Package longclust. March 18, 2018
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1 Package longclust March 18, 2018 Type Package Title Model-Based Clustering and Classification for Longitudinal Data Version Date Author Paul D. McNicholas [aut, cre], K. Raju Jampani [aut] (May to Dec 2012), Sanjeena Subedi [aut] Maintainer Paul D. McNicholas Suggests mvtnorm Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. License GPL (>= 2) LazyLoad yes Repository CRAN NeedsCompilation yes Date/Publication :30:18 UTC R topics documented: longclust-package longclustem plot.longclust print.longclust summary.longclust Index 8 1
2 2 longclustem longclust-package Model-Based Clustering and Classification for Longitudinal Data Details This is a package for clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Package: longclust Type: Package Version: Date: License: GPL-2 or GPL-3 LazyLoad: yes This package contains the function longclustem. P. D. McNicholas, K.R. Jampani and S. Subedi Maintainer: Paul McNicholas <mcnicholas@math.mcmaster.ca> See Also Details, examples, and references are given under longclustem. longclustem Model-Based Clustering and Classification for Longitudinal Data Carries out model-based clustering or classification using multivariate t or Gaussian mixture models with Cholesky decomposed covariance structure. EM algorithms are used for parameter estimation and the BIC is used for model selection. Usage longclustem(x, Gmin, Gmax, class=null, linearmeans = FALSE, modelsubset = NULL, initwithkmeans = FALSE, criteria = "BIC", equaldf = FALSE, gaussian=false, userseed=1004)
3 longclustem 3 Arguments x A matrix or data frame such that rows correspond to observations and columns correspond to variables. Gmin A number giving the minimum number of components to be used. Gmax A number giving the maximum number of components to be used. class If NULL then model-based clustering is performed. If a vector with length equal to the number of observations, then model-based classification is performed. In this latter case, the ith entry of class is either zero, indicating that the component membership of observation i is unknown, or it corresponds to the component membership of observation i. linearmeans If TRUE, then means are modelled using linear models. modelsubset A vector of strings giving the models to be used. If set to NULL, all models are used. initwithkmeans If TRUE, the components are initialized using k-means algorithm. criteria A string that denotes the criteria used for evaluating the models. Its value should be "BIC" or "ICL". equaldf If TRUE, the degrees of freedom of all the components will be the same. gaussian If TRUE, a mixture of Gaussian distributions is used in place of a mixture of t-distributions. userseed The random number seed to be used. Value Gbest zbest nubest mubest Tbest Dbest The number of components for the best model. A matrix that gives the probabilities for any data element to belong to any component in the best model. A vector of Gbest integers, that give the degrees of freedom for each component in the best model. A matrix containing the means of the components for the best model (one per row). A list of Gbest matrices, giving the T matrices of the components for the best model. A list of Gbest matrices, giving the D matrices of the components for the best model. Paul D. McNicholas, K. Raju Jampani and Sanjeena Subedi References Paul D. McNicholas and T. Brendan Murphy (2010). Model-based clustering of longitudinal data. The Canadian Journal of Statistics 38(1), Paul D. McNicholas and Sanjeena Subedi (2012). Clustering gene expression time course data using mixtures of multivariate t-distributions. Journal of Statistical Planning and Inference 142(5),
