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1 Type Package Title PD-Clustering and Factor PD-Clustering Version 1.2 Date Package FPDclustering Author Cristina Tortora and Paul D. McNicholas August 23, 2017 Maintainer Cristina Tortora Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PDclustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets. Depends ThreeWay License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication :08:58 UTC R topics documented: asymmetric asymmetric FPDC outliers PDclust Silh TuckerFactors Index 10 1
2 2 asymmetric3 asymmetric20 Asymmetric data set shape=20 Each cluster has been generated according to a multivariate asymmetric Gaussian distribution, with shape=20, covariance matrix equal to the identity matrix and randomly generated centres. data(asymmetric20) Format A data frame with 800 observations on the following 101 variables. The first variable is the membership. Source Generated with R using the package sn (The skew-normal and skew-t distributions), function rsn data(asymmetric20) plot(asymmetric20[,2:3]) asymmetric3 Asymmetric data set shape=3 Each cluster has been generated according to a multivariate asymmetric Gaussian distribution, with shape=3, covariance matrix equal to the identity matrix and randomly generated centres. data(asymmetric3) Format A data frame with 800 observations on 101 variables. The first variable is the membership labels. Source Generated with R using the package sn (The skew-normal and skew-t distributions), function rsn
3 FPDC 3 data(asymmetric3) plot(asymmetric3[,2:3]) FPDC Factor probabilistic distance clustering An implementation of FPDC, a probabilistic factor clustering algorithm that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion FPDC(data = NULL, k = 2, nf = 2, nu = 2) Arguments data k nf nu A matrix or data frame such that rows correspond to observations and columns correspond to variables. A numerical parameter giving the number of clusters A numerical parameter giving the number of factors for variables A numerical parameter giving the number of factors for units Value A list with components label centers probability JDF iter explained A vector of integers indicating the cluster membership for each unit A matrix of cluster centers A matrix of probability of each point belonging to each cluster The value of the Joint distance function The number of iterations The explained variability Author(s) Cristina Tortora and Paul D. McNicholas
4 4 FPDC References Tortora, C., M. Gettler Summa, M. Marino, and F. Palumbo. Factor probabilistic distance clustering (fpdc): a new clustering method for high dimensional data sets. Advanced in Data Analysis and Classification, to appear, Tortora C., Gettler Summa M., and Palumbo F.. Factor pd-clustering. In Lausen et al., editor, Algorithms from and for Nature and Life, Studies in Classification, Data Analysis, and Knowledge Organization DOI / , , Tortora C., Non-hierarchical clustering methods on factorial subspaces, See Also PDclust # Asymmetric data set clustering example (with shape=3). data('asymmetric3') x<-asymmetric3[,-1] fpdas3=fpdc(x,4,3,3) table(asymmetric3[,1],fpdas3$label) Silh(fpdas3$probability) # Asymmetric data set clustering example (with shape=20). data('asymmetric20') x<-asymmetric20[,-1] fpdas20=fpdc(x,4,3,3) table(asymmetric20[,1],fpdas20$label) Silh(fpdas20$probability) # Clustering example with outliers. data('outliers') x<-outliers[,-1] fpdout=fpdc(x,4,5,4) table(outliers[,1],fpdout$label) Silh(fpdout$probability)
5 outliers 5 outliers Data set with outliers Format Source Each cluster has been generated according to a multivariate Gaussian distribution, with centers c randomly generated. For each cluster, 20% of uniform distributed outliers have been generated at a distance included in max(x-c) and max(x-c)+5 form the center. data(outliers) A data frame with 960 observations on the following 101 variables. The first variable corresponds to the membership generated with R data(outliers) plot(outliers[,2:3]) PDclust Probabilistic Distance Clustering Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PDclust(data = NULL, k = 2) Arguments data k A matrix or data frame such that rows correspond to observations and columns correspond to variables. A numerical parameter giving the number of clusters
6 6 Silh Value A list with components label centers probability JDF iter A vector of integers indicating the cluster membership for each unit A matrix of cluster centers A matrix of probability of each point belonging to each cluster The value of the Joint distance function The number of iterations Author(s) Cristina Tortora and Paul D. McNicholas References Ben-Israel A, Iyigun. Probabilistic D-Clustering. Journal of Classification, 25(1), 5 26, #Normally generated clusters c1 = c(+2,+2,2,2) c2 = c(-2,-2,-2,-2) c3 = c(-3,3,-3,3) n=200 x1 = cbind(rnorm(n, c1[1]), rnorm(n, c1[2]), rnorm(n, c1[3]), rnorm(n, c1[4]) ) x2 = cbind(rnorm(n, c2[1]), rnorm(n, c2[2]),rnorm(n, c2[3]), rnorm(n, c2[4]) ) x3 = cbind(rnorm(n, c3[1]), rnorm(n, c3[2]),rnorm(n, c3[3]), rnorm(n, c3[4]) ) x = rbind(x1,x2,x3) pdn=pdclust(x,3) plot(x[,1:2],col=pdn$label) plot(x[,3:4],col=pdn$label) Silh Probabilistic silhouette plot Graphical tool to see how well each point belongs to the cluster. Silh(p) Arguments p A matrix of probabilities such that rows correspond to observations and columns correspond to clusters.
