Generate Random Draw from Gamma Distribution Re: Monte Carlo Simulation in R...
On Nov 10, 2009, at 2:26 PM, Hongwei Dong wrote:
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
You might want to check that your parameterization in in agreement with that used by the rgamma function. Simply using those numbers yields a distribution that does not look as though it would get many qualifying samples. Here are 20 draws without any exclusions outside a range: > rgamma(20, shape=0.067, scale = 0.008) [1] 2.213459e-03 2.815705e-05 2.381306e-04 2.264602e-07 1.293713e-07 7.680773e-38 6.441082e-15 6.168961e-13 [9] 5.089033e-06 1.571858e-16 9.869878e-12 1.813121e-13 1.253287e-11 1.852885e-04 4.212802e-07 1.774495e-25 [17] 1.892984e-07 5.927422e-17 1.322638e-12 4.327472e-05 http://finzi.psych.upenn.edu/R/Rhelp02/archive/31459.html
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 at 11:17 AM, Duncan Murdoch <murdoch at stats.uwo.ca>wrote:
On 11/10/2009 1:25 PM, Hongwei Dong wrote:
Hi, Dear R users, I'm wondering if I can do Monte Carlo Simulation in R. My problem is like this: I know variable X follows Gamma distribution with shape parameter 0.067 and scale parameter 0.008. The sum of the X is 2000. I need R help me to simulate a vector of X that satisfies both the probability distribution and the sum. Anyone has a clue to this? Much appreciated.
Your requirements are slightly contradictory or incomplete. Here's one way to fully specify the problem: The X_i values are independent Gammas, with the given shape and scale. You want to simulate from the joint distribution conditional on the event sum(X) == 2000. Is that your problem? I don't know how to do the simulation, but maybe someone else does. Duncan Murdoch
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