mdplot: Mean-Difference Plot

In hdeberg/limma: Linear Models for Microarray Data

Description

Creates a mean-difference plot of two columns of a matrix.

Usage

mdplot(x, columns=c(1,2), xlab="Mean", ylab="Difference", main=NULL, ...)

Arguments

x	numeric matrix with at least two columns.
columns	which columns of x to compare. Plot will display second minus first.
xlab	label for the x-axis.
ylab	label for the y-axis.
main	title of the plot. Defaults to
...	any other arguments are passed to plotWithHighlights.

Details

Plots differences vs means for a set of bivariate values. This is a generally useful approach for comparing two correlated measures of the same underlying phenomenon. Bland and Altman (1986) argue it is more information than a simple scatterplot of the two variables. The bivariate values are stored as columns of x.

Value

A plot is created on the current graphics device.

Author(s)

Gordon Smyth

References

Cleveland, W. S., (1993). Visualizing Data. Hobart Press.

Bland, J. M., and Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327, 307-310.

See also http://www.statsci.org/micrarra/refs/maplots.html

See Also

plotWithHighlights

plotMD is an object-oriented implementation of mean-difference plots for expression data.

An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.

Examples

x1 <- runif(100)
x2 <- (x1 + rnorm(100,sd=0.01))^1.2
oldpar <- par(mfrow=c(1,2))
plot(x1,x2)
mdplot(cbind(x1,x2),bg.pch=1,bg.cex=1)
par(oldpar)

hdeberg/limma documentation built on Dec. 20, 2021, 3:43 p.m.