stam.cv {stam} | R Documentation

## Cross Validated Training for StAM

### Description

Determine classifiers in leaf nodes and weights in inner nodes as well as best graph shrinkage by cross validated model fitting.

### Usage

    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)

### Arguments

`expression.matrix` | holds the expression levels. It may be of class exprSet 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`, 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.

### Details

`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.

### Value

An object of class stamCV is returned. Use the methods print and plot to extract information about the cross validation.

### Author(s)

Claudio Lottaz

### See Also

`stamCV-class`, `plot.stamCV`, `stam.writeHTML`

### Examples

    # load and prepare some data
    library(vsn)
    library(golubEsets)
    data(golubMerge)
    golubNorm <- vsn(golubMerge, describe.preprocessing=FALSE)
    golubTrain <- golubNorm[,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)

[Package stam version 1.0.3 Index]