The pdfCluster package: summary information

Description

This package performs cluster analysis via kernel density estimation (Azzalini and Torelli, 2007; Menardi and Azzalini, 2014). Clusters are associated to the maximally connected components with estimated density above a threshold. As the threshold varies, these clusters may be represented according to a hierarchical structure in the form of a tree. Detection of the connected regions is conducted by means of the Delaunay tesselation when data dimensionality is low to moderate, following Azzalini and Torelli (2007). For higher dimensional data, detection of connected regions is performed according to the procedure described in Menardi and Azzalini (2013). In both cases, after that a number of high-density cluster-cores is identified, lower density data are allocated by following a supervised classification-like approach. The number of clusters, corresponding to the number of the modes of the estimated density, is automatically selected by the procedure. Diagnostics methods for evaluating the quality of clustering are also available (Menardi, 2011). Moreover, the package provides a routine to estimate the probability density function by kernel methods, given a set of data with arbitrary dimension. The main features of the package are described and illustrated in Azzalini and Menardi (2014).

Details

The pdfCluster-package makes use of classes and methods of the S4 system. It includes some foreign functions written in the C language: two of them compute the kernel density estimate of data and are interfaced by the R function kepdf. Other C routines included in the package allow for a quicker detection of the connected components of the subgraphs associated with the level sets of the data. Two of them are directly drawn from the homonymous ones in the spdep package.

Starting from version 1.0-0, new features have been introduced:

• kernel density estimation may be performed by using either a a fixed or an adaptive bandwidth; moreover, the option of selecting a Student's t kernel has been included, for computational convenience;

• detection of connected components of the level sets is performed by means of the Delaunay triangulation when data dimensionality is up to 6, following Azzalini and Torelli (2007); for higher dimensional data a new procedure, which is less time-consuming, is now adopted (Menardi and Azzalini, 2014);

• the order of classification of lower density data depends now also on the standard error of the estimated density ratios; moreover, a cluster-specific bandwidth is the default option to classify low density data.

See examples below to understand how to set arguments of the main function of the package, in order to obtain the same results as the ones obtained with versions 0.1-x.

Author(s)

Adelchi Azzalini, Giovanna Menardi, Tiziana Rosolin

Maintainer: Giovanna Menardi <menardi at stat.unipd.it>

References

Azzalini, A., Menardi, G. (2014). Clustering via Nonparametric Density Estimation: The R Package pdfCluster. Journal of Statistical Software, 57(11), 1-26, URL http://www.jstatsoft.org/v57/i11/.

Azzalini A., Torelli N. (2007). Clustering via nonparametric density estimation. Statistics and Computing, 17, 71-80.

Menardi G. (2011). Density based Silhouette diagnostics for clustering methods. Statistics and Computing, 21, 295-308.

Menardi G., Azzalini, A. (2014). An advancement in clustering via nonparametric density estimation. Statistics and Computing, DOI: 10.1007/s11222-013-9400-x, to appear.

Examples

# load data
data(wine)
gr <- wine[, 1]

# select a subset of variables
x <- wine[, c(2, 5, 8)]

#density estimation
pdf <- kepdf(x)
summary(pdf)
plot(pdf)

#clustering
cl <- pdfCluster(x)
summary(cl)
plot(cl)

#comparison with original groups
table(groups(cl),gr)

#density based silhouette diagnostics
dsil <- dbs(cl)
plot(dsil)

##########
# higher dimensions

x <- wine[, -1]

#density estimation with adaptive bandwidth 
pdf <- kepdf(x, bwtype="adaptive")
summary(pdf)
#density plot is not much clear for high- dimensional data
#select a few variables
plot(pdf, indcol = c(1,4,7))

#clustering
#when dimension is >= 6, default method to find connected components is "pairs"
#density is better estimated by using an adaptive bandwidth
cl <- pdfCluster(x, bwtype="adaptive")
summary(cl)
plot(cl)

########
# this example shows how to set the arguments in function pdfCluster
# in order to obtain the same results as the ones of versions 0.1-x.
x <- wine[, c(2, 5, 8)]

# previous versions of the package 
# do not run
# old code: 
# cl <- pdfCluster(x)

# same result is obtained now obtained as follows:
cl <- pdfCluster(x, se=FALSE, hcores= TRUE, graphtype="delaunay", n.grid=50)

Adjusted Rand index

Description

Computes the adjusted Rand index to compare two alternative partitions of the same set.

Usage

adj.rand.index(cl1, cl2)

Arguments

Details

The adjusted Rand index is a correction of the Rand index that measures the similarity between two classifications of the same objects by the proportions of agreements between the two partitions. The correction is obtained by subtracting from the Rand index its expected value.

Value

A numeric vector of length 1.

