RJaCGH (version 1.5.7)

summary.RJaCGH: Summarizing RJaCGH models

Description

'summary' method for objects of class 'RJaCGH', 'RJaCGH.Chrom', 'RJaCGH.Genome' and 'RJaCGH.array'.

Usage

## S3 method for class 'RJaCGH':
summary(object, k = NULL, point.estimator = "median",
quantiles=NULL, \ldots)
## S3 method for class 'RJaCGH.Chrom':
summary(object, point.estimator="median",
Chrom=NULL,quantiles=NULL,  \ldots)
## S3 method for class 'RJaCGH.Genome':
summary(object, k=NULL,
point.estimator="median",
quantiles=NULL, \ldots)
## S3 method for class 'RJaCGH.array':
summary(object, point.estimator="median",
quantiles=NULL, \ldots)

Arguments

object

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.

point.estimator

Type of point estimator for mu, sigma.2 and beta. It can be "mean", "median" or "mode".

Chrom

If object has a different model for every chromosome, this argument is the chromosome to get summaries from. If NULL, all chromosomes will be summarized.

quantiles

A vector of probabilities for the quantiles of the posterior distribution of means and variances.

...

Additional arguments passed to summary.

Value

• k
  Frequencies of the hidden states visited by the sampler.
• mu
  Quantiles of the posterior distribution of mu
• sigma.2
  Quantiles of the posterior distribution of sigma.2
• beta
  Point estimator of beta
• stat
  Initial distribution of the hidden states.

Details

Depending of the type of object, a list with contains sublists can be returned, similarly to RJaCGH and similar objects of the family. The point estimator "mode" is simply the max value obtained in a kernel density estimation through the function density

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, states, model.averaging, plot.RJaCGH, trace.plot, gelman.rubin.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(0.5, 5), sigma.tau.sigma.2=rep(0.3, 5),
sigma.tau.beta=rep(0.7, 5), tau.split.mu=0.5)
fit.chrom <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Chrom",
burnin=10, TOT=100, jump.parameters=jp, k.max = 5)
summary(fit.chrom)