Description Usage Arguments Details Value Note Author(s) References See Also Examples
A generalization of Fisher's exact test; much of the documentation and
R code is inspired by fisher.test()
1 2 3 4 5  aylmer.test(x, alternative = "two.sided", simulate.p.value = FALSE,
n = 1e5, B = 2000, burnin = 100, use.brob = FALSE)
aylmer.function(x, func, simulate.p.value = FALSE, n = 1e5, B = 2000,
burnin=100, use.brob=FALSE, DNAME=NULL)
prob(x, give.log=TRUE, use.brob = FALSE)

x 
A matrix, possibly with some 
alternative 
Indicates the alternative hypothesis. If not a
function, it must be one of “two.sided”, “greater” or
“less”. You may specify just the initial letter. Only used
in cases with one degree of freedom. If a function, then control is
passed to 
simulate.p.value 
Boolean, with default 
n 
Integer specifying the maximum number of boards to list if

B 
Integer specifying the number of replicates used in the Monte Carlo version of the test 
burnin 
Integer specifying the length of burn in. See details section 
use.brob 
Boolean, with default 
give.log 
In function 
func 
In function 
DNAME 
In function 
If simulate.p.value
is TRUE
, a vector of random
probabilities is used instead of the full enumeration. A total of
B+burnin
boards are generated of which the first burnin
are discarded.
An object of class “htest
”
Function prob()
gives a number that is proportional to the
probability of observing a board.
The probability of observing a board B with no NA
s,
conditional on its being permissible is, obvious notation,
ommitted; see pdf
The numerator is the same for any permissable board so is not calculated.
If simulate.p.value
is TRUE
, the default value for
B
of 2000 is likely to be low, especially for large tables, or
tables with large entries. Bear in mind that the Markov chain has high
sequential correlation.
If simulate.p.value
is FALSE
, enumerative techniques are
used. In this case, the default value for n
(10^5) is also
likely to be low: a pvalue of 1 is returned because the first few
boards all have a probability much much smaller than that of the data.
Robin K. S. Hankin (R); Luke J. West (C++); an anonymous
JSS referee who suggested the approach used in
aylmer.function()
Ronald Aylmer Fisher 1955. Statistical methods for research workers, Oliver and Boyd
G. H. Freeman and J. H. Halton 1951. Note on an Exact Treatment of Contingency, Goodness of Fit and Other Problems of Significance. Biometrika, 38(12):141149
A. W. Ghent 1972. A Method for Exact Testing of 2x2, 2x3, 3x3, and Other Contingency Tables, Employing Binomial Coefficients. American Midland Naturalist, 88(1):1527
R. K. S. Hankin 2007. Very Large Numbers in R: Introducing Package Brobdingnag, R news 3(3):1516
M. J. Silvapulle and P. K. Sen 2005. Constrained
statistical inference. Wiley (page 326 for a special case of
the tests performed by aylmer.function()
)
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