R Programming/Probability Distributions

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This page review the main probability distributions and describe the main R functions to deal with them.

R has lots of probability functions.

  • r is the generic prefix for random variable generator such as runif(), rnorm().
  • d is the generic prefix for the probability density function such as dunif(), dnorm().
  • p is the generic prefix for the cumulative density function such as punif(), pnorm().
  • q is the generic prefix for the quantile function such as qunif(), qnorm().


Discrete distributions[edit]

Benford Distribution[edit]

The Benford Distribution is the distribution of the first digit of a number. It is due to Benford 1938[1] and Newcomb 1881[2].

> library(VGAM)
> dbenf(c(1:9))
[1] 0.30103000 0.17609126 0.12493874 0.09691001 0.07918125 0.06694679 0.05799195 0.05115252 0.04575749


Bernouilli[edit]

We can draw from a Bernouilli using sample(), runif() or rbinom() with size = 1.

> n <- 1000
> x <- sample(c(0,1), n, replace=T)
> x <- sample(c(0,1), n, replace=T, prob=c(0.3,0.7))
> x <- runif(n) > 0.3
> x <- rbinom(n, size=1, prob=0.2)

Binomial[edit]

We can sample from a binomial distribution using the rbinom() function with arguments n for number of of samples to take, size defining the number of trials and prob defining the probability of success in each trial.

> x <- rbinom(n=100,size=10,prob=0.5)


Hypergeometric distribution[edit]

We can sample n times from a hypergeometric distribution using the rhyper() function.

> x <- rhyper(n=1000, 15, 5, 5)


Geometric distribution[edit]

The geometric distribution.

> N <- 10000
> x <- rgeom(N, .5)
> x <- rgeom(N, .01)


Multinomial[edit]

The multinomial distribution.

> sample(1:6, 100, replace=T, prob= rep(1/6,6))


Negative binomial distribution[edit]

The negative binomial distribution is the distribution of the number of failures before k successes in a series of Bernoulli events.

> N <- 100000
> x <- rnbinom(N, 10, .25)


Poisson distribution[edit]

We can draw n values from a Poisson distribution with a mean set by the argument lambda.

> x <- rpois(n=100, lambda=3)

Zipf's law[edit]

The distribution of the frequency of words is known as Zipf's Law. It is also a good description of the distribution of city size[3]. dzipf() and pzipf() (VGAM)

> library(VGAM)
> dzipf(x=2, N=1000, s=2


Continuous distributions[edit]

Beta and Dirichlet distributions[edit]

>library(gtools)
>?rdirichlet
>library(bayesm)
>?rdirichlet
>library(MCMCpack)
>?Dirichlet

Cauchy[edit]

We can sample n values from a Cauchy distribution with a given location parameter x_0 (default is 0) and scale parameter \gamma (default is 1) using the rcauchy() function.

> x <- rcauchy(n=100, location=0, scale=1)


Chi Square distribution[edit]

Quantile of the Chi square distribution (\chi^2 distribution)

> qchisq(.95,1)
[1] 3.841459
> qchisq(.95,10)
[1] 18.30704
> qchisq(.95,100)
[1] 124.3421


Exponential[edit]

We can sample n values from a exponential distribution with a given rate (default is 1) using the rexp() function

> x <- rexp(n=100, rate=1)


Fisher-Snedecor[edit]

We can draw the density of a Fisher distribution (F-distribution) :

> par(mar=c(3,3,1,1))
> x <- seq(0,5,len=1000)
> plot(range(x),c(0,2),type="n")
> grid()
> lines(x,df(x,df1=1,df2=1),col="black",lwd=3)
> lines(x,df(x,df1=2,df2=1),col="blue",lwd=3)
> lines(x,df(x,df1=5,df2=2),col="green",lwd=3)
> lines(x,df(x,df1=100,df2=1),col="red",lwd=3)
> lines(x,df(x,df1=100,df2=100),col="grey",lwd=3)
> legend(2,1.5,legend=c("n1=1, n2=1","n1=2, n2=1","n1=5, n2=2","n1=100, n2=1","n1=100, n2=100"),col=c("black","blue","green","red","grey"),lwd=3,bty="n")


Gamma[edit]

We can sample n values from a gamma distribution with a given shape parameter and scale parameter \theta using the rgamma() function. Alternatively a shape parameter and rate parameter \beta=1/\theta can be given.

