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rrandomdistributionsimulate

Simulate Values that follow a Distribution Curve in R

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I want to simulate demand values that follows different distributions (ex above: starts of linear> exponential>invlog>etc) I'm a bit confused by the notion of probability distributions but thought I could use rnorm, rexp, rlogis, etc. Is there any way I could do so?

I think it may be this but in R: Generating smoothed randoms that follow a distribution

Solution

  • Simulating random values from commonly-used probability distributions in R is fairly trivial using rnorm(), rexp(), etc, if you know what distribution you want to use, as well as its parameters. For example, rnorm(10, mean=5, sd=2) returns 10 draws from a normal distribution with mean 5 and sd 2.

    rnorm(10, mean = 5, sd = 2)
##  [1] 5.373151 7.970897 6.933788 5.455081 6.346129 5.767204 3.847219 7.477896 5.860069 6.154341

## or here's a histogram of 10000 draws...
hist(rnorm(10000, 5, 2))

    You might be interested in an exponential distribution - check out hist(rexp(10000, rate=1)) to get the idea.

    The easiest solution will be to investigate what probability distribution(s) you're interested in and their implementation in R.

    It is still possible to return random draws from some custom function, and there are a few techniques out there for doing it - but it might get messy. Here's a VERY rough implementation of drawing randomly from probabilities defined by the region of x^3 - 3x^2 + 4 between zero and 3.

    ## first a vector of random uniform draws from the region
unifdraws <- runif(10000, 0, 3)

## assign a probability of "keeping" draws based on scaled probability
pkeep <- (unifdraws^3 - 3*unifdraws^2 + 4)/4

## randomly keep observations based on this probability
keep <- rbinom(10000, size=1, p=pkeep)
draws <- unifdraws[keep==1]

## and there it is!
hist(draws)

## of course, it's less than 10000 now, because we rejected some
length(draws)
## [1] 4364