Specification of the asymptotic, approximative (Monte Carlo) and exact reference distribution.
1 2 3 4  asymptotic(maxpts = 25000, abseps = 0.001, releps = 0)
approximate(B = 10000, parallel = c("no", "multicore", "snow"),
ncpus = 1, cl = NULL)
exact(algorithm = c("auto", "shift", "splitup"), fact = NULL)

maxpts 
an integer, the maximum number of function values. Defaults to

abseps 
a numeric, the absolute error tolerance. Defaults to 
releps 
a numeric, the relative error tolerance. Defaults to 
B 
a positive integer, the number of Monte Carlo replicates used for the
computation of the approximative reference distribution. Defaults to

parallel 
a character, the type of parallel operation: either 
ncpus 
an integer, the number of processes to be used in parallel operation.
Defaults to 
cl 
an object inheriting from class 
algorithm 
a character, the algorithm used for the computation of the exact reference
distribution: either 
fact 
an integer to multiply the response values with. Defaults to 
asymptotic
, approximate
and exact
can be supplied to the
distribution
argument of, e.g., independence_test
to
provide control of the specification of the asymptotic, approximative (Monte
Carlo) and exact reference distribution respectively.
The asymptotic reference distribution is computed using a randomised
quasiMonte Carlo method (Genz and Bretz, 2009) and is applicable to arbitrary
covariance structures with dimensions up to 1000. See
GenzBretz
in package mvtnorm for
details on maxpts
, abseps
and releps
.
The approximative (Monte Carlo) reference distribution is obtained by a
conditional Monte Carlo procedure, i.e., by computing the test statistic for
B
random samples from all admissible permutations of the response
Y within each block (Hothorn et al., 2008). By default,
the distribution is computed using serial operation (parallel = "no"
).
The use of parallel operation is specified by setting parallel
to
either "multicore"
(not available for MS Windows) or "snow"
. In
the latter case, if cl = NULL
(default) a cluster with ncpus
processes is created on the local machine unless a default cluster has been
registered (see setDefaultCluster
in
package parallel) in which case that gets used instead. Alternatively,
the use of an optional parallel or snow cluster can be specified
by cl
. See ‘Examples’ and package parallel for details on
parallel operation.
The exact reference distribution, currently available for univariate
twosample problems only, is computed using either the shift algorithm
(Streitberg and Röhmel, 1986, 1987) or the splitup algorithm
(van de Wiel, 2001). The shift algorithm handles blocks pertaining to, e.g.,
pre and poststratification, but can only be used with positive
integervalued scores h(Y). The splitup algorithm can be
used with noninteger scores, but does not handle blocks. By default, an
automatic choice is made (algorithm = "auto"
) but the shift and
splitup algorithms can be selected by setting algorithm
to either
"shift"
or "splitup"
respectively.
Starting with coin version 1.10, the default for algorithm
is
"auto"
, having identical behaviour to "shift"
in previous
versions. In earlier versions of the package, algorithm = "shift"
silently switched to the splitup algorithm if noninteger scores were
detected, whereas the current version exits with a warning.
Genz, A. and Bretz, F. (2009). Computation of Multivariate Normal and t Probabilities. Heidelberg: SpringerVerlag.
Hothorn, T., Hornik, K., van de Wiel, M. A. and Zeileis, A. (2008). Implementing a class of permutation tests: the coin package. Journal of Statistical Software 28(8), 1–23. http://www.jstatsoft.org/v28/i08/
Streitberg, B. and Röhmel, J. (1986). Exact distributions for permutations and rank tests: an introduction to some recently published algorithms. Statistical Software Newsletter 12(1), 10–17.
Streitberg, B. and Röhmel, J. (1987). Exakte verteilungen für rang und randomisierungstests im allgemeinen cstichprobenfall. EDV in Medizin und Biologie 18(1), 12–19.
van de Wiel, M. A. (2001). The splitup algorithm: a fast symbolic method for computing pvalues of distributionfree statistics. Computational Statistics 16(4), 519–538.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  ## Approximative (Monte Carlo) CochranMantelHaenszel test
## Serial operation
set.seed(123)
cmh_test(disease ~ smoking  gender, data = alzheimer,
distribution = approximate(B = 100000))
## Not run:
## Multicore with 8 processes (not for MS Windows)
set.seed(123, kind = "L'EcuyerCMRG")
cmh_test(disease ~ smoking  gender, data = alzheimer,
distribution = approximate(B = 100000,
parallel = "multicore", ncpus = 8))
## Automatic PSOCK cluster with 4 processes
set.seed(123, kind = "L'EcuyerCMRG")
cmh_test(disease ~ smoking  gender, data = alzheimer,
distribution = approximate(B = 100000,
parallel = "snow", ncpus = 4))
## Registered FORK cluster with 12 processes (not for MS Windows)
fork12 < parallel::makeCluster(12, "FORK") # setup cluster
parallel::setDefaultCluster(fork12) # register default cluster
set.seed(123, kind = "L'EcuyerCMRG")
cmh_test(disease ~ smoking  gender, data = alzheimer,
distribution = approximate(B = 100000,
parallel = "snow"))
parallel::stopCluster(fork12) # cleanup
## Userspecified PSOCK cluster with 8 processes
psock8 < parallel::makeCluster(8, "PSOCK") # setup cluster
set.seed(123, kind = "L'EcuyerCMRG")
cmh_test(disease ~ smoking  gender, data = alzheimer,
distribution = approximate(B = 100000,
parallel = "snow", cl = psock8))
parallel::stopCluster(psock8) # cleanup
## End(Not run)

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