model.averaging: 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. As the other methods, it may return a list with sublists according to the hierarchy of RJaCGH objects. states{Factor with the hidden state sequence} prob.states{Matrix with the probabilities associated to every states for every observation.} Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122 [object Object],[object Object]

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

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)

models