- Home
- CRAN
**fpc**: Flexible Procedures for Clustering**clusexpect**: Expected value of the number of times a fixed point cluster...

# Expected value of the number of times a fixed point cluster is found

### Description

A rough approximation of the expectation of the number of times a well
separated fixed point
cluster (FPC) of size `n`

is found in `ir`

fixed point
iterations of `fixreg`

.

### Usage

1 | ```
clusexpect(n, p, cn, ir)
``` |

### Arguments

`n` |
positive integer. Total number of points. |

`p` |
positive integer. Number of independent variables. |

`cn` |
positive integer smaller or equal to |

`ir` |
positive integer. Number of fixed point iterations. |

### Details

The approximation is based on the assumption that a well separated FPC
is found iff all `p+2`

points of the initial coinfiguration come
from the FPC. The value is `ir`

times the probability for
this. For a discussion of this assumption cf. Hennig (2002).

### Value

A number.

### Author(s)

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

### References

Hennig, C. (2002) Fixed point clusters for linear regression:
computation and comparison, *Journal of
Classification* 19, 249-276.

### See Also

`fixreg`

### Examples

1 | ```
round(clusexpect(500,4,150,2000),digits=2)
``` |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.

- adcoord: Asymmetric discriminant coordinates
- adcoord: Asymmetric discriminant coordinates
- ancoord: Asymmetric neighborhood based discriminant coordinates
- ancoord: Asymmetric neighborhood based discriminant coordinates
- awcoord: Asymmetric weighted discriminant coordinates
- awcoord: Asymmetric weighted discriminant coordinates
- batcoord: Bhattacharyya discriminant projection
- batcoord: Bhattacharyya discriminant projection
- bhattacharyya.dist: Bhattacharyya distance between Gaussian distributions
- bhattacharyya.dist: Bhattacharyya distance between Gaussian distributions
- bhattacharyya.matrix: Matrix of pairwise Bhattacharyya distances
- bhattacharyya.matrix: Matrix of pairwise Bhattacharyya distances
- calinhara: Calinski-Harabasz index
- calinhara: Calinski-Harabasz index
- can: Generation of the tuning constant for regression fixed point...
- can: Generation of the tuning constant for regression fixed point...
- cat2bin: Recode nominal variables to binary variables
- cat2bin: Recode nominal variables to binary variables
- classifdist: Classification of unclustered points
- classifdist: Classification of unclustered points
- clucols: Sets of colours and symbols for cluster plotting
- clucols: Sets of colours and symbols for cluster plotting
- clujaccard: Jaccard similarity between logical vectors
- clujaccard: Jaccard similarity between logical vectors
- clusexpect: Expected value of the number of times a fixed point cluster...
- clusexpect: Expected value of the number of times a fixed point cluster...
- clusterboot: Clusterwise cluster stability assessment by resampling
- clusterboot: Clusterwise cluster stability assessment by resampling
- cluster.stats: Cluster validation statistics
- cluster.stats: Cluster validation statistics
- cluster.varstats: Variablewise statistics for clusters
- cluster.varstats: Variablewise statistics for clusters
- cmahal: Generation of tuning constant for Mahalanobis fixed point...
- cmahal: Generation of tuning constant for Mahalanobis fixed point...
- concomp: Connectivity components of an undirected graph
- concomp: Connectivity components of an undirected graph
- confusion: Misclassification probabilities in mixtures
- confusion: Misclassification probabilities in mixtures
- cov.wml: Weighted Covariance Matrices (Maximum Likelihood)
- cov.wml: Weighted Covariance Matrices (Maximum Likelihood)
- cweight: Weight function for AWC
- cweight: Weight function for AWC
- dbscan: DBSCAN density reachability and connectivity clustering
- dbscan: DBSCAN density reachability and connectivity clustering
- dipp.tantrum: Simulates p-value for dip test
- dipp.tantrum: Simulates p-value for dip test
- diptest.multi: Diptest for discriminant coordinate projection
- diptest.multi: Diptest for discriminant coordinate projection
- discrcoord: Discriminant coordinates/canonical variates
- discrcoord: Discriminant coordinates/canonical variates
- discrete.recode: Recodes mixed variables dataset
- discrete.recode: Recodes mixed variables dataset
- discrproj: Linear dimension reduction for classification
- discrproj: Linear dimension reduction for classification
- distancefactor: Factor for dissimilarity of mixed type data
- distancefactor: Factor for dissimilarity of mixed type data
- distcritmulti: Distance based validity criteria for large data sets
- distcritmulti: Distance based validity criteria for large data sets
- dridgeline: Density along the ridgeline
- dridgeline: Density along the ridgeline
- dudahart2: Duda-Hart test for splitting
- dudahart2: Duda-Hart test for splitting
