preds.at {Pigengene} | R Documentation

## Prediction using a possibly compacted tree

### Description

A decision tree in Pigengene uses module eigengenes to build a classifier that distincuishes two or more classes. Each eigengene is a weighted average of the expression of all genes in the module, where the weight of each gene corresponds to its membership in the module. Each modules might contain dozens to hundreds of genes, and hence the final classifier might depend on the expression of a large number of genes. In practice, it can be desireable to reduce the number of necessary genes used by a decision tree. This function is helpful in observing changes to the classification output after removing genes with lower weights membership. It determines how a given decision tree would classify the expression data after removing a certain number of genes from consideration.

### Usage

    preds.at(c5Tree, pigengene, pos=0, Data)

### Arguments

`c5Tree` | A decision tree that uses eigengenes from the pigengene object to classify the samples from the expression data.
`pigengene` | A object of `pigengene-class`, output of `compute.pigengene`
`pos` | Number of genes to be removed from the consideration. Genes are removed in ascending order of their absolute weight in the relevant modules. If 0 (default), the prediction will be done without compacting.
`Data` | The expression possibly new data used for classification

### Value

A list with following components:

`predictions` | The vector of predictions after neglecting `pos` number of genes
`eigengenes` | The values for the eigenges after neglecting `pos` number of genes

### See Also

`Pigengene-package`, `pigengene-class`, `make.decision.tree`, `compact.tree`, `compute.pigengene`, `module.heatmap`, `get.used.features`, `get.fitted.leaf`, `Pigengene-package`

### Examples

         ## Data:
         data(aml)
         data(mds)
         data(pigengene)
         d1 <- rbind(aml,mds)

         ## Fiting the trees:
         trees <- make.decision.tree(pigengene=pigengene, Data=d1,
           saveDir="trees", minPerLeaf=15, doHeat=FALSE,verbose=3,
           toCompact=FALSE)
         preds1 <- preds.at(c5Tree=trees$c5Trees[["15"]], pigengene=pigengene,
           pos=0, Data=d1)

[Package Pigengene version 1.6.0 Index]