bga.suppl
performs a bga
between group analysis with projection
of supplementary points using suppl
bga.suppl(dataset, supdata, classvec, supvec = NULL, suponly = FALSE, type="coa", ...)
matrix
, data.frame
,
ExpressionSet
or
marrayRaw-class
.
If the input is gene expression data in a matrix
or data.frame
. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively.matrix
, data.frame
,
ExpressionSet
or
marrayRaw-class
.
If the input is gene expression data in a matrix
or data.frame
. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively. The test dataset supdata
and the training dataset dataset
must contain
the same number of variables (genes).factor
or vector
which describes the classes in the
training data dataset
.factor
or vector
which describes the classes in the
test dataset supdata
.FALSE
, that
is the training coordinates, test coordinates and class assignments
will all be returned.suponly
is FALSE (the default option) bga.suppl
returns a list of length 4 containing
the results of the bga
of the training dataset and the results of the projection of the test dataset onto the bga axes-
dudi
). dudi
),"between" (see bca
),and
"dudi.bga"(see bga
)suppl
suponly
is TRUE only the results from suppl
will be returned.
bga.suppl
calls bga
to perform between group analysis (bga) on the training dataset,
then it calls suppl
to project the test dataset onto the bga axes.
It returns the coordinates and class assignment of the cases (microarray samples) in the test dataset as
described by Culhane et al., 2002. 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.
bga
,
suppl
, bca
,
plot.bga
, bga.jackknife
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
}
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