Description Usage Arguments Value Author(s) References See Also Examples
Performs a trioFS (trio Feature Selection) analysis as proposed by Schwender et al. (2011) based on bagging/subsampling with base learner trio logic regression (Li et al., 2011).
1 2 3 4 5 6 7 8 9 10  ## Default S3 method:
trioFS(x, y, B = 20, nleaves = 5, replace = TRUE, sub.frac = 0.632,
control = lrControl(), fast = FALSE, addMatImp = TRUE, addModels = TRUE,
verbose = FALSE, rand = NA, ...)
## S3 method for class 'trioPrepare'
trioFS(x, ...)
## S3 method for class 'formula'
trioFS(formula, data, recdom = TRUE, ...)

x 
either an object of class 
y 
a numeric vector specifying the casepseudocontrol status for the observations in 
B 
number of bootstrap samples or subsamples used in 
nleaves 
maximum number of leaves, i.e.\ variables, in the logic tree considered in each of the 
replace 
should sampling of the trios be done with replacement? If

sub.frac 
a proportion specifying the fraction of trios that
are used in each iteration to fit a trio logic regression model if 
control 
a list of control parameters for the search algorithms and the logic trees considered when fitting the
trio logic regression model, where the parameters for an MC logic regression are ignored. For details and the parameters,
see 
fast 
should a greedy search be used instead of simulated annealing, i.e. the standard search algorithm in (trio) logic regression? 
addMatImp 
should the matrix containing the improvements due to the interactions
in each of the iterations be added to the output, where the importance of each interaction
is computed by the average over the 
addModels 
should the 
verbose 
should some comments on the progress the 
rand 
positive integer. If specified, the random number generator is set into a reproducible state. 
formula 
an object of class 
data 
a data frame containing the variables in the model. Each row of 
recdom 
a logical value or vector of length 
... 
for the 
An object of class trioFS
consisting of
vim 
a numeric vector containing the values of the importance measure for the found interactions, 
prop 
a numeric vector consisting of the percentage of models that contain the respective found interactions, 
primes 
a character vector naming the found interactions, 
param 
a list of parameters used in the trioFS analysis, i.e. 
mat.imp 
if 
logreg.model 
if 
inbagg 
if 
Holger Schwender, holger.schwender@udo.edu
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.
Schwender, H., Bowers, K., Fallin, M.D., and Ruczinski, I. (2011). Importance Measures for Epistatic Interactions# in CaseParent Trios. Annals of Human Genetics, 75, 122132.
trioLR
, print.trioFS
, trio.prepare
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  # Load the simulated data.
data(trio.data)
# Prepare the data in trio.ped1 for a trioFS 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 trioFS, 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).
# TrioFS can then be applied to the trio data in trio.ped1 by
fs.out < trioFS(trio.bin, control=my.control, rand=9876543)
# where we specify rand just to make the results reproducible.

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