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 |
data |
something that can be coerced into a an
|
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 |
centroids |
for |
nnk |
number of nearest neighbours if |
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).- "qda"
only in
classifnp
. Classifies by quadratic discriminant analysis (this is associated to Gaussian clusters with flexible covariance matrices), callingqda
with default settings. Ifqda
gives an error (usually because a class was too small),lda
is used.- "lda"
only in
classifnp
. Classifies by linear discriminant analysis (this is associated to Gaussian clusters with equal covariance matrices), callinglda
with default settings.- "averagedist"
assigns to the cluster to which an observation has the minimum average dissimilarity to all points in the cluster (this is associated with average linkage clustering).
- "knn"
classifies by
nnk
nearest neighbours (fornnk=1
, this is associated with single linkage clustering). Callsknn
inclassifnp
.- "fn"
classifies by the minimum distance to the farthest neighbour. This is associated with complete linkage clustering).
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 christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/
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")