References

L. Hubert and P. Arabie (1985) Comparing partitions, Journal of Classification, 2, 193-218.

See Also

table

Examples

# load data
data(wine)
#actual groups
gr <- wine[, 1]

# select a subset of variables
x <- wine[, c(2, 5, 8)]

#clustering
cl <- pdfCluster(x)

#comparison with original groups
table(groups(cl), gr)
adj.rand.index(groups(cl), gr)

Density-based silhouette information methods

Description

Computes the density-based silhouette information of clustered data. Two methods are associated to this function. The first method applies to two arguments: the matrix of data and the vector of cluster labels; the second method applies to objects of pdfCluster-class.

Usage

## S4 method for signature 'matrix'
dbs(x, clusters, h.funct="h.norm", hmult=1, prior, ...)

## S4 method for signature 'pdfCluster'
dbs(x, h.funct="h.norm", hmult = 1, prior = 
   as.vector(table(x@cluster.cores)/sum(table(x@cluster.cores))), 
   stage=NULL, ...)

Arguments

Details

This function provides diagnostics for a clustering produced by any density-based clustering method. The dbs information is a suitable modification of the silhouette information aimed at evaluating the cluster quality in a density based framework. It is based on the estimation of data posterior probabilities of belonging to the clusters. It may be used to measure the quality of data allocation to the clusters. High values of the \hat{dbs} are evidence of a good quality clustering.

Define

\hat{\tau}_m(x_i)=\frac{\pi_{m} \hat{f}(x_i|x_ \in m)}{\sum_{m=1}^M \pi_{m}\hat{f}(x_i|x_i \in m)} \quad m=1,\ldots,M,

where \pi_{m} is a prior probability of m and \hat{f}(x_i|x_i \in m) is a density estimate at x_i evaluated with function kepdf by using the only data points in m. Density estimation is performed with fixed bandwidths h, as evaluated by function h.funct, possibly multiplied by the shrink factor hmult.

Density-based silhouette information of x_i, the i^{th} row of the data matrix x, is defined as follows:

\hat{dbs}_i=\frac{\log\left(\frac{\hat{\tau}_{m_{0}}(x_i)}{\hat{\tau}_{m_{1}}(x_i)}\right)}{{\max}_{x_i }\left| \log\left(\frac{\hat{\tau}_{m_{0}}(x_i)}{\hat{\tau}_{m_{1}}(x_i)}\right)\right|},

where m_0 is the group where x_i has been allocated and m_1 is the group for which \tau_m is maximum, m\neq m_0.

Note: when there exists x_j such that \hat{\tau}_{m_{1}}(x_j) is zero, \hat{dbs}_j is forced to 1 and {\max}_{x_i }\left| \log\left(\frac{\hat{\tau}_{m_{0}}(x_i)}{\hat{\tau}_{m_{1}}(x_i)}\right)\right| is computed by excluding x_j from the data matrix x.

See Menardi (2011) for a detailed treatment.

Value

An object of class "dbs", with slots:

See dbs-class for more details.

Methods

signature(x = "matrix", clusters = "numeric")

Computes the density based silhouette information for objects partitioned according to any density-based clustering method.

signature(x = "pdfCluster", clusters = "missing")

Computes the density based silhouette information for objects of class "pdfCluster"

.

References

Menardi, G. (2011) Density-based Silhouette diagnostics for clustering methods. Statistics and Computing, 21, 295-308.

See Also

dbs-class, plot,dbs-method, silhouette.

Examples

#example 1: no groups in data
#random generation of group labels
set.seed(54321)
x <- rnorm(50)
groups <- sample(1:2, 50, replace = TRUE)
groups
dsil <- dbs(x = as.matrix(x), clusters=groups)
dsil
summary(dsil)
plot(dsil, labels=TRUE, lwd=6)

#example 2: wines data
# load data
data(wine)

# select a subset of variables
x <- wine[, c(2,5,8)]

#clustering
cl <- pdfCluster(x)
 
dsil <- dbs(cl)
plot(dsil)

Class "dbs"

Description

This class pertains to results of the application of function dbs.

Objects from the Class

Objects can be created by calls of the form new("dbs", ...) or as a result from calling function dbs.

Slots

call:

Object of class "call" reporting the matched call.

x:

Object of class "matrix" representing the clustered data points.

prior:

Object of class "numeric" being the prior probabilities of belonging to the groups.

dbs:

Object of class "numeric" reporting the density-based silhouette information of the clustered data.

clusters:

Object of class "numeric" reporting the group labels of grouped data.

noc:

Object of class "numeric" indicating the number of clusters.

stage:

Object of class "ANY" corresponding to the stage of the classification at which the density-based silhouette information is computed when dbs is applied to an object of pdfCluster-class.

