Learn R Programming

made4 (version 1.46.0)

suppl: Projection of supplementary data onto axes from a between group analysis

Description

Projection and class prediction of supplementary points onto axes from a between group analysis, bga.

Usage

suppl(dudi.bga, supdata, supvec = NULL, assign=TRUE, ...) "plot"(x, dudi.bga, axis1=1, axis2=2, supvec=sup$true.class, supvec.pred= sup$predicted, ...)

Arguments

dudi.bga
An object returned by bga.
supdata
Test or blind dataset. Accepted formats are a matrix, data.frame, ExpressionSet or marrayRaw-class.
supvec
A factor or vector which describes the classes in the training dataset.
supvec.pred
A factor or vector which describes the classes which were predicted by suppl.
assign
A logical indicating whether class assignment should be calculated using the method described by Culhane et al., 2002. The default value is TRUE.
x
An object returned by suppl.
axis1
Integer, the column number for the x-axis. The default is 1.
axis2
Integer, the column number for the y-axis. The default is 2.
...
further arguments passed to or from other methods.

Value

A list containing:
suppl
An object returned by suppl

Details

After performing a between group analysis on a training dataset using bga, a test dataset can be then projected onto bga axes using suppl.

suppl returns the projected coordinates and assignment of each test case (array). The test dataset must contain the same number of variables (genes) as the training dataset. The input format of both the training dataset and test dataset are verified using array2ade4. Use plot.bga to plot results from bga.

References

Culhane AC, et al., 2002 Between-group analysis of microarray data. Bioinformatics. 18(12):1600-8.

See Also

See Also bga, bca, plot.bga, bga.jackknife

Examples

Run this code
data(khan)
#khan.bga<-bga(khan$train, khan$train.classes)
if (require(ade4, quiet = TRUE)) {
khan.bga<-bga.suppl(khan$train, supdata=khan$test, classvec=khan$train.classes, 
          supvec=khan$test.classes)

khan.bga
plot.bga(khan.bga, genelabels=khan$annotation$Symbol)
khan.bga$suppl

plot.suppl(khan.bga$suppl, khan.bga) 
plot.suppl(khan.bga$suppl, khan.bga, supvec=NULL, supvec.pred=NULL)
plot.suppl(khan.bga$suppl, khan.bga, axis1=2, axis2=3,supvec=NULL, supvec.pred=NULL)
}

Run the code above in your browser using DataLab