model.averaging {RJaCGH}R Documentation

Method for model averaging for RJaCGH objects.

Description

Bayesian model averaging for the estimation of hidden state sequence.

Usage

model.averaging(obj)
## S3 method for class 'RJaCGH':
model.averaging(obj)
## S3 method for class 'RJaCGH.Chrom':
model.averaging(obj)
## S3 method for class 'RJaCGH.genome':
model.averaging(obj)
## S3 method for class 'RJaCGH.array':
model.averaging(obj)

Arguments

obj An object of corresponding class

Details

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

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, states, plot.RJaCGH, trace.plot, gelman.brooks.plot, collapseChain

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 <- model.averaging(fit.genome)
print(mo)## End(Not run)


[Package RJaCGH version 1.0.2 Index]