similar to: RFC: d/p/q/rgamma

(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

On a recent FreeBSD 8.0-CURRENT (i386) building R (any version) breaks with the following messages: ---------------------------------------------------------------------- [...snip...] gcc -std=gnu99 -I. -I../../src/include -I../../src/include -I/usr/local/include -DHAVE_CONFIG_H -g -O2 -c wilcox.c -o wilcox.o gcc -std=gnu99 -I. -I../../src/include -I../../src/include -I/usr/local/include

Full_Name: Version: 2.7.1 (2008-06-23) OS: windows vista Submission from: (NULL) (216.82.144.137) Hello, I have been working with various probability distributions in R, and it seems the gamma distribution is inaccurate for some inputs. For example, qgamma(1e-100, 5e-101, lower.tail=FALSE) gives: 1.0. However, it seems this is incorrect; I think the correct answer should be

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

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,

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]

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

Hi I need to find the Hessian matrix for a complicated function from a certain kind of data but i keep getting this error Error in f1 - f2 : non-numeric argument to binary operator the data is given by U<-runif(n) Us<-sort(U) tau1<- 2 F1tau<- pgamma((tau1/theta1),shape,1) N1<-sum(Us<F1tau) X1<- Us[1:N1]

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

Folks, I'm fairly sure that I'm doing something stupid, but I'm getting a few really strange results from *some* of the distributions, but by no means all, when I link directly to the R shared library. I've tried this on both Windows with the precompiled Mingw binary of R-2.5.0 (compiling my code with MinGW-3.4.2), and by building R-2.5.0 on Mandriva Linux with gcc-3.4.4 and

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

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]]

--WWm7B+u2U4 Content-Type: text/plain; charset=us-ascii Content-Description: message body text Content-Transfer-Encoding: 7bit G'day all, I believe that this is related to PR#1717 (filed under not-reproducible) which was reported for a version of R that is a quite a bit older than the ones used in for this report. But I noticed this behaviour under R 2.1.1 and R 2.2.0 on my linux box 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 want to create upper and lower 95% confidence intervals for a p-p plot of an empirical distribution with a theoretical gamma distribution. This is my code: x<-rgamma(100,shape=2, rate=1) # empirical data fitdistr(x,"gamma") # fit a gamma distribution dist<-pgamma(x,shape=1.9884256 ,rate=0.8765314 ) # fitted distribution, using the loglikelihood estimated parameters

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Hi! I have just downloaded the R-0.60.1 sources and have problems compiling R on a Sun Ultra 1 running Solaris 2.6 and gcc 2.7.2.1. I have not been able to find to find any compiling hints in the documentation or the FAQ. After ./configure I use make and get the output below. Any hints are welcome. I am not on the list, so please answer me directly too. Best regards Jens --- Jens Lund

David, Duncan, > Hi > > Following on David's rate argument > > try (with modifications of pch and grid) > > rate <- 1/4 > shape = 8 > rate = c(rep(1/4,100),rep(1/3,100)) I don't think the problem is related to the rate argument, which can well be vectorized, as is the case for a number of arguments in distrib functions in R (note that you are redefining it

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)