4 4 plot.longclust Examples library(mvtnorm) m1 <- c(23,34,39,45,51,56) S1 <- matrix(c(1.00, -0.90, 0.18, -0.13, 0.10, -0.05, -0.90, 1.31, -0.26, 0.18, -0.15, 0.07, 0.18, -0.26, 4.05, -2.84, 2.27, -1.13, -0.13, 0.18, -2.84, 2.29, -1.83, 0.91, 0.10, -0.15, 2.27, -1.83, 3.46, -1.73, -0.05, 0.07, -1.13, 0.91, -1.73, 1.57), 6, 6) m2 <- c(16,18,15,17,21,17) S2 <- matrix(c(1.00, 0.00, -0.50, -0.20, -0.20, 0.19, 0.00, 2.00, 0.00, -1.20, -0.80, -0.36,-0.50, 0.00, 1.25, 0.10, -0.10, -0.39, -0.20, -1.20, 0.10, 2.76, 0.52, -1.22,-0.20, -0.80, -0.10, 0.52, 1.40, 0.17, 0.19, -0.36, -0.39, -1.22, 0.17, 3.17), 6, 6) m3 <- c(8, 11, 16, 22, 25, 28) S3 <- matrix(c(1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, -0.20, -0.64, 0.26, 0.00, 0.00, -0.20, 1.04, -0.17, -0.10, 0.00, 0.00, -0.64, -0.17, 1.50, -0.65, 0.00, 0.00, 0.26, -0.10, -0.65, 1.32, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00), 6, 6) m4 <- c(12, 9, 8, 5, 4,2) S4 <- diag(c(1,1,1,1,1,1)) data <- matrix(0, 40, 6) data[1:10,] <- rmvnorm(10, m1, S1) data[11:20,] <- rmvnorm(10, m2, S2) data[21:30,] <- rmvnorm(10, m3, S3) data[31:40,] <- rmvnorm(10, m4, S4) clus <- longclustem(data, 3, 5, linearmeans=true) summary(clus) plot(clus,data) plot.longclust Plots the components of the model. Usage Displays a series of two plots, one containing all the components in different colors, and one containing subplots one per each component. ## S3 method for class 'longclust' plot(x, data,...) Arguments x data... Default arguments. An object of type longclust returned by longclustem. The data matrix used in computing clus.
5 print.longclust 5 Paul D. McNicholas, K. Raju Jampani and Sanjeena Subedi Examples library(mvtnorm) m1 <- c(23,34,39,45,51,56) S1 <- matrix(c(1.00, -0.90, 0.18, -0.13, 0.10, -0.05, -0.90, 1.31, -0.26, 0.18, -0.15, 0.07, 0.18, -0.26, 4.05, -2.84, 2.27, -1.13, -0.13, 0.18, -2.84, 2.29, -1.83, 0.91, 0.10, -0.15, 2.27, -1.83, 3.46, -1.73, -0.05, 0.07, -1.13, 0.91, -1.73, 1.57), 6, 6) m2 <- c(16,18,15,17,21,17) S2 <- matrix(c(1.00, 0.00, -0.50, -0.20, -0.20, 0.19, 0.00, 2.00, 0.00, -1.20, -0.80, -0.36,-0.50, 0.00, 1.25, 0.10, -0.10, -0.39, -0.20, -1.20, 0.10, 2.76, 0.52, -1.22,-0.20, -0.80, -0.10, 0.52, 1.40, 0.17, 0.19, -0.36, -0.39, -1.22, 0.17, 3.17), 6, 6) m3 <- c(8, 11, 16, 22, 25, 28) S3 <- matrix(c(1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, -0.20, -0.64, 0.26, 0.00, 0.00, -0.20, 1.04, -0.17, -0.10, 0.00, 0.00, -0.64, -0.17, 1.50, -0.65, 0.00, 0.00, 0.26, -0.10, -0.65, 1.32, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00), 6, 6) m4 <- c(12, 9, 8, 5, 4,2) S4 <- diag(c(1,1,1,1,1,1)) data <- matrix(0, 40, 6) data[1:10,] <- rmvnorm(10, m1, S1) data[11:20,] <- rmvnorm(10, m2, S2) data[21:30,] <- rmvnorm(10, m3, S3) data[31:40,] <- rmvnorm(10, m4, S4) clus <- longclustem(data, 3, 5, linearmeans=true) plot(clus,data) print.longclust Brief overview of the longclust object Prints the number of components, probabily matrix, degrees of freedom and the component means of the computed best model. Usage ## S3 method for class 'longclust' print(x,...) Arguments x An object of type longclust, computed by longclustem.... Default Arguments
