fpc (version 2.1-6)

classifdist: 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 d

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="centroi

nnk

number of nearest neighbours if method="knn".

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.

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: [object Object],[object Object],[object Object],[object Object]

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")