crlmm (version 1.30.0)

xyplot: Plot prediction regions and normalized intensities.

Description

Plot prediction regions for integer copy number and normalized intensities.

Usage

xyplot(x, data, ...)

Arguments

x

A formula.

data

A CNSet object.

...

Additional arguments passed to xyplot function in lattice.

Value

A trellis object.

See Also

xyplot, 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
## 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)