Pseudo-Random Number Generation

The tools of random number generation are used for creating entirely new "synthetic" datasets and for permutation, subsampling, and bootstrapping (resampling with replacement) of existing data. You will learn how to use built-in R functions to generate random samples from different probability distributions (more distributions are available from user-contributed packages, such as the package gamlss.dist).

R as a set of statistical tables

One convenient use of R is to provide a comprehensive set of statistical tables. Functions are provided to evaluate the cumulative distribution function P(X <= x), the probability density function and the quantile function (given q, the smallest x such that P(X <= x) > q), and to simulate from the distribution.

Distribution R name additional arguments
beta beta shape1, shape2, ncp
binomial binom size, prob
Cauchy cauchy location, scale
chi-squared chisq df, ncp
exponential exp rate
F f df1, df2, ncp
gamma gamma shape, scale
geometric geom prob
hypergeometric hyper m, n, k
log-normal lnorm meanlog, sdlog
logistic logis location, scale
negative binomial nbinom size, prob
normal norm mean, sd
Poisson pois lambda
signed rank signrank n
Student's t t df, ncp
uniform unif min, max
Weibull weibull shape, scale
Wilcoxon wilcox m, n

Prefix the name given here by 'd' for the density, 'p' for the CDF, 'q' for the quantile function and 'r' for simulation (random deviates). The first argument is x for dxxx, q for pxxx, p for qxxx and n for rxxx (except for rhyper, rsignrank and rwilcox, for which it is nn). In not quite all cases is the non-centrality parameter ncp currently available: see the on-line help for details.

Source: R Core Team, https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Probability-distributions
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