xyplot {crlmm} | R Documentation |
Plot prediction regions for integer copy number and normalized intensities.
xyplot(x, data, ...)
x |
A |
data |
A |
... |
Additional arguments passed to |
A trellis
object.
R. Scharpf
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 ## Not run: tab <- table(batch(cnSetExample2)) bns <- names(tab)[tab > 50] sample.index <- which(batch(cnSetExample2) 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) 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) ## End(Not run)