Methods

plot

signature(x = "dbs", y = "missing"):

S4 method for plotting objects of dbs-class. Data are partitioned into the clusters, sorted in a decreasing order with respect to their dbs value and displayed on a bar graph. See plot,dbs-method for further details.

show

signature(object = "dbs"):

S4 method for showing objects of dbs-class. The following elements are shown:

• the dbs index computed at the observed data;

• The cluster membership of each data point;

summary

signature(object = "dbs"):

S4 method for summarizing objects of dbs-class. The following elements are shown:

• a summary (minimum, 1st quartile, median, mean, 3rd quartile, maximum) of the dbs values for each cluster;

• a summary (minimum, 1st quartile, median, mean, 3rd quartile, maximum) of the dbs values for all the observations.

See Also

dbs, silhouette, plot,dbs-method, plot-methods, show-methods, summary-methods.

Examples

showClass("dbs")

#wine example
#data loading
data(wine)

# select a subset of variables
x <- wine[, c(2,5,8)]

#clustering
cl <- pdfCluster(x)
 
dsil <- dbs(cl)
dsil
summary(dsil)

Extracts groups

Description

Extracts the detected groups from objects of pdfCluster-class.

Usage

groups(obj, stage = length(obj@stages))

Arguments

Details

This function is an user-friendly version of command obj@clusters, now obsolete, to ease extraction of groups from objects of pdfCluster-class.

Value

A numeric vector containing the group labels. NA values are associated to points not classified at the selected stage of the classification procedure.

See Also

pdfCluster

Examples

# load data
data(wine)

# select a subset of variables
x <- wine[, c(2, 5, 8)]

#clustering
cl <- pdfCluster(x)

groups(cl)

#equivalent to:
cl@clusters

#to extract the cluster cores
groups(cl, stage = 0)

Normal optimal choice of smoothing parameter in density estimation

Description

This function computes the smoothing parameter to be used in kernel density estimation, as asymptotically optimal when the underlying distribution is Normal. Unidimensional as well as multidimensional data can be handled. When multidimensional data are supplied, a vector of smoothing parameters is computed having one element for each component.

Usage

h.norm(x)

Arguments

Details

The smoothing parameter of component j of a n\times d data matrix is estimated as follows:

\sigma_j{\left(\frac{4}{(d+2)n }\right)}^{\frac{1}{d+4}}

where \sigma_j is the estimated standard deviation of component j. See Section 2.4.2 of the reference below.

Value

Returns a numeric vector with the same length as the number of columns of x or with length one if x is a vector. When x is a matrix, a vector of smoothing parameters is provided having one element for each component.

References

Bowman, A.W. and Azzalini, A. (1997). Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Oxford University Press, Oxford.

See Also

hnorm

Examples

set.seed(123)
x <- rnorm(30)
sm.par <- h.norm(x)
pdf <- kepdf(x, bwtype= "fixed", h = sm.par)
plot(pdf, eval.points=seq(-4,4,by=.2))

Sample smoothing parameters in adaptive density estimation

Description

This function computes the sample smoothing parameters to be used in adaptive kernel density estimation, according to Silverman (1986).

Usage

hprop2f(x, h = h.norm(x), alpha = 1/2, kernel = "gaussian")

Arguments

Details

A vector of smoothing parameters h_{i} is chosen for each sample point x_i, as follows:

h_i = h \left(\frac{\hat{f}_h(x_i)}{g}\right)^{- \alpha }

where \hat{f}_h is a pilot kernel density estimate of the density function f, with vector of bandwidths h, and g is the geometric mean of \hat{f}_h(x_i), i=1, ..., n. See Section 5.3.1 of the reference below.

Value

Returns a matrix with the same dimensions of x where row i provides the vector of smoothing parameters for sample point x_i.

References

Silverman, B. (1986). Density estimation for statistics and data analysis. Chapman and Hall, London.

See Also

h.norm

Examples

set.seed(123)
x <- rnorm(10)

sm.par <- hprop2f(x)
pdf <- kepdf(x, bwtype= "adaptive")

pdf@par$hx
sm.par

plot(pdf,eval.points=seq(-4,4,by=.2))

Kernel estimate of a probability density function.

Description

Estimates density of uni- and multivariate data by the kernel method.

Usage

kepdf(x, eval.points = x, kernel = "gaussian", 
      bwtype = "fixed", h = h.norm(x), hx = NULL, alpha = 1/2)

Arguments

Details

The current version of pdfCluster-package allows for computing estimates by a kernel product estimator of the form:

\hat{f}(y)= \sum_{i=1}^n \frac{1}{n h_{i,1} \cdots h_{i,d}} \prod_{j=1}^d K\left(\frac{y_{j} - x_{i,j}}{h_{i,j}}\right).

The kernel function K can either be a Gaussian density (if kernel = "gaussian") or a t_\nu density, with \nu = 7 degrees of freedom (when kernel = "t7"). Although uncommon, the option of selecting a t kernel is motivated by computational efficiency reasons. Hence, its use is suggested when either x or eval.points have a huge number of rows.

