Package FMsmsnReg. March 30, 2016
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1 Package FMsmsnReg March 30, 2016 Type Package Title Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors Version 1.0 Date Author Luis Benites Sanchez and Rocio Paola Maehara and Victor Hugo Lachos Maintainer Luis Benites Sanchez Imports mvtnorm Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy- Tailed Errors. License GPL (>= 2) Repository CRAN NeedsCompilation no Date/Publication :14:20 R topics documented: FMsmsnReg-package ais FMsmsnReg horses Index 8 1
2 2 FMsmsnReg-package FMsmsnReg-package Linear Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors Details This package contains a principal function that performs to estimate the parameters of a regression model considering an error that follows a finite mixture of Skew Heavy-Tailed Errors, using an analytically simple and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters. Package: FMsmsnsReg Type: Package Version: 1.0 Date: License: GPL (>=2) Author(s) Luis Benites Sanchez <lbenitesanchez@gmail.com>, Rocio Paola Maehara <rmaeharaa@gmail.com> and Victor Hugo Lachos <hlachos@ime.unicamp.br> References Basso, R. M., Lachos, V. H., Cabral, C. R., Ghosh, P., Robust mixture modeling based on scale mixtures of skew - normal distributions. Computational Statistics & Data Analysis. Lachos, V. H., Ghosh, P., Arellano-Valle, R. B., Likelihood based inference for skew-normal independent linear mixed models. Statistica Sinica 20, See Also FMsmsnReg Examples #See examples for the FMsmsnReg function linked above.
3 ais 3 ais Australian institute of sport data Data on 102 male and 100 female athletes collected at the Australian Institute of Sport. Format This data frame contains the following columns: Sex (0 = male or 1 = female) Ht height (cm) Wt weight (kg) LBM lean body mass RCC red cell count WCC white cell count Hc Hematocrit Hg Hemoglobin Ferr plasma ferritin concentration BMI body mass index, weight/height^2 SSF sum of skin folds Bfat Percent body fat Label Case Labels Sport Sport References S. Weisberg (2005). Applied Linear Regression, 3rd edition. New York: Wiley, Section 6.4 Examples ##Load the data library(fmsmsnreg) data(ais) attach(ais) #Set the response y and covariate x x1 <- cbind(1,ssf,ht) y <- Bfat ##Fits a linear Regression Model with Finite Mixtures of Skew t parst <- FMsmsnReg(y, x1, g=2, get.init = TRUE, criteria = TRUE, group = FALSE,family = "Skew.t", error = 10^-4,
4 4 FMsmsnReg iter.max = 2000,obs.prob= FALSE, kmeans.param = NULL,show.converge=FALSE,cp=0.5) ##Fits a linear Regression Models with Finite Mixtures of Skew normal parsn <- FMsmsnReg(y, x1, g=2, get.init = TRUE, criteria = TRUE, group = FALSE,family = "Skew.normal", error = 10^-4, iter.max = 5000,obs.prob= FALSE, kmeans.param = NULL,show.converge=FALSE,cp=0.5) ##Fits a linear Regression Models with Finite Mixtures of Skew Contaminated Normal parcn <- FMsmsnReg(y, x1, g=2, get.init = TRUE, criteria = TRUE, group = FALSE,family = "Skew.cn", error = 10^-4, iter.max = 5000,obs.prob= FALSE, kmeans.param = NULL,show.converge=FALSE,cp=0.5) FMsmsnReg Linear Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors Usage Performs a Finite Mixture of Scale Mixture Skew Normal Regression Model using EM-type algorithm (ECME) for iteratively computing maximum likelihood estimates of the parameters. FMsmsnReg(y, x1, Abetas = NULL, medj= NULL, sigma2 = NULL, shape = NULL, pii = NULL, g = NULL, get.init = TRUE, criteria = TRUE, group = FALSE, family = "Skew.normal", error = , iter.max = 100, obs.prob= FALSE, kmeans.param = NULL, show.convergence=true, cp=0.4) Arguments y x1 Abetas medj sigma2 shape the response matrix (dimension nx1) Matrix or vector of covariates. Parameters of vector regression dimension (p + 1) include intercept a list of g arguments of vectors of initial values (dimension p) for the location parameters a list of g arguments of matrices of initial values (dimension pxp) for the scale parameters a list of g arguments of vectors of initial values (dimension p) for the skewness parameters pii Initial value for the EM algorithm. Each of them must be a vector of length g. (the algorithm considers the number of components to be adjusted based on the size of these vectors) g get.init criteria the number of cluster to be considered in fitting if TRUE, the initial values are generated via k-means It indicates if are calculated the criterion selection methods (AIC, BIC, EDC and ICL)
