R: Classification of unclustered points

classifdist {fpc} | R Documentation

## Classification of unclustered points

### Description

Various methods for classification of unclustered points from clustered points for use within functions `nselectboot` and `prediction.strength`.

### Usage

    classifdist(cdist,clustering,
                          method="averagedist",
                          centroids=NULL,nnk=1)

    classifnp(data,clustering,
                          method="centroid",cdist=NULL,
                          centroids=NULL,nnk=1)

### Arguments

`cdist` | dissimilarity matrix or `dist`-object. Necessary for `classifdist` but optional for `classifnp` and there only used if `method="averagedist"` (if not provided, `dist` is applied to `data`).
`data` | something that can be coerced into a an `n*p`-data matrix.
`clustering` | integer vector. Gives the cluster number (between 1 and k for k clusters) for clustered points and should be -1 for points to be classified.
`method` | one of `"averagedist", "centroid", "qda", "knn"`. See details.
`centroids` | for `classifnp` a k times p matrix of cluster centroids. For `classifdist` a vector of numbers of centroid objects as provided by `pam`. Only used if `method="centroid"`; in that case mandatory for `classifdist` but optional for `classifnp`, where cluster mean vectors are computed if `centroids=NULL`.
`nnk` | number of nearest neighbours if `method="knn"`.

### Details

`classifdist` is for data given as dissimilarity matrix, `classifnp` is for data given as n times p data matrix. The following methods are supported:

"centroid"

  assigns observations to the cluster with closest cluster centroid as specified in argument `centroids` (this is associated to k-means and pam/clara-clustering).

"averagedist"

  assigns to the cluster to which an observation has the minimum average dissimilarity to all points in the cluster (this is associated to average linkage clustering).

"qda"

  only in `classifnp`. Classifies by quadratic discriminant analysis (this is associated to Gaussian clusters with flexible covariance matrices), calling `qda` with default settings. If `qda` gives an error (usually because a class was too small), `lda` is used.

"knn"

  classifies by `nnk` nearest neighbours (for `nnk=1`, this is associated with single linkage clustering). Calls `knn` in `classifnp`.

### Value

An integer vector giving cluster numbers for all observations; those for the observations already clustered in the input are the same as in the input.

### Author(s)

Christian Hennig c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/

### See Also

`prediction.strength`, `nselectboot`

### Examples

    set.seed(20000)
    x1 <- rnorm(50)
    y <- rnorm(100)
    x2 <- rnorm(40,mean=20)
    x3 <- rnorm(10,mean=25,sd=100)
    x <- cbind(c(x1,x2,x3),y)
    truec <- c(rep(1,50),rep(2,40),rep(3,10))
    topredict <- c(1,2,51,52,91)
    clumin <- truec
    clumin[topredict] <- -1

    classifnp(x,clumin, method="averagedist")
    classifnp(x,clumin, method="qda")
    classifdist(dist(x),clumin, centroids=c(3,53,93),method="centroid")
    classifdist(dist(x),clumin,method="knn")

[Package fpc version 2.1-10 Index]