similar to: Regularized gamma function/ incomplete gamma function

Hello I am trying to evaluate an Incomplete gamma function in R. Library Zipfr gives the Igamma function. From Mathematica, I have: "Gamma[a, z] is the incomplete gamma function." In[16]: Gamma[9,11.1] Out[16]: 9000.5 Trying the same in R, I get > Igamma(9,11.1) [1] 31319.5 OR > Igamma(11.1,9) [1] 1300998 I know I have to understand the theory and the math behind it rather

Hi folks, I am having a question about efficiently finding the integrals of a list of functions. To be specific, here is a simple example showing my question. Suppose we have a function f defined by f<-function(x,y,z) c(x,y^2,z^3) Thus, f is actually corresponding to three uni-dimensional functions f_1(x)=x, f_2(y)=y^2 and f_3(z)=z^3. What I am looking for are the integrals of these three

Hi, I would like to solve a double integral of the form \int_0^1 \int_0^1 x*y dx dy using Gauss Quadrature. I know that I can use R's integrate function to calculate it: integrate(function(y) { sapply(y, function(y) { integrate(function(x) x*y, 0, 1)$value }) }, 0, 1) but I would like to use Gauss Quadrature to do it. I have written the following code (using R's statmod package)

Thanks Gabor and Duncan! It works. For those interested, I added this to the beginning of the \details{} section: \if{html}{\out{ <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax at 3/es5/tex-mml-chtml.js"> </script> }} And then I can use: \if{html}{\out{\(B_{x(a,b)} = \int_0^x t^{a-1} (1-t)^{b-1} dt\)}} or

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

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

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 All, After some tinkering, and with support from Duncan, I put together a package that allows for easy inclusion of MathJax equations in Rd files. The package has been submitted to CRAN, but those who want to try this out already can get it here: https://github.com/wviechtb/mathjaxr or in other words: install.packages("remotes")

Hi, I need to integrate a 2D function over range where the limits depend on the other e.g integrate f(x,y)=x*y over {x,0,1} and {y,x,1}. i.e \int_0^1 \int_x^1 xy dydx I checked adapt but it doesn't seem to help here. Are they any packages for this sort of thing? I tried RSitesearch but couldn't find the answer to this. Many thanks for you help. Regards Saptarshi Saptarshi Guha

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

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

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

Dear R Gang, I'm interested in using R and the nls package for fitting kinetic models. I'm having some difficulty getting a model specified for nls though. The math for the model that I want to fit is dg(t)/dt = K1 f(t) - k2 g(t) where g(t) and f(t) are measured data at a sequence of times t. K1 and k2 are the parameters of the model. If I solve this, the solution is g(t) = K1

I am trying to solve an integral in R. However, I am getting an error when I am trying to solve for that integral. The equation that I am trying to solve is as follows: $$ C_m = \frac{{abs{x}}e^{2x}}{\pi^{1/2}}\int_0^t t^{-3/2}e^{-x^2/t-t}dt $$ [image: enter image description here] The code that I am using is as follows: a <- seq(from=-10, by=0.5,length=100) ## Create a function to compute

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, :

Greetings, Sorry, the last message was sent by mistake! Here it is again: I encounter a strange problem computing some numerical integrals on [0,oo). Define $$ M_j(x)=exp(-jax) $$ where $a=0.08$. We want to compute the $L^2([0,\infty))$-inner products $$ A_{ij}:=(M_i,M_j)=\int_0^\infty M_i(x)M_j(x)dx $$ Analytically we have $$ A_{ij}=1/(a(i+j)). $$ In the code below we compute the matrix

Exactly! Thanks, Duncan. Let me re-phrase me question like this: 1) X_i values are independent Gammas, with the shape 0.067 and scale 0.008 2) Min(X)=1 and Max(X)=85 3) SUM(X)=2000 4) Do I also have to define the number of draws? if yes, it could be 250. Based on these restrictions, I want to generate random draw. I'm wondering how I can do this in R. Thanks. Garry On Tue, Nov 10, 2009

I'm testing a FC5 machine for use in a student lab. R 2.3.1 is installed and seems to work fine. There is one peculiarity - the logins are authenticating to a server, and a "verbose" flag is set somewhere, leading to lots of spurious messages like this request done: ld 0xa227598 msgid 1 which may be confusing R. However, R CMD check seems to fail for packages with no

Dear R People, This question has nothing to do with R directly, but it is a simulation question. I need to generate a random variable distributed as gamma(\alpha,\beta), with the additional proviso that it must be a function of random variable(s) which do not depend on \alpha, \beta. In this case, I have \alpha = (T-1)/2, where T is a positive integer. So, it seems reasonable to first simulate

Dear [R]-list, I'm looking for a classic equivalent of the wle.gamma function (library wle) that estimate robustly the shape and the scale parameters of gamma data. I have a vector of iid gamma rv : >data=rgamma(100,shape=10,scale=3) and a vector of their weights: >weights=c(rep(.5/70,70),rep(.25/20,20),rep(.25/10,10)) and want to estimate the scale and shape of the gamma