simEd (version 2.0.2)

craps: Monte Carlo Simulation of the Dice Game "Craps"

Description

A Monte Carlo simulation of the dice game "craps". Returns a point estimate of the probability of winning craps using fair dice.

Usage

craps(nrep = 1000, seed = NA, showProgress = TRUE)

Value

Point estimate of the probability of winning at craps (a real-valued scalar).

Arguments

nrep

Number of replications (plays of a single game of craps)

seed

Initial seed to the random number generator (NA uses current state of random number generator; NULL seeds using system clock)

showProgress

If TRUE, displays a progress bar on screen during execution

Author

Barry Lawson (),
Larry Leemis (),
Vadim Kudlay ()

Details

Implements a Monte Carlo simulation of the dice game craps played with fair dice. A single play of the game proceeds as follows:

• Two fair dice are rolled. If the sum is 7 or 11, the player wins immediately; if the sum is 2, 3, or 12, the player loses immediately. Otherwise the sum becomes the point.

• The two dice continue to be rolled until either a sum of 7 is rolled (in which case the player loses) or a sum equal to the point is rolled (in which case the player wins).

The simulation involves nrep replications of the game.

Note: When the value of nrep is large, the function will execute noticeably faster when showProgress is set to FALSE.

See Also

base::set.seed

Examples

 # set the initial seed externally using set.seed;
 # then use that current state of the generator with default nrep = 1000
 set.seed(8675309)
 craps()  # uses state of generator set above

 # explicitly set the seed in the call to the function,
 # using default nrep = 1000
 craps(seed = 8675309)

 # use the current state of the random number generator with nrep = 10000
 prob <- craps(10000)

 # explicitly set nrep = 10000 and seed = 8675309
 prob <- craps(10000, 8675309)