Description Usage Arguments Details Value Author(s) References See Also Examples
Performs full Bayesian logic regression for Single Nucleotide Polymorphism (SNP) data as described in Fritsch and Ickstadt (2007).
fblr.weight
allows to incorporate prior pathway information by restricting
search for interactions to specific groups of SNPs and/or giving them different
weights. fblr.weight
is only implemented for an interaction level of 2.
1 2 3 4 5 6 7 | fblr(y, bin, niter, thin = 5, nburn = 10000, int.level = 2, kmax = 10,
geo = 1, delta1 = 0.001, delta2 = 0.1, predict = FALSE,
file = "fblr_mcmc.txt")
fblr.weight(y, bin, niter, thin = 5, nburn = 10000, kmax = 10, geo = 1,
delta1 = 0.001, delta2 = 0.1, predict = FALSE, group = NULL,
weight = NULL, file = "fblr_mcmc.txt")
|
y |
binary vector indicating case-control status. |
bin |
binary matrix with number of rows equal to |
niter |
number of MCMC iterations after burn-in. |
thin |
after burn-in only every |
nburn |
number of burn-in iterations. |
int.level |
maximum number of binaries allowed in a logic predictor.
Is fixed to 2 for |
kmax |
maximum number of logic predictors allowed in the model. |
geo |
geometric penalty parameter for the number of binaries in a predictor.
Value between 0 and 1. Default is |
delta1 |
shape parameter for hierarchical gamma prior on precision of regression parameters. |
delta2 |
rate parameter for hierarchical gamma prior on precision of regression parameters. |
predict |
should predicted case probabilities be returned? |
file |
character string naming a file to write the MCMC output to. If
|
group |
list containing vectors of indices of binaries that are allowed to interact. Groups may be overlapping, but every binary has to be in at least one group. Groups have to contain at least two binaries. Defaults to NULL, meaning that all interactions are allowed. |
weight |
vector of length |
The MCMC output in file
can be analysed using the function
analyse.models
. In the help of this function it is also described how
the models are stored in file
.
accept |
acceptance rate of MCMC algorithm. |
pred |
vector of predicted case probabilities. Only given if
|
Arno Fritsch, arno.fritsch@uni-dortmund.de
Fritsch, A. and Ickstadt, K.\ (2007). Comparing logic regression based methods for identifying SNP interactions. In Bioinformatics in Research and Development, Hochreiter, S.\ and Wagner, R.\ (Eds.), Springer, Berlin.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
# SNP dataset with 500 persons and 20 SNPs each,
# a two-SNP interaction influences the case probability
snp <- matrix(rbinom(500*20,2,0.3),ncol=20)
bin <- snp2bin(snp)
int <- apply(bin,1,function(x) (x[1] == 1 & x[3] == 0)*1)
case.prob <- exp(-0.5+log(5)*int)/(1+exp(-0.5+log(5)*int))
y <- rbinom(nrow(snp),1,prob=case.prob)
# normally more iterations should be used
fblr(y, bin, niter=1000, nburn=0)
analyse.models("fblr_mcmc.txt")
# Prior information: SNPs 1-10 belong to genes in one pathway,
# SNPs 8-20 to another. Only interactions within a pathway are
# considered and the first pathway is deemed to be twice as
# important than the second.
fblr.weight(y,bin,niter=1000, nburn=0, group=list(1:20, 15:40),
weight=c(rep(2,20),rep(1,20)))
analyse.models("fblr_mcmc.txt")
## End(Not run)
|
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