similar to: rgamma question

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 friends, I run the Gelman-Rubin Convergence test for a MCMC object I have and I got the following result Multivariate psrf 1.07+0i, What does this mean? I guess (if I am not mistaken) that I should get a psrf close to 1.00 but what is 1.07+0i? Is that convergence or something else? Jorge [[alternative HTML version deleted]]

Dear friends, Over the past weeks, I have been asking a lot of questions about how to use R in Bayesian analysis. I am brand new to R, but I am very pleased with it. I started with winbugs but I found winbugs to be a limited software, not bad but has several limitations. By contrast, R allows the analyst to tackle any problem with a huge set of tools for any kind of analysis. I love R. In

Hi, Has anyone encountered the problem of rgamma function in C? The following simplified program always dies for me, and I wonder if anyone can tell me the reason. #include <Rmath.h> #include <time.h> #include <Rinternals.h> SEXP generateGamma () { srand(time(NULL)); return (rgamma(5000,1)); } Has anyone encountered a similar problem before? Is there another way

Dear R people, This is a possibly silly question, but the rgamma function takes the shape and scale arguments and simulates gamma rvs corresponding to those values, right? But the shape and scale parameters have to be positive, right? However, rgamma quite happily returns to me values for negative values of shape and scale, and in some cases returns negative values eg. > rgamma(1, 1, -1) [1]

Estimados Quisiera tener la sintaxis generar una serie de números entre 0 y 1 con distribución gamma de 25 puntos y que la suma de todos los puntos debe dar 1. Por ejemplo: c (0.000, 0.001, 0.012, 0.043, 0.078, 0.104, 0.117, 0.116, 0.108, 0.094, 0.078, 0.063, 0.049, 0.038, 0.028, 0.021, 0.015, 0.011, 0.008, 0.005, 0.004, 0.003, 0.002, 0.001, 0.001) No hemos podido

Hello fellow R users, I wanted to view the density on the standard deviation scale of a gamma(0.001, 0.001) prior for the precision. I did this as seen in the code below and found that for some reason rgamma is giving many values equal to zero, which is strange since a gamma distribution is continuous. What is going on here? Thanks for any help in advance. Greg > x1 <- rgamma(10000,

Estimado Jorge Perdóneme que lo moleste de nuevo, hay otra condición además de que sum(y)=1 y es que y[1] tiene que dar 0 en el ejemplo y<- c (0.0000000000, 0.6321985783, 0.2325728597, 0.0855587737, 0.0314753138, 0.0115791209, 0.0042597205, 0.0015670636, 0.0005764905, 0.0002120790) y[1]=0 sum(y)=1 esto se utiliza en el paquete EpiEstim donde se

dgamma and friends in S are documented as dgamma(x, shape, rate=1) pgamma(q, shape, rate=1) qgamma(p, shape, rate=1) rgamma(n, shape, rate=1) whereas R has dgamma(x, shape, scale=1, log = FALSE) pgamma(q, shape, scale=1, lower.tail = TRUE, log.p = FALSE) qgamma(p, shape, scale=1, lower.tail = TRUE, log.p = FALSE) rgamma(n, shape, scale=1) Note the use of rate vs scale. Indeed, as both S and

Hi - I have a question about the following code from Bayesian Computation with R (Jim Albert). par(mfrow=c(2,2)) m = 500 alphas = c(5, 20, 80, 400) for (j in 1:4) { mu = rgamma(m, shape=10, rate=10) lambda1 = rgamma(m, shape=alphas[j], rate=alphas[j]/mu) lambda2 = rgamma(m, shape=alphas[j], rate=alphas[j]/mu) plot(lambda1, lambda2) title(main=paste('alpha=',

Hi, I'm seeing something that may be a bug in R's standalone math library, which is packaged by Debian as r-mathlib. I reported it to the Debian BTS as http://bugs.debian.org/657573 I'm using Debian squeeze, and the code was tested with r-mathlib 2.11.1-6 (default on stable) and 2.14.1-1 (from testing/unstable). I summarize this report below. The following code with the R math

Hi, I've been trying to get this working for ages, but it causes R to hang. Here is my C code saved as test1.c # include <R.h> # include <Rmath.h> void test1 (double *x, double *result) { result[0] = rgamma(*x, 2.0); } This was compiled using R CMD SHLIB test1.c & loaded in R using: dyn.load("test1.dll") out <-

Hello, I have a data frame that looks like this (containing interarrival times): > str(df) 'data.frame': 18233 obs. of 1 variable: $ Interarrival: int 135 806 117 4 14 1 9 104 169 0 ... > head(df) Interarrival 1 135 2 806 3 117 4 4 5 14 6 1 > This corresponds to the time differences (in ms) of a poisson arrival

Dear R-help readers, I am writing to you in order to ask you a few questions about distribution fitting in R. I am trying to find out whether the set of event interarrival times that I am currently analyzing is distributed with a Gamma or General Pareto distribution. The event arrival granularity is in minutes and interarrival times are in seconds, so the values I have are 0, 60, 120, 180, and

Can someone explain to me in what way the new (dpqr)gamma parameter can be interpreted as a rate (when shape != 1)? The only gamma rate that I am aware of is the hazard rate given by dgamma/(1-pgamma), the log of which is returned by my hgamma function (event library). Jim -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, I have the following vector which is created from 3 distinct distribution (three components) of gamma: x=c(rgamma(30,shape=.2,scale=14),rgamma(30,shape=12,scale=10),rgamma(30,shape=5,scale=6)) I want to plot the density curve of X, in a way that it shows a distinct 3 curves that represent each component. How can I do that? I tried this but doesn't work: lines(density(x)) Please

Hi R Users This is a code I wrote and just want to confirm if the first 1000 values are raw gamma (z) and the next 1000 values are transformed gamma (k) or not. As I get 2000 rows once I import into excel, the p - values beyond 1000 dont look that good, they are very high. -- sink("a1.txt"); for (i in 1:1000) { x<-rgamma(10, 2.5, scale = 10) y<-rgamma(10, 2.5, scale = 10)

Hello! I am trying with this question again: I would like to test few distributional assumptions for some behavioral response data. There are few theories about true distribution of those data, like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The best way would be via qq-plot, to show to students differences. First two are trivial: qqnorm(dat$X)

In glm() you can use the summary() function to recover the shape parameter (the reciprocal of the dispersion parameter). How do you recover the scale parameter? Also, in the given example, how I estimate and save the geometric mean of the predicted values? For a simple model you can use fitted() or predicted() functions. I will appreciate any help. ? ? ? #Call required R packages require(plyr)?

Hi My simulation with the followin R code works perfectly: sim <- replicate(999, sum(exp(rgamma(rpois(1,2000), scale = 0.5, shape = 12)))) But now I do not want to have values in object "sim" exceeding 5'000'000, that means that I am just using the beginning of densitiy function gamma x < 15.4. Is there a possibility to modify my code in an easy way? Thanks for any help!