similar to: new dgamma rate argument

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

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

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Hi there, Could somebody help me disect this legacy R script I inherited at work, I have two questions: 1. I've tried to upgrade our R version from 1.6.2 (yeah, I know), to R 2.0, but some of the lines in this script are not compatible with R 2.0, could someone help me figure out where the problem is? 2. the jpeg generated (attached) seems to be off on some of the data, is there a better way

Hi, All, I am getting some funny results trying to use R's built in distribution functions. In R: > dgamma(4.775972,1.37697964405418, 0.106516604930466) [1] 0.05585295 > dgamma(4.775972,1.37697964405418, 0.106516604930466,TRUE) ### THIS IS JUST WRONG! [1] 0.01710129 > log(dgamma(4.775972,1.37697964405418, 0.106516604930466)) [1] -2.885033 > In C:

Hi All, Here 's what I got using dgamma function : > nu<-.2 > nu*log(nu)-log(gamma(nu))+(nu-1)*log(1)-nu*(1) [1] -2.045951 > dgamma(1,nu,nu,1) [1] 0.0801333 > dgamma(1,nu,nu,0) [1] NaN Warning message: NaNs produced in: dgamma(x, shape, scale, log) Could anyone tell me what is wrong here ? I am using R-2.4.1 on windows XP. Thanks a lot.

I define priors in jags within r using a gamma distribution. I would like to control the shape but I have problems. Any help will be usefull. From help of dgamma ___________________ The Gamma distribution with parameters shape = a and scale = s has density f(x)= 1/(s^a Gamma(a)) x^(a-1) e^-(x/s) and rate=1/scale From jags user manual ____________________ dgamma(r, mu) has a density of

Dear stats experts: Me and my little brain must be missing something regarding bootstrapping. I understand how to get a 95%CI and how to hypothesis test using bootstrapping (e.g., reject or not the null). However, I'd also like to get a p-value from it, and to me this seems simple, but it seems no-one does what I would like to do to get a p-value, which suggests I'm not understanding

Dear R-Devel group, My name is Alexey, a data scientist from Moscow, currently working for Align Technology Inc. We have recently had a discussion of the results that the dgamma function (stats) returns for an extreme point (x == 0). <dgamma(0,1,1,log = FALSE) [1] 1 and <dgamma(0,0.5,1,log = FALSE) [1] Inf Density appears to be defined in point zero for the distribution with the

Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with *seq()* *theta<-seq(0,1,len=500)* #Then I try out the

Hello, In WinBUGS 1.4 manual (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/manual14.pdf), the gamma density is presented as dgamma(r,mu) where r and mu are the shape and rate parameters, respectively. In JAGS (rjags) manual version 1.0.2, May 9, 2008 (http://www-fis.iarc.fr/~martyn/software/jags/jags_user_manual.pdf), on page 26 the gamma density is presented as dgamma(mu,r) instead of dgamma(r,mu).

(Sent this to r-help back in October, got no comments, forgot to re-submit it as a bug report.) There are a few inconsistencies, at least, in some of the functions that generate random deviates from particular distributions (I think they're bugs because they're inconvenient, but maybe someone can make an argument for the current behavior). If people think these are really bugs I can

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

I was trying to generate random numbers with a gamma distribution. In R the function is: rgamma(n, shape, rate = 1, scale = 1/rate). My question is that if X~gamma(alpha, beta) and I want to generate one random number where do I plug alpha and beta in rgamma? and, what is the meaning and use of rate? Thanks for your attention, Jorge [[alternative HTML version deleted]]

Dear R-List members, I want to check if a set of measurements follows better a gamma or a lognormal distribution (see data below). Using shapiro.test I can test for normality (shapiro.test(log (Lt)). To test for gamma (and normal) distribution I would use ks.test but I need to specify its shape and scale. How should I calculate these values in R? I tried > Lt.fit <- glm(Lt ~ 1,

Hi all I have a couple of obscure questions for R/Splus experts (which unfortunately isn't me!) I am trying to compute Bayes Factors using some Splus code of Raftery in Gilks et al (1996). Only problem is 1) R doesn't seem to have a robust covariance (cov.mve) which I suspect I need rather than a non-robust classical estimate 2) Splus has cov.mve BUT dgamma in Splus doesn't have a

There's an apparent inconsistency between the behavior of d(pqr)gamma and other distribution functions for "bad" parameter values. Specifically, most distributions give NaN and a warning for bad parameters (e.g. probabilities <0 or >1). In contrast, d(pqr)gamma actually gives an error and stops when shape<0. I don't see why it has to be this way -- the internal C code

Hi, In src/nmath/dgamma.c the comment at the top says * DESCRIPTION * * Computes the density of the gamma distribution, * * 1/s (x/s)^{a-1} exp(-x/s) * p(x;a,s) = ----------------------- * (a-1)! * * where `s' is the scale (= 1/lambda in other parametrizations) * and `a' is the shape parameter ( = alpha in

Hi everyone, I have built an R package and for the sake of speed I have decided to rewrite some part of the code in C++. In my original R code I use the pnorm, qnorm, rnorm, pgamma, dgamma, rgamma, rbeta and runif function. First I was thinking in going with the boost libraries, but I noticed the functions described above are available within the R.h header file (or is it Rmath.h?). So my

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