smoothMeans {RJaCGH} | R Documentation |
Smoothed posterior mean for every probe after fitting a RJaCGH model.
smoothMeans(obj, k = NULL) ## S3 method for class 'RJaCGH': smoothMeans(obj, k=NULL) ## S3 method for class 'RJaCGH.Chrom': smoothMeans(obj, k=NULL) ## S3 method for class 'RJaCGH.genome': smoothMeans(obj, k=NULL) ## S3 method for class 'RJaCGH.array': smoothMeans(obj, k=NULL)
obj |
An RJaCGH object, of class 'RJaCGH',
'RJaCGH.Chrom', 'RJaCGH.genome' or 'RJaCGH.array'.
|
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 this smoothed means are computed for every
model and averaged by the posterior probability of each model.
For class 'RJaCGH', 'RJaCGH.Chrom' and 'RJaCGH.genome' a vector with the smoothed means for every probe. For class 'RJaCGH.array' a list with as many elements as arrays, each one a vector with the smoothed means for that array.
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
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)