RJaCGH (version 2.0.4)

states: 'states' method for RJaCGH objects

Description

Methods for estimating the hidden state sequence of a RJaCGH model.

Usage

states(obj, array=NULL, Chrom=NULL, k=NULL)
"states"(obj, array=NULL, Chrom=NULL, k=NULL)

Arguments

obj

any of RJaCGH, RJaCGH.Chrom, RJaCGH.Genome objects

array

vector of arrays to get the states from.

Chrom

vector of chromosomes to get the states from.

k

Model to summarize (i.e., number of hidden states). If NULL, the most visited is taken.

Value

states

Factor with the hidden state sequence

prob.states

Matrix with the probabilities associated to every states for every observation.

Details

The posterior probability of the hidden state sequence is computed via viterbi.

The state with more observatios is called 'Normal'. Those with bigger means than it are called 'Gain', 'Gain1'... and those with lesser means are called 'Loss', 'Loss1',...

Depending on the hierarchy of the object, it can return lists with sublists, as in RJaCGH.

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, modelAveraging, plot.RJaCGH, trace.plot,

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(0.05, 4), sigma.tau.sigma.2=rep(0.03, 4),
           sigma.tau.beta=rep(0.07, 4), tau.split.mu=0.1, tau.split.beta=0.1)
fit.genome <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Genome",
burnin=100, TOT=1000, jump.parameters=jp, k.max = 4)
states(fit.genome)