Generate a Random Vector from a Bernoulli Sampling Model

rvbern generates a random vector where each simulation comes from a Bernoulli sampling distribution.

rvbern(n = 1, prob, logical = FALSE)

Arguments

n

number of random scalars to draw

prob

probability of "success"; may be a random vector itself

logical

logical; return a logical random variable instead

Value

A random vector (an rv object) of length n.

Details

rvbern is a special case of rvbinom with the argument size=1.

If logical is TRUE, the function returns a logical random variable which has TRUE for 1, FALSE for 0. (The printed summary of this object is slightly different from a regular continuous numeric random variable.)

Note

The resulting vector will not be independent and identically distributed Bernoulli unless prob is a fixed number.

References

Kerman, J. and Gelman, A. (2007). Manipulating and Summarizing Posterior Simulations Using Random Variable Objects. Statistics and Computing 17:3, 235-244.

See also vignette("rv").

Author

Jouni Kerman jouni@kerman.com

Examples

  rvbern(2, prob=0.5)
#>     mean  sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 0.51 0.5  0    0   0   1   1     1   1  200
#> [2] 0.44 0.5  0    0   0   0   1     1   1  200
  rvbinom(2, size=1, prob=0.5) # Equivalent
#>     mean  sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 0.53 0.5  0    0   0   1   1     1   1  200
#> [2] 0.55 0.5  0    0   0   1   1     1   1  200
  print(rvbern(1, 0.5, logical=TRUE)) # won't show the quantiles
#>     mean  sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 0.49 0.5  0    0   0   0   1     1   1  200
  print(as.logical(rvbern(1, 0.5))) # equivalent
#>     mean  sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 0.48 0.5  0    0   0   0   1     1   1  200