RJaCGH (version 1.1.1)

chainsSelect: Select between several parallel chains ('RJaCGH' objects)

Description

method to remove outlier chains ('RJaCGH' objects') from a set of parallel RJMCMC chains.

Usage

chainsSelect(obj, nutrim=4, trim=NULL)
## S3 method for class 'RJaCGH':
chainsSelect(obj, nutrim=4, trim=NULL)
## S3 method for class 'RJaCGH.genome':
chainsSelect(obj, nutrim = 4, trim = NULL)
## S3 method for class 'RJaCGH.array':
chainsSelect(obj, nutrim = 4, trim = NULL)

Arguments

obj

a list containing several parallel chains; that is objects of any of RJaCGH, RJaCGH.genome, RJaCGH.array classes (obviously, all of the same class).

nutrim

Number of chains to remove.

trim

Proportion of chains to remove. This or nutrim must be passed to the method.

Value

• A list of less elements of the same class asobj.

Details

In Reversible Jump MCMC, there may be occasions in that a chain can get trapped in a model. If this happens, Gelman-brooks - see gelman.brooks.plot diagnostics for k, the number of states is not defined. This method removes the chains whose k trajectories are most unusual compared to the rest. In the current version of RJaCGH, this method is not defined for chromosome models (RJaCGH.Chrom). After removing otilers chains, one can run gelman.brooks.plot to check for convergnce of the remaining chains and later collapseChain to join them, or directly this last function.

References

Oscar M. Rueda and Ramon Diaz Uriarte. A flexible, accurate and extensible statistical method for detecting genomic copy-number changes. http://biostats.bepress.com/cobra/ps/art9/. {http://biostats.bepress.com/cobra/ps/art9/}.

See Also

RJaCGH, summary.RJaCGH, model.averaging, plot.RJaCGH, states, trace.plot, collapseChain

Examples

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, 4), sigma.tau.sigma.2=rep(0.3, 4),
           sigma.tau.beta=rep(0.7, 4), tau.split.mu=0.5, tau.split.beta=0.5)

fit.chrom <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Chrom",
                    burnin=10, TOT=1000, k.max = 4,
                    jump.parameters=jp)
fit.genome <- list()
for (i in 1:4) {
fit.genome[[i]] <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="genome",
burnin=10, TOT=1000, jump.parameters=jp, k.max = 4)
fit.genome <- chainsSelect(fit.genome, nutrim=1)
}