> x <- rgamma(n=100, scale=1, shape=0.4)
> x <- rgamma(n=100, scale=1, rate=0.8)


Levy[edit]

We can sample n values from a Levy distribution with a given location parameter \mu (defined by the argument m, default is 0) and scaling parameter (given by the argument s, default is 1) using the rlevy() function.

> x <- rlevy(n=100, m=0, s=1)


Log-normal distribution[edit]

We can sample n values from a log-normal distribution with a given meanlog (default is 0) and sdlog (default is 1) using the rlnorm() function

> x <- rlnorm(n=100, meanlog=0, sdlog=1)


Normal and related distributions[edit]

We can sample n values from a normal or gaussian Distribution with a given mean (default is 0) and sd (default is 1) using the rnorm() function

> x <- rnorm(n=100, mean=0, sd=1)

Quantile of the normal distribution

> qnorm(.95)
[1] 1.644854
> qnorm(.975)
[1] 1.959964
> qnorm(.99)
[1] 2.326348
  • The mvtnorm package includes functions for multivariate normal distributions.
    • rmvnorm() generates a multivariate normal distribution.
> library(mvtnorm)
> sig <- matrix(c(1, 0.8, 0.8, 1), 2, 2)
> r <- rmvnorm(1000, sigma = sig)
> cor(r) 
          [,1]      [,2]
[1,] 1.0000000 0.8172368
[2,] 0.8172368 1.0000000


Pareto Distributions[edit]

  • Generalized Pareto dgpd() in evd
  • dpareto(), ppareto(), rpareto(), qpareto() in actuar
  • The VGAM package also has functions for the Pareto distribution.


Student's t distribution[edit]

Quantile of the Student t distribution

> qt(.975,30)
[1] 2.042272
> qt(.975,100)
[1] 1.983972
> qt(.975,1000)
[1] 1.962339

The following lines plot the .975th quantile of the t distribution in function of the degrees of freedom :

curve(qt(.975,x), from = 2 , to = 100, ylab = "Quantile 0.975 ", xlab = "Degrees of freedom", main = "Student t distribution")
abline(h=qnorm(.975), col = 2)


Uniform distribution[edit]

We can sample n values from a uniform distribution (also known as a rectangular distribution] between two values (defaults are 0 and 1) using the runif() function

> runif(n=100, min=0, max=1)


Weibull[edit]

We can sample n values from a Weibull distribution with a given shape and scale parameter \mu (default is 1) using the rweibull() function.

> x <- rweibull(n=100, shape=0.5, scale=1)


Extreme values and related distribution[edit]

plogis, qlogis, dlogis, rlogis

  • Frechet dfrechet() evd
  • Generalized Extreme Value dgev() evd
  • Gumbel dgumbel() evd
  • Burr, dburr, pburr, qburr, rburr in actuar


Distribution in circular statistics[edit]

  • Functions for circular statistics are included in the CircStats package.
    • dvm() Von Mises (also known as the nircular normal or Tikhonov distribution) density function
    • dtri() triangular density function
    • dmixedvm() Mixed Von Mises density
    • dwrpcauchy() wrapped Cauchy density
    • dwrpnorm() wrapped normal density.


See also[edit]

  • Packages VGAM, SuppDists, actuar, fBasics, bayesm, MCMCpack


References[edit]

  1. Benford, F. (1938) The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78, 551–572.
  2. Newcomb, S. (1881) Note on the Frequency of Use of the Different Digits in Natural Numbers. American Journal of Mathematics, 4, 39–40.
  3. Gabaix, Xavier (August 1999). "Zipf's Law for Cities: An Explanation". Quarterly Journal of Economics 114 (3): 739–67. doi:10.1162/003355399556133. ISSN 0033-5533. http://pages.stern.nyu.edu/~xgabaix/papers/zipf.pdf.
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