Smoothed posterior mean for every probe after fitting a RJaCGH model.

1 2 3 | ```
smoothMeans(obj, array=NULL, Chrom = NULL, k = NULL)
## S3 method for class 'RJaCGH'
smoothMeans(obj, array=NULL, Chrom = NULL, k=NULL)
``` |

`obj` |
An |

`array` |
Vector of names of the array to get smoothed means. If NULL, all of them. |

`Chrom` |
Vector of the chromosomes. If NULL, all of them. |

`k` |
Number of states (or model) to get the smoothed means from. If NULL, Bayesian Model Averaging is used. |

For a model with *k* hidden states, the mean from the MCMC samples
from *mu* is computed for every hidden state.
Then, for every probe these means are averaged by its posterior
probability of belonging to every hidden state.
If `k`

is NULL, then these smoothed means are computed for every
model and averaged by the posterior probability of each model.

A matrix. Columns of the matrix are arrays (i.e., for an RJaCGH object with a single array, the value is a one column matrix). Each column contains the smoothed means for every probe.

Oscar M. Rueda and Ramon Diaz Uriarte

Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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.genome <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Genome",
burnin=10, TOT=1000, k.max = 4,
jump.parameters=jp)
plot(y~Pos)
lines(smoothMeans(fit.genome) ~ Pos)
``` |

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