7 Silh 7 Details The probabilistic silhouettes are an adaptation of the ones proposed by Menardi(2011) according to the following formula:, Value dbs i = (log(p imk /p im1 ))/max i log(p imk /p im1 ) where m k is such that x i belongs to cluster k and m 1 is such that p im1 is maximum for m different fromm k. Probabilistic silhouette plot Author(s) Cristina Tortora References Menardi G. Density-based Silhouette diagnostics for clustering methods.statistics and Computing, 21, , # Asymmetric data set silhouette example (with shape=3). data('asymmetric3') x<-asymmetric3[,-1] fpdas3=fpdc(x,4,3,3) Silh(fpdas3$probability) # Asymmetric data set shiluette example (with shape=20). data('asymmetric20') x<-asymmetric20[,-1] fpdas20=fpdc(x,4,3,3) Silh(fpdas20$probability) # Shiluette example with outliers. data('outliers') x<-outliers[,-1] fpdout=fpdc(x,4,4,3) Silh(fpdout$probability)
8 8 TuckerFactors TuckerFactors Choice of the number of Tucker 3 factors An empirical way of choosing the number of factors. The algorithm returns a graph and a table representing the explained variability varying the number of factors. TuckerFactors(data = NULL, nc = 2) Arguments data nc A matrix or data frame such that rows correspond to observations and columns correspond to variables. A numerical parameter giving the number of clusters Value A table containing the explained variability varying the number of factors for units (column) and for variables (row) and a plot Author(s) Cristina Tortora References Kiers H, Kinderen A. A fast method for choosing the numbers of components in Tucker3 analysis.british Journal of Mathematical and Statistical Psychology, 56(1), , Kroonenberg P. Applied Multiway Data Analysis. Ebooks Corporation, Hoboken, New Jersey, Tortora C., Gettler Summa M., and Palumbo F.. Factor pd-clustering. In Lausen et al., editor, Algorithms from and for Nature and Life, Studies in Classification, Data Analysis, and Knowledge Organization DOI / , , See Also T3 # Asymmetric data set example (with shape=3). data('asymmetric3') xp=tuckerfactors(asymmetric3[,-1], nc = 4)
9 TuckerFactors 9 # Asymmetric data set example (with shape=20). data('asymmetric20') xp=tuckerfactors(asymmetric20[,-1], nc = 4)
10 Index asymmetric20, 2 asymmetric3, 2 FPDC, 3 outliers, 5 PDclust, 4, 5 Silh, 6 T3, 8 TuckerFactors, 8 10
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Package RAMP. May 25, 2017
Type Package Package RAMP May 25, 2017 Title Regularized Generalized Linear Models with Interaction Effects Version 2.0.1 Date 2017-05-24 Author Yang Feng, Ning Hao and Hao Helen Zhang Maintainer Yang
Package munfold. R topics documented: February 8, Type Package. Title Metric Unfolding. Version Date Author Martin Elff
Package munfold February 8, 2016 Type Package Title Metric Unfolding Version 0.3.5 Date 2016-02-08 Author Martin Elff Maintainer Martin Elff Description Multidimensional unfolding using
Package bdots. March 12, 2018
Type Package Title Bootstrapped Differences of Time Series Version 0.1.19 Date 2018-03-05 Package bdots March 12, 2018 Author Michael Seedorff, Jacob Oleson, Grant Brown, Joseph Cavanaugh, and Bob McMurray
Package ecp. December 15, 2017
Type Package Package ecp December 15, 2017 Title Non-Parametric Multiple Change-Point Analysis of Multivariate Data Version 3.1.0 Date 2017-12-01 Author Nicholas A. James, Wenyu Zhang and David S. Matteson
Package gppm. July 5, 2018
Version 0.2.0 Title Gaussian Process Panel Modeling Package gppm July 5, 2018 Provides an implementation of Gaussian process panel modeling (GPPM). GPPM is described in Karch (2016; )