5 FMsmsnReg 5 group family error iter.max obs.prob kmeans.param if TRUE, the vector with the classification of the response is returned distribution famility to be used in fitting (Skew.t", "Skew.cn", "Skew.slash", "Skew.normal") define the stopping criterion of the algorithm the maximum number of iterations of the EM algorithm if TRUE, the posterior probability of each observation belonging to one of the g groups is reported a list with alternative parameters for the kmeans function when generating initial values, list(iter.max = 10, n.start = 1, algorithm = "Hartigan-Wong") show.convergence graphics of convergence for the parameters cp Cut Point Value The function returns a list with 16 elements detailed as iter criteria convergence mu sigma2 lambda pii nu SE table loglik AIC BIC EDC ICL time Number of iterations. Attained criteria value. Convergence reached or not. Location parameter estimate. Scale parameter estimate. Shape parameter estimate. Weight parameter estimate. Estimated degrees of freedom parameter. Standard Error estimates, if the output shows NA the function does not provide the standard error for this parameter. Table containing the inference for the estimated parameters. Log-likelihood value. Akaike information criterion. Bayesian information criterion. Efficient Determination Criterion. Information Completed Likelihood. Processing time. Author(s) Luis Benites Sanchez <lbenitesanchez@gmail.com> and Rocio Paola Maehara <rmaeharaa@gmail.com> and Victor Hugo Lachos <hlachos@ime.unicamp.br>
6 6 horses References Basso, R.. M., Lachos, V. H., Cabral, C. R., Ghosh, P., Robust mixture modeling based on scale mixtures of skew-normal distributions. Computational Statistics & Data Analysis doi: /j.csda Lachos, V. H., Ghosh, P., Arellano-Valle, R. B., Likelihood based inference for skew - normal independent linear mixed models. Statistica Sinica 20, See Also FMsmsnReg, ais, horses Examples #See examples for the FMsmsnReg function linked above. horses Horse Racing at Eagle Farm data Results of horse races at Eagle Farm, Brisbane, on 31 August The data, collected by Donald Forbes for his MS305 Data Analysis Project, give results for each horse in a sequence of 8 races. Format This data frame contains the following columns: Position (Finishing position) Starters Number of horses in race Last Finishing position in last race Since Days since last race Number Identifying number of horse in race Carried Weight carried Weight Handicap weight Barrier Barrier position at start of race Distance Length of race Lengths Number of lengths that horse finished from winner Odds Starting odds Starts Number of races previously started in Age Age of horse in years Ratio Proportion of wins in previous starts
7 horses 7 References Forbes, D. (1998). A Day at the Races. MS305 Data Analysis Project, Department of Mathematics, University of Queensland. Examples ##Load the data library(fmsmsnreg) data(horses) attach(horses) #Set the response y and covariate x x1 <- cbind(1,last,carried) y <- Position ##Fits a linear Regression Model with Finite Mixtures of Skew Contaminated Normal parcn <- FMsmsnReg(y, x1, g=2, get.init = TRUE, criteria = TRUE, group = FALSE, family = "Skew.cn", error = 10^-4, iter.max = 5000,obs.prob= FALSE, kmeans.param = NULL,show.converge=FALSE,cp=0.5) ##Fits a linear Regression Model with Finite Mixtures of Skew normal parsn <- FMsmsnReg(y, x1, g=2, get.init = TRUE, criteria = TRUE, group = FALSE,family = "Skew.normal", error = 10^-4, iter.max = 5000,obs.prob= FALSE, kmeans.param = NULL,show.converge=FALSE,cp=0.5)
8 Index Topic EM FMsmsnReg, 4 Topic Finite Mixture FMsmsnReg, 4 Topic datasets ais, 3 horses, 6 Topic package FMsmsnReg, 4 FMsmsnReg-package, 2 Topic smsn FMsmsnReg, 4 ais, 3, 6 FMsmsnReg, 2, 4, 6 FMsmsnReg-package, 2 horses, 6, 6 8
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Type Package Package RLRsim November 4, 2016 Title Exact (Restricted) Likelihood Ratio Tests for Mixed and Additive Models Version 3.1-3 Date 2016-11-03 Maintainer Fabian Scheipl
Package gplm. August 29, 2016
Type Package Title Generalized Partial Linear Models (GPLM) Version 0.7-4 Date 2016-08-28 Author Package gplm August 29, 2016 Maintainer Provides functions for estimating a generalized
Package esabcv. May 29, 2015
Package esabcv May 29, 2015 Title Estimate Number of Latent Factors and Factor Matrix for Factor Analysis Version 1.2.1 These functions estimate the latent factors of a given matrix, no matter it is highdimensional
Package mcemglm. November 29, 2015
Type Package Package mcemglm November 29, 2015 Title Maximum Likelihood Estimation for Generalized Linear Mixed Models Version 1.1 Date 2015-11-28 Author Felipe Acosta Archila Maintainer Maximum likelihood
Package msda. February 20, 2015
Title Multi-Class Sparse Discriminant Analysis Version 1.0.2 Date 2014-09-26 Package msda February 20, 2015 Author Maintainer Yi Yang Depends Matri, MASS Efficient procedures for computing
Package DCEM. September 30, 2018
Type Package Package DCEM September 30, 2018 Title Clustering for Multivariate and Univariate Data Using Expectation Maximization Algorithm Version 0.0.1 Maintainer Sharma Parichit Implements
Predicting Diabetes using Neural Networks and Randomized Optimization
Predicting Diabetes using Neural Networks and Randomized Optimization Kunal Sharma GTID: ksharma74 CS 4641 Machine Learning Abstract This paper analysis the following randomized optimization techniques