pknnCMA {CMA} | R Documentation

## Probabilistic Nearest Neighbours

### Description

Nearest neighbour variant that replaces the simple voting scheme by a weighted one (based on euclidean distances). This is also used to compute class probabilities.

For `S4` class information, see pknnCMA-methods.

### Usage

    pknnCMA(X, y, f, learnind, beta = 1, k = 1, models=FALSE, ...)

### Arguments

`X` | Gene expression data. Can be one of the following: A `matrix`. Rows correspond to observations, columns to variables. A `data.frame`, when `f` is not missing (s. below). An object of class `ExpressionSet`.
`y` | Class labels. Can be one of the following: A `numeric` vector. A `factor`. A `character` if `X` is an `ExpressionSet` that specifies the phenotype variable. `missing`, if `X` is a `data.frame` and a proper formula `f` is provided. WARNING: The class labels will be re-coded to range from `0` to `K-1`, where `K` is the total number of different classes in the learning set.
`f` | A two-sided formula, if `X` is a `data.frame`. The left part correspond to class labels, the right to variables.
`learnind` | An index vector specifying the observations that belong to the learning set. Must not be missing for this method.
`beta` | Slope parameter for the logistic function which is used for the computation of class probabilities. The default value (1) need not produce reasonable results and can produce warnings.
`k` | Number of nearest neighbours to use.
`models` | a logical value indicating whether the model object shall be returned
`...` | Currently unused argument.

### Details

The algorithm is as follows:

- Determine the `k` nearest neighbours

- For each class represented among these, compute the average euclidean distance.

- The negative distances are plugged into the logistic function with parameter `beta`.

- Classify into the class with highest probability.

### Value

An object of class `cloutput`.

### Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

### See Also

`compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `nnetCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `pnnCMA`, `qdaCMA`, `rfCMA`, `scdaCMA`, `shrinkldaCMA`, `svmCMA`

### Examples

    ### load Golub AML/ALL data
    data(golub)
    ### extract class labels
    golubY <- golub[,1]
    ### extract gene expression from first 10 genes
    golubX <- as.matrix(golub[,-1])
    ### select learningset
    ratio <- 2/3
    set.seed(111)
    learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
    ### run probabilistic k-nearest neighbours
    result <- pknnCMA(X=golubX, y=golubY, learnind=learnind, k = 3)
    ### show results
    show(result)
    ftable(result)
    plot(result)

[Package CMA version 1.38.0 Index]