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
<murd...@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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