most recent version, running under R 2.3.1 on a Windows machine), for the life of me I can't seem to get some basic boa.xxxx command-line functions to work at all. Even things like

Dear List members, I am using R to generate MCMC time series plots. This is the code I use; however, everytime an error message will come out saying could not find function "plot.mcmc." I have coda package and Lattice package installed. Does anyone know what may get wrong here? Thanks so much for your suggestions. Hongli Li *library (coda) dat1=read.csv("Itemtime.csv")

Dear R-helpers, Hi! All. I'm doing a project which needs MCMC simulation. I wonder whether there exists related packages in R. The only one I know is a MCMCpack package. What I want to do is implementing gibbs sampling and Metropolis-Hastings Algorithm to get the posterior of hierarchical bayesian models. Thanks in advance. Jun