The vectors of bandwidths h_{i} = (h_{i,1} \cdots h_{i,d})' are defined as follows:

Fixed bandwidth

When bwtype='fixed', h_{i} = h that is, a constant smoothing vector is used for all the observations x_i. Default values are set as asymptotically optimal for a multivariate Normal distribution (e.g., Bowman and Azzalini, 1997). See h.norm for further details.

Adaptive bandwidth

When bwtype='adaptive', a vector of bandwidths h_i is specified for each observation x_i. Default values are selected according to Silverman (1986, Section 5.3.1). See hprop2f.

Value

An S4 object of kepdf-class with slots:

References

Bowman, A.W. and Azzalini, A. (1997). Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Oxford University Press, Oxford.

Silverman, B. (1986). Density estimation for statistics and data analysis. Chapman and Hall, London.

See Also

h.norm, hprop2f, kepdf-class.

Examples

## A 1-dimensional example
data(wine)
x <- wine[,3] 
pdf <- kepdf(x, eval.points=seq(0,7,by=.1))
plot(pdf, n.grid= 100, main="wine data")

## A 2-dimensional example
x <- wine[,c(2,8)] 
pdf <- kepdf(x)
plot(pdf, main="wine data", props=c(5,50,90), ylim=c(0,4))
plot(pdf, main="wine data", method="perspective", phi=30, theta=60)

### A 3-dimensional example
x <- wine[,c(2,3,8)] 
pdf <- kepdf(x)
plot(pdf, main="wine data", props=c(10,50,70), gap=0.2)
plot(pdf, main="wine data", method="perspective", gap=0.2, phi=30, theta=10)

### A 6-dimensional example
### adaptive kernel density estimate is preferable in high-dimensions
x <- wine[,c(2,3,5,7,8,10)]
pdf <- kepdf(x, bwtype="adaptive")
plot(pdf, main="wine data", props=c(10,50,70), gap=0.2)
plot(pdf, main="wine data", method="perspective", gap=0.2, phi=30, theta=10)

Class "kepdf"

Description

This class encapsulates results of the application of function kepdf.

Objects from the Class

Objects can be created by calls of the form new("kepdf", ...) or as a result of a call to kepdf.

Slots

call:

Object of class "call", corresponding to the matched call.

x:

Object of class "matrix" representing the data points used to estimate the probability density function.

eval.points:

Object of class "matrix" representing the data points at which the density is evaluated.

estimate

The values of the density estimate at the evaluation points.

kernel:

Object of class "character" giving the selected kernel.

bwtype:

Object of class "character" giving the selected type of estimator.

par:

Object of class "list" providing the parameters used to estimate the density. Its elements are h, hx, and possibly alpha.

See kepdf for further details.

Methods

plot

signature(x = "kepdf", y = "ANY")

Plots objects of kepdf-class. plot-methods are available for density estimates of:

• one-dimensional data;

• two-dimensional data: contour, image or perspective plots are available;

• multi-dimensional data: matrix of plots of all the pairs of two-dimensional marginal kernel density estimates.

See plot,kepdf-method for further details.

show

signature(object = "kepdf")

Prints the following elements:

• the class of the object;

• the selected kernel;

• the selected type of estimator;

• either the fixed smoothing parameters or the smoothing parameters of each observation;

• the density estimates at the evaluation points.

summary

signature(object = "kepdf")

Provides a summary of kepdf-class object by printing the highest density data point and the row or index position of a percentage top density data points, possibly given as optional argument prop.

See Also

h.norm, kepdf, plot,kepdf-method, plot-methods, show-methods, summary-methods.

Examples

#
showClass("kepdf")

#
data(wine)
#select only "Barolo"-type wines
x <- wine[1:59,3] 
pdf <- kepdf(x)
pdf
summary(pdf)
summary(pdf, props = 10*seq(1, 9, by = 1))

Olive oil data

Description

This data set represents eight chemical measurements on different specimen of olive oil produced in various regions in Italy (northern Apulia, southern Apulia, Calabria, Sicily, inland Sardinia and coast Sardinia, eastern and western Liguria, Umbria) and further classifiable into three macro-areas: Centre-North, South, Sardinia. The data set is used to evaluate the pdfCluster ability of recunstructing the macro-area membership.

Usage

data(oliveoil)

Format

This data frame contains 572 rows, each corresponding to a different specimen of olive oil, and 10 columns. The first and the second column correspond to the macro-area and the region of origin of the olive oils respectively; here, the term "region" refers to a geographical area and only partially to administrative borders. Columns 3-10 represent the following eight chemical measurements on the acid components for the oil specimens: palmitic, palmitoleic, stearic, oleic, linoleic, linolenic, arachidic, eicosenoic.

