\name{xyplot} \alias{xyplot} \alias{xyplot,formula,CNSet-method} \title{Plot prediction regions and normalized intensities.} \description{ Plot prediction regions for integer copy number and normalized intensities. } \usage{ xyplot(x, data, ...) } \arguments{ \item{x}{ A \code{formula}. } \item{data}{ A \code{CNSet} object. } \item{\dots}{ Additional arguments passed to \code{xyplot} function in lattice. } } \value{ A \code{trellis} object. } \author{ R. Scharpf } \seealso{ \code{\link[lattice]{xyplot}}, \code{\link{ABpanel}} } \examples{ library(oligoClasses) data(cnSetExample2) table(batch(cnSetExample2)) sample.index <- which(batch(cnSetExample2) == "CUPID") ## A single SNP pr <- predictionRegion(cnSetExample2[1:4, sample.index], copyNumber=0:4) gt <- calls(cnSetExample2[1:4, sample.index]) lim <- c(6,13) xyplot(B~A|snpid, data=cnSetExample2[1:4, sample.index], predictRegion=pr, panel=ABpanel, pch=21, fill=c("red", "blue", "green3")[gt], xlim=lim, ylim=lim) ## multiple SNPs, prediction regions for 3 batches \dontrun{ tab <- table(batch(cnSetExample2)) bns <- names(tab)[tab > 50] sample.index <- which(batch(cnSetExample2) %in% bns[1:3]) pr <- predictionRegion(cnSetExample2[1:10, sample.index], copyNumber=0:4) gt <- as.integer(calls(cnSetExample2[1:10, sample.index])) xyplot(B~A|snpid, data=cnSetExample2[1:10, sample.index], predictRegion=pr, panel=ABpanel, pch=21, fill=c("red", "blue", "green3")[gt], xlim=c(6,12), ylim=c(6,12)) ## nonpolymorphic markers data(cnSetExample2) tab <- table(batch(cnSetExample2)) bns <- names(tab)[tab > 50] sample.index <- which(batch(cnSetExample2)%in%bns[1:3]) np.index <- which(!isSnp(cnSetExample2))[1:10] taus <- tau2(cnSetExample)[np.index, , , ] pr <- predictionRegion(cnSetExample2[np.index, sample.index], copyNumber=0:4) pp <- posteriorProbability(cnSetExample2[np.index, sample.index], predictRegion=pr, copyNumber=0:4) } } \keyword{dplot} \keyword{hplot}