print.pREC_A {RJaCGH} | R Documentation |
A print method for pREC_A
objects
## S3 method for class 'pREC_A': print(x,...) ## S3 method for class 'pREC_A.none': print(x,...) ## S3 method for class 'pREC_A.Chromosomes': print(x,...)
x |
An object of class pREC_A ,
pREC_A.Chromosomes , pREC_A.RJaCGH.none .
|
... |
Additional arguments passed to print . Currently ignored. |
A data.frame is printed with as many rows as regions found and with columns containing chromosome where the region is, position of start and end of the region, number of genes in it and joint probability.
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
RJaCGH
,
states
, modelAveraging
,
pREC_A
## MCR for a single array: 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) pREC_A(fit.genome, p=0.8, alteration="Gain") pREC_A(fit.genome, p=0.8, alteration="Loss") ##MCR for two arrays: z <- c(rnorm(110, 0, 1), rnorm(20, 3, 1), rnorm(100,0, 1)) fit.array.genome <- RJaCGH(y=cbind(y,z), Pos=Pos, Chrom=Chrom, model="Genome", burnin=100, TOT=1000, jump.parameters=jp, k.max = 4) pREC_A(fit.array.genome, p=0.4, alteration="Gain") pREC_A(fit.array.genome, p=0.4, alteration="Loss")