Permutation Test for exploring significance of MBMDR result.
Description
Performs a permutation test for specified interaction models from mbmdr
object.
Usage
1  mbmdr.PermTest(x, n, model = NULL, sig.level=1)

Arguments
x 
An mbmdr object returned by 
n 
Number of permutations. 
model 
Vector specifiying an interaction model or matrix with rows referring to interaction models
to be submitted for permutation testing. If 
sig.level 
Significance level for the confidence intervals of the permutation based pvalues, using
a normal approximation (Nettleton, 2000). 
Details
A permutation testing is performed for each specified model by permuting the outcome variable and calling
the mbmdr
function.
The call to the mbmdr
function is made by recovering the call from mbmdr
object and replacing
the outcome (by a permuted outcome vector) and SNP data (by the subset of the specified model). All other arguments in the
initial call to mbmdr
are transfered to mbmdr.PermTest
.
Value
An object is returned of a new class, mbmdr.PermTest
, with following attributes:
n 
The number of permutations.  
mbmdr 
The  
PermTest 
A

References
Nettleton D., Doerge R.W. (2000) Accounting for Variability in the Use of Permutation Testing to Detect Quantitative Trait Loci. Biometrics, Vol. 56, No. 1, pp. 5258.
See Also
mbmdr
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  data(simSNP)
fit < mbmdr(y=simSNP$Y,data=simSNP[,3:12],order=2,list.models=c(2,1),
family=binomial(link=logit))
# Single model permutation test
mbmdr.PermTest(fit,100)
# Next steps takes some time
# Permutation test for all models with MIN.P <= 0.05
# order < 2
# models < subset(fit$result, MIN.P <= 0.05, select = 1:order)
# mbmdr.PermTest(fit,100,models)
# Permutation test and confidence interval for all models with MIN.P <= 0.05
# mbmdr.PermTest(fit,100,models,sig.level=0.05)
