Description Usage Arguments Details Value Author(s) References See Also Examples
Yields fitted values or predicted class labels for training
and test data, which are based on the supervised gene clusters
wilma
found, and on a choice of four different classifiers: the
nearestneighbor rule, diagonal linear discriminant analysis, logistic
regression and aggregated trees.
1 2 3 4 
object 
an R object of 
newdata 
numeric matrix with the same number of explanatory
variables as the original 
type 
character string describing whether fitted values

classifier 
character string specifying which classifier should
be used. Choices are 
noc 
integer specifying how many clusters the fitted values or class label predictions should be determined. Also numeric vectors are allowed as an argument. The output is then a numeric matrix with fitted values or class label predictions for a multiple number of clusters. 
... 
further arguments passed to and from methods. 
If newdata = NULL
, then the insample fitted values or class
label predictions are returned.
Depending on whether noc
is a single number or a numeric
vector. In the first case, a numeric vector of length r is
returned, which contains fitted values for noc
clusters, or
class label predictions with noc
clusters.
In the latter case, a numeric matrix with length(noc)
columns,
each containing fitted values for noc
clusters, or class label
predictions with noc
clusters, is returned.
Marcel Dettling, [email protected]
Marcel Dettling (2002) Supervised Clustering of Genes, see http://stat.ethz.ch/~dettling/supercluster.html
Marcel Dettling and Peter B<c3><bc>hlmann (2002). Supervised Clustering of Genes. Genome Biology, 3(12): research0069.10069.15.
wilma
and for the four classifiers,
nnr
, dlda
, logreg
,
aggtrees
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## Working with a "real" microarray dataset
data(leukemia, package="supclust")
## Generating random test data: 3 observations and 250 variables (genes)
set.seed(724)
xN < matrix(rnorm(750), nrow = 3, ncol = 250)
## Fitting Wilma
fit < wilma(leukemia.x, leukemia.y, noc = 3, trace = 1)
## Fitted values and class predictions for the training data
predict(fit, type = "cla")
predict(fit, type = "fitt")
## Predicting fitted values and class labels for test data
predict(fit, newdata = xN)
predict(fit, newdata = xN, type = "cla", classifier = "nnr", noc = c(1,2,3))
predict(fit, newdata = xN, type = "cla", classifier = "dlda", noc = c(1,3))
predict(fit, newdata = xN, type = "cla", classifier = "logreg")
predict(fit, newdata = xN, type = "cla", classifier = "aggtrees")

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