similar to: R CMD check

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 code/documentation mismatches ... WARNING

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

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

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 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

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.

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 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

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

Dear People, I want to run gcc with optimisation turned on (-O2), and with -Wall (all warnings) enabled, when using R CMD SHLIB. When I do make, which is R CMD SHLIB -Wall -O2 cftp.c mcmc.c latticefn.c -lm in this case, I get faheem ~/research/cftp>make R CMD SHLIB -Wall -O2 cftp.c mcmc.c latticefn.c -lm make[1]: Entering directory `/home/faheem/research/cftp' gcc -I/usr/lib/R/include

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

I have hit a problem with the design of the mcmc package I can't figure out, possibly because I don't really understand the R function call mechanism. The function metrop in the mcmc package has a ... argument that it passes to one or two user-supplied functions, which are other arguments to metrop. When the two functions don't have the same arguments, this doesn't work.

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

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:

What's the current best wisdom on how to construct confidence bounds on something like a normal probability plot? I recall having read a suggestion to Monte Carlo something like 201 simulated lines with the same number of points, then sort the order statistics, and plot the 6th and 196th of these. [I use 201 not 200 because quantile(1:201, c(0.025, 0.975)) = 6 and 196 while

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

Hi folks, I have assumed that ratios of variance components (Fst and Qst in population genetics) could be estimated using the output of mcmcsamp (the series on mcmc sample estimates of variance components). What I have started to do is to use the matrix output that included the log(variances), exponentiate, calculate the relevant ratio, and apply either quantile or or HPDinterval to get

Dear list, How do you delete repeated samples? In MCMC, when your candidate value has been reject, so you remain on the same point, so you keep that value. Say I have this toy example, > c(1,6,6,6,3,5,4,4,2,3,5) The 6 and 4 are repeated, I only want the index of the non-repeated values. I thought of using which() and unique(), but that does not give you the index of the unique values.