Details

Since the raw data are of compositional nature, ideally totalling 10000, some preliminary transformations of data are advisable. In particular, Azzalini and Torelli (2007) adopt an additive log-ratio transformation (ALR). If x_j denotes the j^{th} chemical measurement (j=1,\ldots,8), the ALR transformation is y_j= \log x_j/x_k, j\neq k, where k is an arbitrary but fixed variable. However, in this data set, the raw data do not always sum up exactly to 10000, because of measurement errors. Moreover, some 0's are present in the data, corresponding to measurements below the instrument sensitivity level. Therefore, it is suggested to add 1 to all raw data and normalize them by dividing each entry by the corresponding row sum \sum_j (x_j+1).

Source

Forina, M., Lanteri, S. Armanino, C., Casolino, C., Casale, M., Oliveri, P. (2008). V-PARVUS. An Extendible Package of programs for explorative data analysis, classification and regression analysis. Dip. Chimica e Tecnologie Farmaceutiche ed Alimentari, Università di Genova.

References

Azzalini A., Torelli N. (2007). Clustering via nonparametric density estimation. Statistics and Computing, 17, 71-80.

Classification of low density data

Description

Allocates low density data points in a multi-stage procedure after that cluster cores have been detected by applying pdfCluster.

Usage

pdfClassification(obj, n.stage = 5, se = TRUE, hcores = FALSE)

Arguments

Details

The basic idea of the classification stage of the procedure is as follows: for an unallocated data point x_0, compute the estimated density \hat{f}_m(x_0) based on the data already assigned to group m, m = 1, 2, \ldots, M, and assign x_0 to the group with highest log ratio \hat{f}_m(x_0)/\max_m \hat{f}_m(x_0).

In case \hat{f}_m(x_0)=0, for all m = 1, 2, \ldots, M, x_0 is considered as an outlier. The procedure gives a warning message and the outlier remains unclassified. The cluster label of x_0 will be set to zero.

The current implementation of this idea proceeds in n.stage stages, allocating a block of points at a time, updating the estimates \hat{f}_m(\cdot) based on the new members of each group and then allocating a new block of points. When se = TRUE, classification is performed by further weighting the log-ratios inversely with their approximated standard error, so that points whose density estimate has highest precision are allocated first.

Each of the \hat{f}_m(\cdot) is built by selecting either the same bandwidths h_0 as the ones used to form the cluster cores (when hcores = TRUE) or cluster-specific bandwidths, obtained as follows:

h_m^{*} = \exp [(1-a_m) \log(h_0) + a_m \log(h_m)],

where a_m is the proportion of data points in the m-th cluster core and h_m are asymptotically optimal for a normal distribution of the m-th cluster or computed according to the Silverman (1986) approach, if the kernel estimator has fixed or adaptive bandwidth, respectively.

Value

An object of pdfCluster-class with slot stages of class "list" having length equal to n.stage. See pdfCluster-class for further details.

Note

Function pdfClassification is called internally, from pdfCluster, when the argument n.stage is set to a value greater than zero. Alternatively, it may be called externally, by providing as argument an object of pdfCluster-class.

When pdfClassification is internally called from pdfCluster and one group only is detected, the slot stages is a list with n.stage elements, each of them being a vector with length equal to the number of data points and all elements equal to 1.

References

Azzalini A., Torelli N. (2007). Clustering via nonparametric density estimation. Statistics and Computing. 17, 71-80.

Silverman, B. (1986). Density estimation for statistics and data analysis. Chapman and Hall, London.

See Also

pdfCluster, pdfCluster-class

Examples

# load data
data(wine)

# select a subset of variables
x <- wine[, c(2,5,8)]

#whole procedure, included the classification phase
cl <- pdfCluster(x)
summary(cl)
table(groups(cl))

#use of bandwidths specific for the group 
cl1 <- pdfClassification(cl, hcores= TRUE)
table(groups(cl1))

Clustering via nonparametric density estimation

Description

Cluster analysis is performed by the density-based procedures described in Azzalini and Torelli (2007) and Menardi and Azzalini (2014), and summarized in Azzalini and Menardi (2014).

Usage

## S4 method for signature 'numeric'
pdfCluster(x, graphtype, hmult, Q = "QJ", lambda = 0.1, 
   grid.pairs = 10, n.grid = min(round((5 + sqrt(NROW(x))) * 4), NROW(x)), ...)

## S4 method for signature 'matrix'
pdfCluster(x, graphtype, hmult, Q = "QJ", lambda = 0.1, 
	grid.pairs = 10, n.grid = min(round((5 + sqrt(NROW(x))) * 4), NROW(x)), ...)

## S4 method for signature 'data.frame'
pdfCluster(x, graphtype, hmult, Q = "QJ", lambda = 0.1, 
	grid.pairs = 10, n.grid = min(round((5 + sqrt(NROW(x))) * 4), NROW(x)), ...)
	  
## S4 method for signature 'pdfCluster'
pdfCluster(x, graphtype, hmult, Q, lambda = 0.1, 
	grid.pairs, n.grid = min(round((5 + sqrt(NROW(x@x))) * 4), NROW(x@x)), ...) 

