| model.averaging {RJaCGH} | R Documentation |
Bayesian model averaging for the estimation of hidden state sequence.
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)
obj |
An object of corresponding class |
With the posterior distribution of the number of hidden states,
bayesian model averaging is performed on every model using
states method. newline
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. |
Oscar M. Rueda and Ramon Diaz Uriarte
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/}.
RJaCGH,
summary.RJaCGH, states,
plot.RJaCGH, trace.plot,
gelman.brooks.plot, collapseChain
## Not run: 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)## End(Not run)