trio (version 3.10.0)

trio.permTest: Permutation Tests for Trio Logic Regression

Description

Performs either a null-model or a conditional permutation test for a trio logic regression analysis.

Usage

trio.permTest(object, conditional = FALSE, n.perm = 10, nleaves = NULL,  control = NULL, rand = NA)

Arguments

object

an object of class trioLR, i.e. the output of the function trioLR. This object must be the result of a trio logic regression analysis in which a single model has been fitted (i.e. in trioLR, search must have been set to "sa" and nleaves must have been a single integer).

conditional

should the conditional permutation test be performed? If FALSE, a null-model permutation test is done analogously to the null-model permutation test for a standard logic regression for population-based data implemented in the function logreg of the R package LogicReg. If TRUE, a test analogous to the conditional permutation test for a standard logic regression is performed.

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 NULL, the maximum number of leaves saved in object is used.

control

a list containing the control parameters for the search algorithms and the logic tree considered in trioLR, where the parameters for an MCMC run and the logic tree are ignored. If NULL (i.e. by default), the same values for the parameters are used that have been employed in the original analysis with trioLR. If other values should be used, it is highly recommended to specify control by employing lrControl.

rand

an integer. If specified, the random number generator will be set into a reproducible state.

Value

A list consisting of

origScore

NA, if conditional = FALSE, and otherwise, the score, i.e.\ the value of the partial likelihood, of the original model saved in object

,

permScore

a vector of length n.perm containing the scores for the trio logic regression models built in the iterations of the permutation test.

References

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 SNP-SNP Interactions in Trios of Parents with Schizophrenic Children. Genetic Epidemiology, 34, 396-406.

See Also

trioLR

Examples

# 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)