6 6 summary.longclust Paul D. McNicholas, K. Raju Jampani and Sanjeena Subedi Examples library(mvtnorm) m1 <- c(23,34,39,45,51,56) S1 <- matrix(c(1.00, -0.90, 0.18, -0.13, 0.10, -0.05, -0.90, 1.31, -0.26, 0.18, -0.15, 0.07, 0.18, -0.26, 4.05, -2.84, 2.27, -1.13, -0.13, 0.18, -2.84, 2.29, -1.83, 0.91, 0.10, -0.15, 2.27, -1.83, 3.46, -1.73, -0.05, 0.07, -1.13, 0.91, -1.73, 1.57), 6, 6) m2 <- c(16,18,15,17,21,17) S2 <- matrix(c(1.00, 0.00, -0.50, -0.20, -0.20, 0.19, 0.00, 2.00, 0.00, -1.20, -0.80, -0.36,-0.50, 0.00, 1.25, 0.10, -0.10, -0.39, -0.20, -1.20, 0.10, 2.76, 0.52, -1.22,-0.20, -0.80, -0.10, 0.52, 1.40, 0.17, 0.19, -0.36, -0.39, -1.22, 0.17, 3.17), 6, 6) m3 <- c(8, 11, 16, 22, 25, 28) S3 <- matrix(c(1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, -0.20, -0.64, 0.26, 0.00, 0.00, -0.20, 1.04, -0.17, -0.10, 0.00, 0.00, -0.64, -0.17, 1.50, -0.65, 0.00, 0.00, 0.26, -0.10, -0.65, 1.32, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00), 6, 6) m4 <- c(12, 9, 8, 5, 4,2) S4 <- diag(c(1,1,1,1,1,1)) data <- matrix(0, 40, 6) data[1:10,] <- rmvnorm(10, m1, S1) data[11:20,] <- rmvnorm(10, m2, S2) data[21:30,] <- rmvnorm(10, m3, S3) data[31:40,] <- rmvnorm(10, m4, S4) clus <- longclustem(data, 3, 5, linearmeans=true) print(clus) ## The function is currently defined as function (tch,...) { cat("number of Clusters:", tch$gbest, "\n") cat("z:\n") print(tch$zbest) cat("\n") for (g in 1:tch$Gbest) { cat("cluster: ", g, "\n") cat("v: ", tch$nubest[g], "\n") cat("mean:", tch$mubest[g, ], "\n\n") } } summary.longclust Summary of the longclust object
7 summary.longclust 7 Usage Prints all the items in the object. ## S3 method for class 'longclust' summary(object,...) Arguments object... Default arguments. An object of type longclust, returned by longclustem. Paul D. McNicholas, K. R. Jampani and Sanjeena Subedi Examples library(mvtnorm) m1 <- c(23,34,39,45,51,56) S1 <- matrix(c(1.00, -0.90, 0.18, -0.13, 0.10, -0.05, -0.90, 1.31, -0.26, 0.18, -0.15, 0.07, 0.18, -0.26, 4.05, -2.84, 2.27, -1.13, -0.13, 0.18, -2.84, 2.29, -1.83, 0.91, 0.10, -0.15, 2.27, -1.83, 3.46, -1.73, -0.05, 0.07, -1.13, 0.91, -1.73, 1.57), 6, 6) m2 <- c(16,18,15,17,21,17) S2 <- matrix(c(1.00, 0.00, -0.50, -0.20, -0.20, 0.19, 0.00, 2.00, 0.00, -1.20, -0.80, -0.36,-0.50, 0.00, 1.25, 0.10, -0.10, -0.39, -0.20, -1.20, 0.10, 2.76, 0.52, -1.22,-0.20, -0.80, -0.10, 0.52, 1.40, 0.17, 0.19, -0.36, -0.39, -1.22, 0.17, 3.17), 6, 6) m3 <- c(8, 11, 16, 22, 25, 28) S3 <- matrix(c(1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, -0.20, -0.64, 0.26, 0.00, 0.00, -0.20, 1.04, -0.17, -0.10, 0.00, 0.00, -0.64, -0.17, 1.50, -0.65, 0.00, 0.00, 0.26, -0.10, -0.65, 1.32, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00), 6, 6) m4 <- c(12, 9, 8, 5, 4,2) S4 <- diag(c(1,1,1,1,1,1)) data <- matrix(0, 40, 6) data[1:10,] <- rmvnorm(10, m1, S1) data[11:20,] <- rmvnorm(10, m2, S2) data[21:30,] <- rmvnorm(10, m3, S3) data[31:40,] <- rmvnorm(10, m4, S4) clus <- longclustem(data, 3, 5, linearmeans=true) summary(clus)
8 Index longclust (longclust-package), 2 longclust-package, 2 longclustem, 2, 2 plot.longclust, 4 print.longclust, 5 summary.longclust, 6 8
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Type Package Title Perform and Display Google Trends Queries Version 1.4.2 Date 2018-08-03 Package gtrendsr August 4, 2018 An interface for retrieving and displaying the information returned online by
Package ETC. February 19, 2015
Type Package Title Equivalence to control Version 1.3 Date 2009-01-30 Author Suggests SimComp, multcomp, mratios Imports mvtnorm Package ETC February 19, 2015 Maintainer The
Package apastyle. March 29, 2017
Type Package Title Generate APA Tables for MS Word Version 0.5 Date 2017-03-29 Author Jort de Vreeze [aut, cre] Package apastyle March 29, 2017 Maintainer Jort de Vreeze Most
Package RPMM. August 10, 2010
Package RPMM August 10, 2010 Type Package Title Recursively Partitioned Mixture Model Version 1.06 Date 2009-11-16 Author E. Andres Houseman, Sc.D. Maintainer E. Andres Houseman
