stam.cv {stam} | R Documentation |
Determine classifiers in leaf nodes and weights in inner nodes as well as best graph shrinkage by cross validated model fitting.
stam.cv(expression.matrix, classifications, chip = "hgu95av2", root = "GO:0008150", beta = NULL, deltas = NULL, ndeltas = 10, results.per.node = FALSE, old.cv = NULL, pamimagefile = NULL, verbose = FALSE)
expression.matrix |
holds the expression levels. It may be of
classes exprSet or ExpressionSet, or a
plain numeric matrix. In the first case exprs is used to
extract the expression levels. The matrix is expected to hold one
column per sample and one row per probeset. |
classifications |
This character vector must contain one entry per sample
identifying the group it belongs to. Alternatively, if expression.matrix is
an exprSet or ExpressionSet , this may be the name of
a phenoData variable. |
chip |
the name of the microarray chip. A meta data package is expected to be found holding the needed annotation, namely the links between probesets and Gene Ontology nodes. |
root |
the GO node used as root of the classifier graph. Only successors of this node are considered during construction of the graph. |
beta |
holds class weights used when judging classifier quality. The default is to set class weights to the corresponding prevalence. |
deltas |
numeric vector holding graph shrinkage candidates. Default is to
determine ndelta candidates between 0 and the lowest shrinkage level which
removes all leaf nodes. |
ndeltas |
number of automatically determined graph shrinkage candidates determined
if deltas is not defined. |
results.per.node |
whether results for each node should be returned |
old.cv |
stamCV object used to modify when PAM fits need not to be
recomputed. E.g. used when only beta is adapted. |
pamimagefile |
When this parameter is specified stam.cv tries to read this
file and extract a stamCV object to be used as old.cv . If the file does
not yet exist, PAM fits are stored there after computation. |
verbose |
when set to TRUE reports summary on each leaf training, otherwise shows a progress bar. |
stam.cv
uses stam.net
to generate a classifier graph for the microarray
chip at hand. It then fits a PAM classifier for each leaf node only considering the
probesets annotated to the node. Afterwards, in each inner node, weights are attributed
to each child according to the childs classification performance. Finally, the weights
are shrunken such that most of them become zero. In fact, the best shrinkage level is
chosen in a cross validation setting.
Classification performance is evaluated using an inverted deviance like measure which uses weights to overstate specificity of a classifier. Weights for nodes are chosen according to this measure and shrunken by an absolute shrinkage level. For each shrinkage candidate cross validated performance results in terms of graph heterogeneity and classification performance are stored.
An object of class stamCV is returned. Use the methods print and plot to extract information about the cross validation.
Claudio Lottaz
stamCV-class
, plot.stamCV
,
stam.writeHTML
# load and prepare some data library(golubEsets) data(Golub_Merge) golubTrain <- Golub_Merge[,1:38] # classify into ALL and AML # (root is chosen to yield results reasonably fast, # consider GO:0008150 (biological process) to obtain # meaningful results) golubTrain.cv <- stam.cv(golubTrain, "ALL.AML", chip="hu6800", root="GO:0005576", ndeltas=10) # get further information print(golubTrain.cv) plot(golubTrain.cv, delta=0.6)