modelAveraging: Method for model averaging for RJaCGH objects.

In RJaCGH: Reversible Jump MCMC for the Analysis of CGH Arrays

Description

Bayesian model averaging for the estimation of hidden state sequence.

Usage

modelAveraging(obj, array=NULL, Chrom=NULL)
## S3 method for class 'RJaCGH'
modelAveraging(obj, array=NULL, Chrom=NULL)

Arguments

obj	An object of corresponding class
array	Array to be used. If NULL, all of them are used.
Chrom	Vector of chromosomes to be used. If NULL, all of them are used.

Details

With the posterior distribution of the number of hidden states, bayesian model averaging is performed on every model using states method.

As the other methods, it may return a list with sublists according to the hierarchy of RJaCGH objects.

Value

states	Factor with the hidden state sequence
prob.states	Matrix with the probabilities associated to every states for every observation.

Author(s)

Oscar M. Rueda and Ramon Diaz Uriarte

References

Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122

See Also

RJaCGH, summary.RJaCGH, states, plot.RJaCGH, trace.plot

Examples

## Not run: y <- c(rnorm(100, 0, 1), rnorm(10, -3, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
Pos <- sample(x=1:500, size=230, replace=TRUE)
Pos <- cumsum(Pos)
Chrom <- rep(1:23, rep(10, 23))

jp <- list(sigma.tau.mu=rep(0.5, 5), sigma.tau.sigma.2=rep(0.3, 5),
sigma.tau.beta=rep(0.7, 5), tau.split.mu=0.5, tau.split.beta=0.5)
fit.genome <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Genome",
burnin=1000, TOT=10000, jump.parameters=jp, max.k=5)
mo <- modelAveraging(fit.genome)
print(mo)
## End(Not run)

RJaCGH documentation built on May 2, 2019, 3:34 p.m.