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Type Package Title Generalized Simulated Annealing Version 1.1.7 Date 2018-01-15 Package GenSA January 17, 2018 Author Sylvain Gubian, Yang Xiang, Brian Suomela, Julia Hoeng, PMP SA. Maintainer Sylvain
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Package deductive June 2, 2017 Maintainer Mark van der Loo License GPL-3 Title Data Correction and Imputation Using Deductive Methods LazyData no Type Package LazyLoad yes Attempt
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Package Rsomoclu Version 1.7.5.3 Date 2019-01-03 Title Somoclu Imports kohonen January 3, 2019 Suggests Description Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore
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Type Package Package fastdummies January 8, 2018 Title Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables Version 1.0.0 Description Creates dummy columns from columns that have
Package sigqc. June 13, 2018
Title Quality Control Metrics for Gene Signatures Version 0.1.20 Package sigqc June 13, 2018 Description Provides gene signature quality control metrics in publication ready plots. Namely, enables the
Package sspse. August 26, 2018
Type Package Version 0.6 Date 2018-08-24 Package sspse August 26, 2018 Title Estimating Hidden Population Size using Respondent Driven Sampling Data Maintainer Mark S. Handcock
Package RPMM. September 14, 2014
Package RPMM September 14, 2014 Type Package Title Recursively Partitioned Mixture Model Version 1.20 Date 2014-09-13 Author E. Andres Houseman, Sc.D. and Devin C. Koestler, Ph.D. Maintainer E. Andres
Package listdtr. November 29, 2016
Package listdtr November 29, 2016 Type Package Title List-Based Rules for Dynamic Treatment Regimes Version 1.0 Date 2016-11-20 Author Yichi Zhang Maintainer Yichi Zhang Construction
Package svalues. July 15, 2018
Type Package Package svalues July 15, 2018 Title Measures of the Sturdiness of Regression Coefficients Version 0.1.6 Author Carlos Cinelli Maintainer Carlos Cinelli Implements
Package simr. April 30, 2018
Type Package Package simr April 30, 2018 Title Power Analysis for Generalised Linear Mixed Models by Simulation Calculate power for generalised linear mixed models, using simulation. Designed to work with
Package NetCluster. R topics documented: February 19, Type Package Version 0.2 Date Title Clustering for networks
Type Package Version 0.2 Date 2010-05-09 Title Clustering for networks Package NetCluster February 19, 2015 Author Mike Nowak , Solomon Messing , Sean
Package bayesdp. July 10, 2018
Type Package Package bayesdp July 10, 2018 Title Tools for the Bayesian Discount Prior Function Version 1.3.2 Date 2018-07-10 Depends R (>= 3.2.3), ggplot2, survival, methods Functions for data augmentation
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Package mvprobit. November 2, 2015
Version 0.1-8 Date 2015-11-02 Title Multivariate Probit Models Package mvprobit November 2, 2015 Author Arne Henningsen Maintainer Arne Henningsen
Package OLScurve. August 29, 2016
Type Package Title OLS growth curve trajectories Version 0.2.0 Date 2014-02-20 Package OLScurve August 29, 2016 Maintainer Provides tools for more easily organizing and plotting individual ordinary least
Package egonet. February 19, 2015
Package egonet February 19, 2015 Type Package Title Tool for ego-centric measures in Social Network Analysis Version 1.2 Date 2012-11-01 Author A. Sciandra, F. Gioachin, L. Finos Maintainer L. Finos
