RJaCGH (version 1.1.1)

states: 'states' method for RJaCGH objects

Description

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

Usage

states(obj, k)
states.RJaCGH(obj, k=NULL)
states.RJaCGH.Chrom(obj, k=NULL)
states.RJaCGH.genome(obj, k=NULL)
states.RJaCGH.array(obj, k=NULL)

Arguments

obj

any of RJaCGH, RJaCGH.Chrom, RJaCGH.genome, RJaCGH.array objects

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

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, trace.plot, gelman.brooks.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(0.05, 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)