predict.wilma {supclust} | R Documentation |
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
nearest-neighbor rule, diagonal linear discriminant analysis, logistic
regression and aggregated trees.
predict.wilma(object, newdata = NULL, type = c("fitted", "class"), classifier = c("nnr", "dlda", "logreg", "aggtrees"), noc = object$noc, ...)
object |
An R object of class "wilma" ,
typically the result of wilma() . |
newdata |
Numeric matrix with the same number of explanatory
variables as the original x -matrix (p variables in
columns, r cases in rows). For example, these can be
additional microarray gene expression data which should be
predicted. |
type |
Character string, describing whether fitted values
"fitted" or predicted class labels "class" should be
returned. |
classifier |
Character string, saying which classifier should be
used. Choices are "nnr" , the 1-nearest-neighbor-rule;
"dlda" , diagonal linear discriminant analysis;
"logreg" , logistic regression; "aggtrees" aggregated
trees. |
noc |
Integer, saying with 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. |
... |
If newdata = NULL
, then the in-sample 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, dettling@stat.math.ethz.ch
Marcel Dettling (2002) Supervised Clustering of Genes, see http://stat.ethz.ch/~dettling/supercluster.html
Marcel Dettling and Peter Bühlmann (2002). Supervised Clustering of Genes. Genome Biology, 3(12): research0069.1-0069.15.
wilma
and for the four classifiers,
nnr
, dlda
, logreg
,
aggtrees
.
## 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")