Type: Package
Title: Variable Selection for Clustering and Classification
Version: 0.7
Date: 2023-10-17
Author: Jeffrey L. Andrews [aut], Mackenzie R. Neal [aut], Paul D. McNicholas ORCID iD [aut, cre]
Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>
Description: Performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods ('teigen' and 'mclust' are currently supported). Can account for mixtures of non-Gaussian distributions via Manly transform (via 'ManlyMix'). See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023) <doi:10.48550/arXiv.2305.16464>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: teigen, mclust, MixGHD
Depends: ManlyMix
NeedsCompilation: no
Repository: CRAN
Packaged: 2023-10-17 17:17:35 UTC; paul
Date/Publication: 2023-10-17 22:20:02 UTC

Variable Selection for Clustering and Classification

Description

Performs variable selection under a clustering or classification framework. Automated implementation using model-based clustering is based on teigen and mclust.

Details

Package: vscc
Type: Package
Version: 0.7
Date: 2023-10-17
License: GPL>="2"

Author(s)

Jeffrey L. Andrews, Mackenzie Neal, Paul D. McNicholas

Maintainer: Paul D. McNicholas <mcnicholas@math.mcmaster.ca>

References

See citation("vscc").

See Also

vscc

Plotting for VSCC Objects

Description

Dedicated plot function for objects of class vscc.

Usage

## S3 method for class 'vscc'
plot(x, ...)

Arguments

x

An object of class vscc.

...

Further arguments to be passed on

Details

Provides a scatterplot matrix of the selected variables with colours corresponding to each group.

Value

No return value.

Author(s)

Jeffrey L. Andrews

See Also

vscc

Examples

require("mclust")
data(banknote)
X<-banknote[,-1]
bankrun <- vscc(X)
plot(bankrun)

Printing for VSCC

Description

Dedicated print function for objects of class vscc.

Usage

## S3 method for class 'vscc'
print(x, ...)

Arguments

x

An object of class vscc

...

Further arguments to be passed on

Details

Same as summary.

Value

No return value.

Author(s)

Jeffrey L. Andrews

See Also

summary.vscc, vscc

Examples

require("mclust")
data(banknote)
X<-banknote[,-1]
vscc(X)

Summary for VSCC Objects

Description

Dedicated summary function for objects of class vscc

Usage

## S3 method for class 'vscc'
summary(object, ...)

Arguments

object

An object of class vscc

...

Additional arguments to be passed

Value

No return value.

Author(s)

Jeffrey L. Andrews

See Also

vscc

Examples

require("mclust")
data(banknote)
summary(vscc(banknote[,-1]))

Variable Selection for Clustering and Classification

Description

Performs variable selection under a clustering or classification framework. Automated implementation using model-based clustering is based on teigen version 2.0 and mclust version 4.0; issues *may* arise when using different versions.

Usage

vscc(x, G=1:9, automate = "mclust", initial = NULL, initunc=NULL, train = NULL,
    forcereduction = FALSE)

Arguments

x

Data frame or matrix to perform variable selection on

G

Vector for the number of groups to consider during initialization and/or post-selection analysis. Default is 1-9.

automate

Character string ("teigen", "mclust" (default), or NULL only) indicating which mixture model family to implement as initialization and/or post-selection analysis. If NULL, the function assumes manual operation of the algorithm (meaning an initial clustering vector must be given, and no post-selection analysis is performed).

initial

Optional vector giving the initial clustering.

initunc

Optional scalar indicating the total uncertainty of the initial clustering solution. Only used when initial is non-null.

train

Optional vector of training data (for classification framework).

forcereduction

Logical indicating if the full data set should be considered (FALSE) when selecting the ‘best’ variable subset via total model uncertainty. Not used if automate=NULL.

Value

selected

A list containing the subsets of variables selected for each relation. Each set is numbered according to the number in the exponential of the relationship. For instance, vscc_object$selected[[3]] corresponds to the variable subset selected by the cubic relationship.

family

The family used as initialization and/or post selection. (Same as user input automate, and can be NULL).

wss

The within-group variance associated with each variable from the full data set.

