similar to: generating a gamma random variable

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]

I'm looking for goodness of fit tests for gamma distributions with large data sizes and for different data. I have a matrix with around 4.000 data values in it and i have fitted a gamma distribution with "fitdistr". You can see the example: > fitdistr(corpo,"gamma",lower=0.001) Errore in optim(x = c(5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, :

Full_Name: Ray Kiddy Version: R 2.5.0 GUI 1.19 (4308) OS: Mac OS X 10.4.9 Submission from: (NULL) (71.139.33.60) There is no section in the FAQ equivalent to "Using R". The "R Basics" is too basic. I would say it addresses "meta-R" questions, such as installing. I had a FAQ that I would think would be a basic "Using R" question, namely: "How does

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

Dear People, I have been writing a simulation routine that is currently entirely written in c++. I've been using the R standalone math library to give me random number generation for this. However, I have had to set the seed myself, and I am using set_seed(time(NULL), clock) for every call to unif_rand(). However, this works really badly. The random numbers aren't uniform at all. Does

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

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)

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

Dear all, I would be very grateful if you could help me with: Given the regularized gamma function Reg=int_0^r (x^(k-1)e^(-x))dx/int_0^Inf (x^(k-1)e^(-x))dx ; 0<r<Inf (which is eventually the ratio of the Incomplete gamma function by the gamma function), does anyone know of a package in R that would evaluate the derivative of the inverse of Reg with respect to k? I am aware that the

Dear R users, ?rgamma gives me : rgamma(n, shape, rate = 1, scale = 1/rate) rate: an alternative way to specify the scale. 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) Should I understand that scale=1/rate ? Is it written somewhere ? Then

Dear People, Consider the following fragment of R code choose <- function(n,r) { if( is.integer(n) && is.integer(r) && n > 0 && r >= 0 ) { .C("choose",as.double(n),as.double(r),comb = double(1))$comb } else stop("n must be a positive integer and r a non-negative integer.") } This is a practice function (n choose r), which I

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)

Hi, Is there a way to check whether a file is empty in R. I did the customary searches, but did not find anything. Please cc me on any reply. Thanks, Faheem.

Hi, I have normally distributed random terms u~N(0,1). I want to get gamma distributed random terms g~(scale,shape) with E(g)=1=shape/scale and var(g)=theta=1/scale=1/shape. How can I reach my goal? The following way doesn't work: use the distribution function of u to get U(0,1)- distributed random terms, then take the quantile function of the gamma distribution with shape and scale. The

Hi, All: assume I need to generate X from inverse gamma with parameter (k, beta). should I generate from Y from gamma(-k, beta), then take X=1/Y? Thanks pat

Dear People, In a qqplot I am doing, I get lines/points that are very thick. I've tried setting the lwd variable to 0.1, but it doesn't seem to have any effect. Also, I have set the value of lty to dashed, but I still get dots. The command looks like qqplot(cdf.inv(seq(0,1,length=size),theta,pos,len),empmargdistvec(len,theta,pos,size), xlim=c(-theta,theta), ylim=c(-theta,theta),

Hi, Consider the following > x = c(1,2) > y = c(3,4) > d = data.frame(cbind(x,y)) > d$x [1] 1 2 > d$"x" [1] 1 2 > > foo = function(val) + { + return(d$val) + } > > bar = function() + { + return(d$"x") + } > > foo("x") NULL > bar() [1] 1 2 I'm a little surprised that R accepts both the form d$x and d$"x", but

Hi to all I found an formular of an ** ***p-Value Calculator for the Chi-Square test* *http://www.danielsoper.com/statcalc/calc11.aspx* *with the formula* *http://www.danielsoper.com/statkb/topic11.aspx* *what's the gamma function of this formula in r?* *df=5* *ch2=25.50878* *the following code does not give the result <0.001 for the values above * *p=

Dear People, I am trying to include a barplot (see code below), in a latex document. However, the plot appears in totally the wrong position. I know this is not an R question as such, but the eps file is produced by R and I thought that perhaps other people had had similar problems and could give me pointers. Am I doing anything obviously wrong? Sincerely, Faheem Mitha. PS.