randomprobs {aylmer} | R Documentation |
Probabilities of a random Markov chain of boards, chosen by the Metropolis-Hastings algorithm
randomprobs(x, B=2000, n=100, burnin = 0, use.brob=FALSE, func=NULL) candidate(x, n = 100, give = FALSE)
x |
Matrix, coerced to class board : the start point |
B |
Number of samples to take |
burnin |
Number of samples to discard at the beginning |
use.brob |
Boolean, with default FALSE meaning to use IEEE
arithmetic and TRUE meaning to use Brobdingnagian arithmetic |
n |
The number of times to try to find a candidate board with no non-negative entries; special value 0 means to search until one is found |
func |
In function randomprobs() , the statistic to
return; default of NULL interpreted as prob() |
give |
In function candidate() , Boolean with default
FALSE meaning to return a permissible board, and TRUE
meaning to return instead the number of attempts made to find a
permissible board (zero meaning no board was found). See details
section below |
Returns a vector of length B
with entries corresponding to the
probabilities of the boards encountered
Argument n
of function candidate()
specifies how many
times to search for a board with no non-negative entries. The special
value n=0
means to search until one is found.
Boards with a large number of zeros may require more than the default
100 attempts to find a permissible board. Set the give
flag to
see how many candidates are generated before a permissible one is found.
Warning: a board with at most one entry greater than zero is
the unique permissible board and the algorithm will not terminate if
n=0
A board that requires more than 100 attempts is probably well-suited
to the exact test as permissible boards will likely be enumerable
using allboards()
.
Robin K. S. Hankin (R); Luke J. West (C++)
data(chess) aylmer.test(chess) a <- matrix(1,9,9) plot(randomprobs(a,1000),type="b",main="Importance of burn-in") set.seed(0) b <- diag(rep(6,6)) plot(randomprobs(b,B=1000,n=1000), type="b",main="Importance of burn-in, part II")