Arguments

Details

Clusters are associated to the connected components of the level sets of the density underlying the data. Density estimation is performed by kernel methods and the connected regions are approximated by the connected components of a graph built on data. Three alternative procedures to build the graph are adopted:

Unidimensional procedure

When data are univariate an edge is set between two observations when all the data points included in the segment between the two candidate observations belong to the same level set.

Delaunay triangulation

An edge is set between two observations when they are contiguous in the Voronoi diagram; see Azzalini and Torelli (2007).

Pairs evaluation

An edge is set between two observations when the density function, evaluated along the segment joining them, does not exhibit any valley having a relative amplitude greater than a tolerance threshold 0 \le \lambda < 1. Being a tolerance threshold, sensible values of \lambda are, in practice, included in [0, 0.3]; see Menardi and Azzalini (2013).

As the level set varies, the number of detected components gives rise to the tree of clusters, where each leave corresponds to a mode of the density function. Observations around these modes form a number of cluster cores, while the lower density observations are allocated according to a classification procedure; see also pdfClassification.

Value

An S4 object of pdfCluster-class with slots:

Methods

signature(x="data.frame")

This method applies the pdfCluster procedure to objects of class data.frame.

signature(x="matrix")

This method applies the pdfCluster procedure to objects of class matrix.

signature(x="numeric")

This method applies the pdfCluster procedure to objects of class numeric.

signature(x="pdfCluster")

This method applies to objects of pdfCluster-class when the graph has been built according to the "pairs" procedure. It allows to save time and computations if the user wants to compare results of cluster analysis for different values of the lambda parameter. See examples below.

Warning

It may happen that the variability of the estimated density is so high that not all jumps in the mode function can be detected by the selected grid scanning the density function. In that case, no output is produced and a message is displayed. As this may be associated to the occurrence of some spurious connected components, which appear and disappear within the range between two subsequent values of the grid, a natural solution is to increase the value of n.grid. Alternatively either lambda or hmult may be increased to alleviate the chance of detecting spurious connected components.

Using graphtype= 'delaunay' when the dimensionality d of data is greater than 6 is highly time-consuming unless the number of rows n is fairly small, since the number of operations to run the Delaunay triangulation grows exponentially with d. Use graphtype= "pairs", instead, whose computational complexity grows quadratically with the number of observations.

References

Azzalini, A., Menardi, G. (2014). Clustering via nonparametric density estimation: the R package pdfCluster. Journal of Statistical Software, 57(11), 1-26, URL http://www.jstatsoft.org/v57/i11/.

Azzalini A., Torelli N. (2007). Clustering via nonparametric density estimation. Statistics and Computing. 17, 71-80.

Menardi, G., Azzalini, A. (2014). An advancement in clustering via nonparametric density estimation. Statistics and Computing. DOI: 10.1007/s11222-013-9400-x.

See Also

kepdf, pdfCluster-class, pdfClassification.

Examples

##########
#example 1
###########
# not run here for time reasons
#loading data
data(oliveoil)

#preparing data
olive1 <- 1 + oliveoil[, 3:10]
margin <- apply(data.matrix(olive1),1,sum)
olive1 <- olive1/margin
alr <- (-log( olive1[, -4]/olive1[, 4]))
#select the first 5 principal components
x <- princomp(alr, cor=TRUE)$scores[, 1:5]

#clustering
# not run here for time reasons
#cl <- pdfCluster(x, h = h.norm(x), hmult=0.75)
#summary(cl)
#plot(cl)

#comparing groups with original macro-area membership
#groups <- groups(cl)
#table(oliveoil$macro.area, groups)

#cluster cores
#table(groups(cl, stage = 0))

##########
#example 2
###########
# not run here for time reasons
# loading data
#data(wine)
#x <-wine[ ,-1]
#gr <- wine[ ,1]

# when data are high-dimensional, an adaptive kernel estimator is preferable 
# building the Delaunay graph entails a too high computational effort
# use option "pairs" to build the graph 
# it is the default option for dimension >6 


# cl <- pdfCluster(x, graphtype="pairs", bwtype="adaptive")
# summary(cl)
# plot(cl)

#comparison with original groups
#table(groups(cl),gr)

# a better classification is obtained with larger value of lambda
# not necessary to run the whole procedure again
# a pdfCluster method applies on pdfCluster-class objects!

#cl1 <- pdfCluster(cl, lambda=0.25)
#table(gr, groups(cl1))

Class "pdfCluster"

Description

This class pertains to results of the application of function pdfCluster.

Objects from the Class

Objects can be created by calls of the form new("pdfCluster", ...) or as a result to a call to pdfCluster.