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Package abe October 30, 2017 Type Package Title Augmented Backward Elimination Version 3.0.1 Date 2017-10-25 Author Rok Blagus [aut, cre], Sladana Babic [ctb] Maintainer Rok Blagus
Package CuCubes. December 9, 2016
Package CuCubes December 9, 2016 Title MultiDimensional Feature Selection (MDFS) Version 0.1.0 URL https://featureselector.uco.uwb.edu.pl/pub/cucubes/ Functions for MultiDimensional Feature Selection (MDFS):
Package ordinalclust
Type Package Package ordinalclust August 6, 2018 Title Ordinal Data Clustering, Co-Clustering and Classification Version 1.3.1 Date 2018-08-06 Author Margot Selosse, Julien Jacques, Christophe Biernacki
Package SSRA. August 22, 2016
Type Package Title Sakai Sequential Relation Analysis Version 0.1-0 Date 2016-08-22 Author Takuya Yanagida [cre, aut], Keiko Sakai [aut] Package SSRA August 22, 2016 Maintainer Takuya Yanagida
Package sfc. August 29, 2016
Type Package Title Substance Flow Computation Version 0.1.0 Package sfc August 29, 2016 Description Provides a function sfc() to compute the substance flow with the input files --- ``data'' and ``model''.
Package ClusterBootstrap
Package ClusterBootstrap June 26, 2018 Title Analyze Clustered Data with Generalized Linear Models using the Cluster Bootstrap Date 2018-06-26 Version 1.0.0 Provides functionality for the analysis of clustered
Package PTE. October 10, 2017
Type Package Title Personalized Treatment Evaluator Version 1.6 Date 2017-10-9 Package PTE October 10, 2017 Author Adam Kapelner, Alina Levine & Justin Bleich Maintainer Adam Kapelner
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Package clusternomics
Type Package Package clusternomics March 14, 2017 Title Integrative Clustering for Heterogeneous Biomedical Datasets Version 0.1.1 Author Evelina Gabasova Maintainer Evelina Gabasova
Package sglasso. May 26, 2018
Type Package Title Lasso Method for RCON(V,E) Models Version 1.2.3 Date 2018-05-26 Author Package sglasso May 26, 2018 Maintainer Depends Matrix, igraph, R (>= 3.2) Imports
Package ManlyMix. November 19, 2017
Version 0.1.11 Date 2017-11-18 Package ManlyMix November 19, 2017 Title Manly Mixture Modeling and Model-Based Clustering Depends R (>= 3.0.0) LazyLoad yes LazyData no The utility of this package includes
Package rfsa. R topics documented: January 10, Type Package
Type Package Package rfsa January 10, 2018 Title Feasible Solution Algorithm for Finding Best Subsets and Interactions Version 0.9.1 Date 2017-12-21 Assists in statistical model building to find optimal
Package areaplot. October 18, 2017
Version 1.2-0 Date 2017-10-18 Package areaplot October 18, 2017 Title Plot Stacked Areas and Confidence Bands as Filled Polygons Imports graphics, grdevices, stats Suggests MASS Description Plot stacked
Package ecoseries. R topics documented: September 27, 2017
Package ecoseries September 27, 2017 Title An R Interface to Brazilian Central Bank and Sidra APIs and the IPEA Data Version 0.1.5 Date 2017-09-27 Maintainer Fernando Teixeira
Package BinNor. February 10, 2018
Type Package Package BinNor February 10, 2018 Title Simultaneous Generation of Multivariate Binary and Normal Variates Version 2.2 Date 2018-02-22 Author Anup Amatya, Hakan Demirtas Maintainer Anup Amatya
Package subplex. April 5, 2018
Package subplex April 5, 2018 Version 1.5-4 Date 2018-04-04 Title Unconstrained Optimization using the Subplex Algorithm License GPL-3 Depends R(>= 2.5.1) URL https://github.com/kingaa/subplex/ BugReports
Package rknn. June 9, 2015
Type Package Title Random KNN Classification and Regression Version 1.2-1 Date 2015-06-07 Package rknn June 9, 2015 Author Shengqiao Li Maintainer Shengqiao Li