The remaining values are provided as long as automate is not NULL:

topselected

The best variable subset according to the total model uncertainty.

initialrun

Results from the initialization; an object of class teigen or mclust.

bestmodel

Results from the best model on the selected variable subset; an object of class teigen or mclust.

chosenrelation

Numeric indication of the relationship chosen according to total model uncertainty. The number corresponds to exponent in the relationship: for instance, a value of '4' suggests the quartic relationship. If the value "Full dataset" is given, then the unreduced data provides the best model uncertainty; can be avoided by specifying forcereduction=TRUE in the function call.

uncertainty

Total model uncertainty associated with the best relationship.

allmodelfit

List containing the results (teigen or mclust objects) from the post-selection analysis on each variable subset. Number corresponds to the exponent in the relationship. For instance, vscc_object$allmodelfit[[1]] gives the results from the analysis on the variables selected by the linear relationship.

Author(s)

Jeffrey L. Andrews, Paul D. McNicholas

References

See citation("vscc") for the variable selection references. See also citation("teigen") and citation("mclust") if using those families of models via the automate call.

See Also

teigen, Mclust

Examples

require("mclust")
data(banknote)
head(banknote)
bankrun <- vscc(banknote[,-1])
head(bankrun$topselected) #Show preview of selected variables
table(banknote[,1], bankrun$initialrun$classification) #Clustering results on full data set
table(banknote[,1], bankrun$bestmodel$classification) #Clustering results on reduced data set

Variable Selection for Skewed Clustering and Classification

Description

Performs variable selection under a clustering framework. Accounts for mixtures of non-Gaussian distributions via the ManlyTransform (via 'ManlyMix').

Usage

vsccmanly(x, G=2:9, numstart=100, selection="backward",forcereduction=FALSE,
                     initstart="k-means", seedval=2354)

Arguments

x

Data frame or matrix to perform variable selection on

G

Vector for the number of groups to consider during initialization and/or post-selection analysis. Default is 2-9.

numstart

Number of random starts.

selection

Forward or backward transformation parameter selection. User may also choose to fit a full Manly mixture (options are 'forward', 'backward', or 'none').

forcereduction

Logical indicating if the full data set should be considered (FALSE) when selecting the ‘best’ variable subset via total model uncertainty.

initstart

Method for initial starting values (options are 'k-means' or 'hierarchical').

seedval

Value of seed, used for k-means initialization.

Value

selected

A list containing the subsets of variables selected for each relation. Each set is numbered according to the number in the exponential of the relationship. For instance, vscc_object$selected[[3]] corresponds to the variable subset selected by the cubic relationship.

wss

The within-group variance associated with each variable from the full data set.

topselected

The best variable subset according to the total model uncertainty.

initialrun

Results from the initial model, prior to variable selection; an object of class ManlyMix.

bestmodel

Results from the best model on the selected variable subset; an object of class ManlyMix.

variables

Variables used to fit the final model.

chosenrelation

Numeric indication of the relationship chosen according to total model uncertainty. The number corresponds to exponent in the relationship: for instance, a value of '4' suggests the quartic relationship. If the value "Full dataset" is given, then the unreduced data provides the best model uncertainty; can be avoided by specifying forcereduction=TRUE in the function call.

uncertainty

Total model uncertainty associated with the best relationship.

allmodelfit

List containing the results (ManlyMix objects) from the post-selection analysis on each variable subset. Number corresponds to the exponent in the relationship. For instance, vscc_object$allmodelfit[[1]] gives the results from the analysis on the variables selected by the linear relationship.

Author(s)

Jeffrey L. Andrews, Mackenzie R. Neal, Paul D. McNicholas

References

See citation("vscc") for the variable selection references.

See Also

vscc

Examples

## Not run: 
data(ais)
X=ais[,3:13]
aisfor=vsccmanly(as.data.frame(scale(X)),G=2:9,selection = "forward", forcereduction = TRUE,
                        initstart = "k-means",seedval=2354) 
aisfor$variables #Show selected variables
table(ais[,1], aisfor$bestmodel$id) #Clustering results on reduced data set

## End(Not run)