plotRLDF {limma} | R Documentation |
Plot of regularized linear discriminant functions for microarray data.
plotRLDF(y,design=NULL,z=NULL,labels.y=NULL,labels.z=NULL,col.y=1,col.z=1, df.prior=5,show.dimensions=c(1,2),main=NULL,nprobes=500,...)
y |
any data object which can be coerced to a matrix, such as |
z |
any data object which can be coerced to a matrix, such as |
design |
the design matrix ofthe microarray experiment for |
labels.y |
character vector of sample names or labels in |
labels.z |
character vector of sample names or labels in |
col.y |
numeric or character vector of colors for the plotting characters of |
col.z |
numeric or character vector of colors for the plotting characters of |
df.prior |
prior degrees of freedom for residual variances. Used in gene selection. |
show.dimensions |
which two dimensions should be plotted, numeric vector of length two. |
main |
title of the plot. |
nprobes |
number of probes to be used for the calculations. Selected by moderated F tests. |
... |
any other arguments are passed to |
This function is a variation on the plot of usual linear discriminant fuction, in that the within-group covariance matrix is regularized to ensure that it is invertible, with eigenvalues bounded away from zero.
A diagonal regulation using df.prior
and the median within-group variance is used.
The calculations are based on a filtered list of probes.
The nprobes
probes with largest moderated F statistics are used to discriminate.
See text
for possible values for col
and cex
.
A list containing metagene information is (invisibly) returned. A plot is created on the current graphics device.
Di Wu and Gordon Smyth
lda
in package MASS
# Simulate gene expression data for 1000 probes and 6 microarrays. # Samples are in two groups # First 50 probes are differentially expressed in second group sd <- 0.3*sqrt(4/rchisq(1000,df=4)) y <- matrix(rnorm(1000*6,sd=sd),1000,6) rownames(y) <- paste("Gene",1:1000) y[1:50,4:6] <- y[1:50,4:6] + 2 z <- matrix(rnorm(1000*6,sd=sd),1000,6) rownames(z) <- paste("Gene",1:1000) z[1:50,4:6] <- z[1:50,4:6] + 1.8 z[1:50,1:3] <- z[1:50,1:3] - 0.2 design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1)) options(digit=3) plotRLDF(y,z, design=design)