Slots

call:

Object of class "call" representing the matched call;

x:

Object of class "matrix" representing the clustered data points;

pdf:

Object of class "list" reporting details about the kernel density estimate at data points x.

nc:

Object of class "list" summarizing the result of the connected components search for different sections of the estimated density.

graph:

An object of class "list" defining details about the graph built to find the connected sets of high density regions.

cluster.cores:

Object of class "ANY" reporting the group labels of the data allocated to the cluster cores.

tree:

Object of class "ANY", namely class dendrogram if the procedure detects more than one group, list otherwise. It reports the cluster tree structure associated to the different connected components for different density levels.

noc:

Object of class "numeric" giving the number of clusters.

stages:

Object of class "ANY", being NULL if the cluster cores only are detected, "list" when also the lower density data are allocated. The elements of the list correspond to the group labels at the different stages of the classification procedure. NA values correspond to unlabeled data.

clusters:

Object of class "ANY" being NULL if the cluster cores only are detected, "numeric" when all the data are clustered. This slot is obsolete. Groups can be extracted by a call to function groups.

See pdfCluster for further details.

Methods

dbs

signature(x = "pdfCluster", clusters = "missing")

Computes the density based Silhouette diagnostics of clustered data. See dbs for further details.

pdfCluster

signature(x="pdfCluster")

Speeds up time for re-running the pdfCluster procedure with different values of tau when graphtype = "pairs"

plot

signature(x = "pdfCluster", y = "missing")

Plots objects of pdfCluster-class. plot-methods are available for:

• the mode function: gives the number of connected components when the proportion of data points with density above a threshold varies. Set argument which to 1 to display this plot.

• the cluster tree: plot the hierarchical structure associated to the clusters detected by different sections of the density estimate. Set argument which to 2 to display this plot.

• the data points: scatterplot of data or of all the possible couples of coordinates reporting the label group. Set argument which to 3 to display this plot.

• the density-based Silhouette information: graphical diagnostics of the clustering. See plot,dbs-method. Set argument which to 4 to display this plot. Not available when noc=1.

See plot,pdfCluster-method for further details.

show

signature(object = "pdfCluster").

Prints the following elements:

• the matched Call;

• the type of kernel estimator;

• the type of graph built;

• the groups tree (if available);

• the cluster cores;

• the cluster labels at the different stages of the classification procedure;

• the final clustering.

summary

signature(object = "pdfCluster").

Provides a summary of pdfCluster-class objects by printing the following elements:

• the matched call to pdfCluster function

• the frequency table of the cluster cores;

• the frequency table of the final grouping;

• the tree of clusters.

See Also

pdfCluster, plot,pdfCluster-method, show-methods, summary-methods

Examples

showClass("pdfCluster")

data(wine)
x <-wine[ ,-1]
gr <- wine[ ,1]

# clustering
cl <- pdfCluster(x, graphtype="pairs", bwtype="adaptive")
summary(cl)
cl
plot(cl)

Methods for function plot

Description

Methods for functions plot aimed at graphically displaying the S4 classes included in the pdfCluster-package.

Methods

signature(x = "kepdf", y = "ANY")

S4 method for plotting objects of kepdf-class. See plot,kepdf-method for further details.

signature(x = "dbs", y = "missing")

S4 method for plotting objects of kepdf-class. See plot,dbs-method for further details.

signature(x = "pdfCluster", y = "missing")

S4 method for plotting objects of pdfCluster-class. See plot,pdfCluster-method for further details.

See Also

plot,dbs-method, plot,kepdf-method, plot,pdfCluster-method

Plot objects of class dbs

Description

This function provides a graphical tool to display diagnostics of density-based cluster analysis by means of the density-based silhouette information.

Usage

## S4 method for signature 'dbs'
plot(x, y , xlab = "", ylab = "", col = NULL, lwd = 3, cex = 0.9, 
    cex.axis = 0.5, main = NULL, labels = FALSE, ...)

Arguments

Details

After computing the density-based silhouette index by applying dbs-methods, data are partitioned into the clusters, sorted in a decreasing order with respect to their dbs value and displayed on a bar graph.

Methods

signature(x = "dbs", y = "missing")

S4 method for plotting objects of dbs-class

See Also

dbs, dbs-class, silhouette.

Examples

#example 1: no groups in data
#random generation of group labels
set.seed(54321)
x <- rnorm(50)
groups <- sample(1:2, 50, replace=TRUE)
groups
dsil <- dbs(x=as.matrix(x), clusters=groups)
dsil
summary(dsil)
plot(dsil, labels=TRUE, lwd=6)

#example 2: wines data
# load data
data(wine)

gr <- wine[,1]

# select a subset of variables
x <- wine[, c(2,5,8)]

#clustering
cl <- pdfCluster(x)
 
dsil <- dbs(cl)
plot(dsil)

Plot objects of class kepdf

Description

Functions and methods for plotting objects of kepdf-class.

