predictPathClassifier {NetPathMiner} | R Documentation

## Predicts new paths given a pathClassifier model.

### Description

Predicts new paths given a pathClassifier model.

### Usage

    predictPathClassifier(mix, newdata)

### Arguments

`mix` | The result from `pathClassifier`.
`newdata` | A data.frame containing the new paths to be classified.

### Value

A list with the following elements.

`h` | The posterior probabilities for each HME3M component.
`posterior.probs` | The posterior probabilities for HME3M model to classify the response.
`label` | A vector indicating the HME3M cluster membership.
`component` | The HME3M component membership for each pathway.
`path.probabilities` | The 3M path probabilities.
`plr.probabilities` | The PLR predictions for each component.

### Author(s)

Timothy Hancock and Ichigaku Takigawa

### See Also

Other Path clustering & classification methods: `pathClassifier`, `pathCluster`, `pathsToBinary`, `plotClassifierROC`, `plotClusterMatrix`, `plotPathClassifier`, `plotPathCluster`, `predictPathCluster`

### Examples

    	## Prepare a weighted reaction network.
    	## Conver a metabolic network to a reaction network.
     data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.
     rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)

    	## Assign edge weights based on Affymetrix attributes and microarray dataset.
     # Calculate Pearson's correlation.
    	data(ex_microarray)	# Part of ALL dataset.
    	rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph,
    		weight.method = "cor", use.attr="miriam.uniprot",
    		y=factor(colnames(ex_microarray)), bootstrap = FALSE)

    	## Get ranked paths using probabilistic shortest paths.
     ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
    					K=20, minPathSize=6)

    	## Convert paths to binary matrix.
    	ybinpaths <- pathsToBinary(ranked.p)
    	p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)

    	## Just an example of how to predict cluster membership
     pclass.pred <- predictPathCluster(p.class, ybinpaths$paths)

[Package NetPathMiner version 1.16.0 Index]