- extract.mixturepars: Extract parameters for certain components from mclust
- extract.mixturepars: Extract parameters for certain components from mclust
- fixmahal: Mahalanobis Fixed Point Clusters
- fixmahal: Mahalanobis Fixed Point Clusters
- fixreg: Linear Regression Fixed Point Clusters
- fixreg: Linear Regression Fixed Point Clusters
- flexmixedruns: Fitting mixed Gaussian/multinomial mixtures with flexmix
- flexmixedruns: Fitting mixed Gaussian/multinomial mixtures with flexmix
- fpclusters: Extracting clusters from fixed point cluster objects
- fpclusters: Extracting clusters from fixed point cluster objects
- fpc-package: fpc package overview
- fpc-package: fpc package overview
- itnumber: Number of regression fixed point cluster iterations
- itnumber: Number of regression fixed point cluster iterations
- jittervar: Jitter variables in a data matrix
- jittervar: Jitter variables in a data matrix
- kmeansCBI: Interface functions for clustering methods
- kmeansCBI: Interface functions for clustering methods
- kmeansruns: k-means with estimating k and initialisations
- kmeansruns: k-means with estimating k and initialisations
- lcmixed: flexmix method for mixed Gaussian/multinomial mixtures
- lcmixed: flexmix method for mixed Gaussian/multinomial mixtures
- localshape: Local shape matrix
- localshape: Local shape matrix
- mahalanodisc: Mahalanobis for AWC
- mahalanodisc: Mahalanobis for AWC
- mahalanofix: Mahalanobis distances from center of indexed points
- mahalanofix: Mahalanobis distances from center of indexed points
- mahalconf: Mahalanobis fixed point clusters initial configuration
- mahalconf: Mahalanobis fixed point clusters initial configuration
- mergenormals: Clustering by merging Gaussian mixture components
- mergenormals: Clustering by merging Gaussian mixture components
- mergeparameters: New parameters from merging two Gaussian mixture components
- mergeparameters: New parameters from merging two Gaussian mixture components
- minsize: Minimum size of regression fixed point cluster
- minsize: Minimum size of regression fixed point cluster
- mixdens: Density of multivariate Gaussian mixture, mclust...
- mixdens: Density of multivariate Gaussian mixture, mclust...
- mixpredictive: Prediction strength of merged Gaussian mixture
- mixpredictive: Prediction strength of merged Gaussian mixture
- mvdcoord: Mean/variance differences discriminant coordinates
- mvdcoord: Mean/variance differences discriminant coordinates
- ncoord: Neighborhood based discriminant coordinates
- ncoord: Neighborhood based discriminant coordinates
- nselectboot: Selection of the number of clusters via bootstrap
- nselectboot: Selection of the number of clusters via bootstrap
- pamk: Partitioning around medoids with estimation of number of...
- pamk: Partitioning around medoids with estimation of number of...
- piridge: Ridgeline Pi-function
- piridge: Ridgeline Pi-function
- piridge.zeroes: Extrema of two-component Gaussian mixture
- piridge.zeroes: Extrema of two-component Gaussian mixture
- plotcluster: Discriminant projection plot.
- plotcluster: Discriminant projection plot.
- prediction.strength: Prediction strength for estimating number of clusters
- prediction.strength: Prediction strength for estimating number of clusters
- randcmatrix: Random partition matrix
- randcmatrix: Random partition matrix
- randconf: Generate a sample indicator vector
- randconf: Generate a sample indicator vector
- regmix: Mixture Model ML for Clusterwise Linear Regression
- regmix: Mixture Model ML for Clusterwise Linear Regression
- rFace: "Face-shaped" clustered benchmark datasets
- rFace: "Face-shaped" clustered benchmark datasets
- ridgeline: Ridgeline computation
- ridgeline: Ridgeline computation
- ridgeline.diagnosis: Ridgeline plots, ratios and unimodality
- ridgeline.diagnosis: Ridgeline plots, ratios and unimodality
- simmatrix: Extracting intersections between clusters from fpc-object
- simmatrix: Extracting intersections between clusters from fpc-object
- solvecov: Inversion of (possibly singular) symmetric matrices
- solvecov: Inversion of (possibly singular) symmetric matrices
- sseg: Position in a similarity vector
- sseg: Position in a similarity vector
- tdecomp: Root of singularity-corrected eigenvalue decomposition
- tdecomp: Root of singularity-corrected eigenvalue decomposition
- tonedata: Tone perception data
- tonedata: Tone perception data
- unimodal.ind: Is a fitted denisity unimodal or not?
- unimodal.ind: Is a fitted denisity unimodal or not?
- weightplots: Ordered posterior plots
- weightplots: Ordered posterior plots
- wfu: Weight function (for Mahalabobis distances)
- wfu: Weight function (for Mahalabobis distances)
- zmisclassification.matrix: Matrix of misclassification probabilities between mixture...
- zmisclassification.matrix: Matrix of misclassification probabilities between mixture...