Description Usage Arguments Value Author(s) References See Also Examples
View source: R/trio.permTest.R
Performs either a nullmodel or a conditional permutation test for a trio logic regression analysis.
1 2  trio.permTest(object, conditional = FALSE, n.perm = 10, nleaves = NULL,
control = NULL, rand = NA)

object 
an object of class 
conditional 
should the conditional permutation test be performed? If 
n.perm 
integer specifying the number of permutations. 
nleaves 
integer specifying the maximum number of leaves that the logic tree in the trio logic regression model is allowed to have.
If 
control 
a list containing the control parameters for the search algorithms and the logic tree considered in 
rand 
an integer. If specified, the random number generator will be set into a reproducible state. 
A list consisting of
origScore 

,
permScore 
a vector of length 
Qing Li, mail2qing@yahoo.com. Modified by Holger Schwender.
Li, Q., Fallin, M.D., Louis, T.A., Lasseter, V.K., McGrath, J.A., Avramopoulos, D., Wolyniec, P.S., Valle, D., Liang, K.Y., Pulver, A.E., and Ruczinski, I. (2010). Detection of SNPSNP Interactions in Trios of Parents with Schizophrenic Children. Genetic Epidemiology, 34, 396406.
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 38 39  # Load the simulated data.
data(trio.data)
# Prepare the data in trio.ped1 for a trio logic
# regression analysis by first calling
trio.tmp < trio.check(dat = trio.ped1)
# and then applying
set.seed(123456)
trio.bin < trio.prepare(trio.dat=trio.tmp, blocks=c(1,4,2,3))
# where we here assume the block structure to be
# c(1, 4, 2, 3), which means that the first LD "block"
# only consists of the first SNP, the second LD block
# consists of the following four SNPs in trio.bin,
# the third block of the following two SNPs,
# and the last block of the last three SNPs.
# set.seed() is specified to make the results reproducible.
# For the application of trio logic regression, some
# parameters of trio logic regression are changed
# to make the following example faster.
my.control < lrControl(start=1, end=3, iter=1000, output=4)
# Please note typically you should consider much more
# than 1000 iterations (usually, at least a few hundred
# thousand).
# Trio regression can then be applied to the trio data in
# trio.ped1 by
lr.out < trioLR(trio.bin, control=my.control, rand=9876543)
# where we specify rand just to make the results reproducible.
# A null model permutation test can be performed by
trio.permTest(lr.out)
# The conditional permutation test can be performed by
trio.permTest(lr.out, conditional = TRUE)

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