dice | R Documentation |
Simulate and optionally plot rolls of dice.
dice(rolls = 1, ndice = 2, sides = 6, plot.it = FALSE, load = rep(1, sides))
## S3 method for class 'dice'
plot(x, ...)
rolls |
Scalar, the number of times to roll the dice. |
ndice |
Scalar, the number of dice to roll each time. |
sides |
Scalar, the number of sides per die. |
plot.it |
Logical, Should the results be plotted. |
load |
Vector of length |
x |
Data frame, return value from |
... |
Additional arguments passed to lattice plotting function. |
Simulates the rolling of dice. By default it will roll 2 dice 1 time
and the dice will be fair. Internally the sample
function is
used and the load option is passed to sample. load
is not
required to sum to 1, but the elements will be divided by the sum of
all the values.
A data frame with rolls
rows and ndice
columns
representing the results from rolling the dice.
If only 1 die is rolled, then the return value will be a vector.
If plot.it
is TRUE, then the return value will be invisible.
If the plot function is used or if plot.it
is TRUE, then a
plot will be created on the current graphics device.
Greg Snow 538280@gmail.com
sample
# 10 rolls of 4 fair dice
dice(10,4, plot.it=TRUE)
# or
plot(dice(10,4))
# or
tmp <- dice(10,4)
plot(tmp)
# a loaded die
table(tmp <- dice(100,1,plot.it=TRUE, load=6:1 ) )
colMeans(tmp)
# Efron's dice
ed <- list( rep( c(4,0), c(4,2) ),
rep(3,6), rep( c(6,2), c(2,4) ),
rep( c(5,1), c(3,3) ) )
tmp <- dice( 10000, ndice=4 )
ed.out <- sapply(1:4, function(i) ed[[i]][ tmp[[i]] ] )
mean(ed.out[,1] > ed.out[,2])
mean(ed.out[,2] > ed.out[,3])
mean(ed.out[,3] > ed.out[,4])
mean(ed.out[,4] > ed.out[,1])
## redo De Mere's question
demere1 <- dice(10000,4)
demere2 <- dice(10000,24,sides=36)
mean(apply( demere1, 1, function(x) 6 %in% x ))
mean(apply( demere2, 1, function(x) 36 %in% x))
plot(demere1[1:10,])
## plot all possible combinations of 2 dice
plot.dice( expand.grid(1:6,1:6), layout=c(6,6) )
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