Simulating a variable following an arbitrary distribution
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, --
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