Usage

## S4 method for signature 'kepdf'
plot(x, y, eval.points = NULL, n.grid = NULL,
		data = NULL, add = FALSE, main = NULL, xlab = NULL, ylab = NULL, 
		zlab = NULL, col = NULL, col.data=2, type="l", props = c(75,50,25), 
		method="contour", ticktype = "detailed", indcol = NULL, 
		text.diag.panel = NULL, gap = 0.5, ...)

Arguments

Details

When density estimation is based on two or higher dimensional data, these functions make use of functions contour, image and persp. For densities estimated on higher than 2-d data, the pairwise marginal estimated densities are plotted for all possible pairs of coordinates or a chosen selection of them.

Value

A list containing the following elements:

Methods

signature(x = "kepdf", y = "missing")

S4 method for plotting objects of kepdf-class.

See Also

kepdf-class, plot, contour, image, plot-methods,persp

Examples

#1-d example
set.seed(123)
x1 <- rnorm(50)

#normal optimal bandwidth
pdf1a <- kepdf(x1)
#shrink the smoothing parameter
pdf1b <- kepdf(x1, h=0.5*h.norm(x1))

plot(pdf1a, n.grid=50, data=x1, xlab="x1", ylim=c(0, max(c(pdf1a@estimate,
   pdf1b@estimate))))
plot(pdf1b, n.grid=50, lty=2, add=TRUE)

#2-d example
set.seed(123)
x2 <- cbind(rnorm(50),rnorm(50))

pdf2 <- kepdf(x2)

plot(pdf2, n.grid=c(50,50), data=x2)
plot(pdf2, n.grid=c(50,50), method="image")
plot(pdf2, n.grid=c(50,50), method="perspective", phi=30, theta=30)

#3-d example
set.seed(123)
x3 <- cbind(rnorm(50), rnorm(50), rnorm(50))

pdf3 <- kepdf(x3)

plot(pdf3, n.grid=c(50,50,50))
plot(pdf3, n.grid=c(50,50,50), method="image", col = terrain.colors(30))
plot(pdf3, n.grid=c(50,50,50), method="perspective", phi=30, theta=30)

Plot objects of class pdfCluster

Description

Functions and methods for plotting objects of pdfCluster-class.

Usage

## S4 method for signature 'pdfCluster'
plot(x, y, which = 1:4, stage = Inf, pch = NULL, col = NULL, ...)

Arguments

Methods

signature(x = "pdfCluster", y = "missing")

S4 method for plotting objects of pdfCluster-class

References

Azzalini A., Torelli N. (2007). Clustering via nonparametric density estimation. Statistics and Computing. vol. 17, pp. 71-80.

See Also

pdfCluster-class, plot, plot-methods.

Examples

data(wine)
gr <- wine[,1]

# select a subset of variables
x <- wine[, c(2,5,8)]

#clustering
cl <- pdfCluster(x)
plot(cl, which=3, stage=2)

table(cl@clusters, gr)
#set "B" for Barolo, "G" for Grignolino, "A" for Barbera
plot(cl,  pch=c("B", "G", "A"), col=c(3,4,5))

Methods for Function show

Description

Methods for function show aimed at showing the S4 classes included in the pdfCluster-package.

Methods

signature(object = "kepdf")

S4 method for showing objects of kepdf-class.

signature(object = "dbs")

S4 method for showing objects of dbs-class.

signature(object = "pdfCluster")

S4 method for showing objects of pdfCluster-class.

See Also

dbs-class, kepdf-class, pdfCluster-class

Methods for Function summary

Description

Methods for function summary aimed at summarizing the S4 classes included in the pdfCluster-package.

Methods

signature(object = "dbs")

S4 method for summarizing objects of dbs-class.

signature(object = "kepdf")

S4 method for summarizing objects of kepdf-class.

signature(object = "pdfCluster")

S4 method for summarizing objects of pdfCluster-class.

See Also

dbs-class, kepdf-class, pdfCluster-class.

Wine data

Description

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wine: Barolo, Grignolino, Barbera. The data set is used to evaluate the pdfCluster ability of understanding the type of wine, given the chemical measurement.

Usage

data(wine)

Format

This data frame contains 178 rows, each corresponding to a different cultivar of wine produced in Piedmont (Italy), and 14 columns. The first column is the type of wine, a factor variable with the following levels: Barolo, Grignolino, Barbera. The variables measured on the three types of wines are the following: Alcohol, Malic acid, Ash, Alcalinity, Magnesium, Phenols, Flavanoids, Nonflavanoids, Proanthocyanins, Color intensity, Hue, OD280.OD315Dilution, Proline. All variables but the label class are continuous.

Details

The original data set comprises 27 variables. Here a subset of 14 variables only has been included.

Source

Forina, M., Lanteri, S. Armanino, C., Casolino, C., Casale, M., Oliveri, P. (2008). V-PARVUS. An Extendible Pachage of programs for esplorative data analysis, classification and regression analysis. Dip. Chimica e Tecnologie Farmaceutiche ed Alimentari, Università di Genova.