[R] Simulating a variable following an arbitrary distribution

[Spencer Graves]

	  Your specific example is a scaled beta.  Therefore, "2*rbeta(1000, 4, 
1)" will generate 1000 random numbers according to that distribution. 
You can get the same distribution from "2*qbeta(runif(1000), 4, 1)".
	  We can generalize this last example to any case of interest.  Example:

df4 <- function(x)(x^3)/4
pf4 <- function(q)(q^4)/16
qf4 <- function(p)(16*p)^0.25
rf4 <- function(n)qf4(runif(n))

Then "rf4(1000)" will produce 1000 pseudo-random deviates following this 
distribution.

hth.  spencer graves

Edgar Acuna wrote:
> Hi,
> Use the Inverse transformation method. See any basic Cbook in simulation
> for instance Sheldon Ross's book.
> Regards,
> Edgar
> 
> On Mon, 2 Jun 2003, Fernando Henrique Ferraz Pereira da Rosa wrote:
> 
> 
>>     Hi, I'd like to know if there's anything in R that could help me do
>>that. Let's suppose I have a density function of a random variable, for example
>>f(x) = (x^3)/4 0 < x < 2 and I would like to simulate it. For the common
>>distributions (exponencial, gamma, cauchy) there are the r-functions (rgamma,
>>rexp, runif, rcauchy, and so on).. But when the variable I want to simulate is
>>not one of those, how should I procede? I read some references on the subject
>>and saw that there are some algorithms that can do that, but I just wonder if
>>there is any implemented in R?
>>
>>Thank you,
>>
>>--
>>
>>______________________________________________
>>R-help at stat.math.ethz.ch mailing list
>>https://www.stat.math.ethz.ch/mailman/listinfo/r-help
>>
> 
> 
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