Package ssmn. R topics documented: August 9, Type Package. Title Skew Scale Mixtures of Normal Distributions. Version 1.1.
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1 Package ssmn August 9, 2016 Type Package Title Skew Scale Mixtures of Normal Distributions Version 1.1 Date Author Luis Benites Sanchez and Clecio da Silva Ferreira Maintainer Luis Benites Sanchez Imports mnormt, moments, truncdist, sn Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions. License GPL (>= 2) LazyData TRUE NeedsCompilation no Repository CRAN Date/Publication :42:17 R topics documented: ssmn-package ais ssmn ssmn.est Index 6 1
2 2 ssmn-package ssmn-package Skew Scale Mixtures of Normal Distributions Details It provides the density, distribution function, quantile function, random number generator, likelihood function, direct and EM algorithm for Maximum Likelihood estimators for a given sample, all this for regression models using Skew Scale Mixtures of Normal Distributions. Package: ssmn Type: Package Version: 1.0 Date: License: GPL (>=2) Author(s) Clecio da Silva Ferreira <clecio.ferreira@ufjf.edu.br> and Luis Benites Sanchez <lbenitesanchez@gmail.com> References C.S. Ferreira; H. Bolfarine; V. H. Lachos (2011). Skew scale mixture of normal distributions: properties and estimation. Statistical Methodology, 8(2), C. S. Ferreira, V. H. Lachos, H. Bolfarine (2015). Inference and diagnostics in skew scale mixtures of normal regression models. Journal of Statistical Computation and Simulation, 85, See Also ssmn Examples #See examples 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 Fe input description BMI body mass index, weight/(height)**2 SSF sum of skin folds Bfat Percent body fat sport Sport References S. Weisberg (2005). Applied Linear Regression, 3rd edition. New York: Wiley, Section 6.4 Examples ##Load the data library(ssmn) data(ais) attach(ais) #Set the response y and covariate x x1 <- cbind(1,ssf,ht) y <- Bfat ##Fits a Skew Scale Mixtures of Normal Distributions to the data fit.ssmn <- ssmn(y, x1, family="sn", method="em", error = 1e-6, maxit=1000, show.envelope=false)
4 4 ssmn #Show envelope theta <- c(fit.ssmn$beta,fit.ssmn$sigma2,fit.ssmn$lambda) envel(y,x1,theta,family="sn",alpha=0.05) ssmn Skew Scale Mixtures of Normal Distributions It provides the density, distribution function, quantile function and random number generator for the Skew Scale Mixtures of Normal Distributions. Usage dssmn(x, location=0, scale=1, shape=0, nu= 1, gama=1, dp=null, family="sn") pssmn(q, location=0, scale=1, shape=0, nu= 1, gama=1, dp=null, family="sn") qssmn(p, location=0, scale=1, shape=0, nu= 1, gama=1, dp=null, family="sn") rssmn(n, location=0, scale=1, shape=0, nu= 1, gama=1, dp=null, family="sn") Arguments x q p n location scale shape nu gama dp family the response vector of length n where n is the total of observations. vector of quantiles. vector of probabilities. number of observations. parameter of location. parameter of scale. parameter of shape. degrees of freedom for "stn", "ssl" and "sep". For "scn", nu parameter is considered as proportion of outliers. factor scale, but only used by family "scn". vector of parameters. Distribution famility to be used in fitting ("sn", "stn", "ssl", "scn", "sep")
5 ssmn.est 5 ssmn.est EM algorithm for Skew Scale Mixtures of Normal Distributions Usage Performs the EM algorithm and envelope for regression models using Skew Scale Mixtures of Normal Distributions ssmn(y, X, family="sn", method="em", error = envel(y,x, theta, family="sn", alpha=0.05) 1e-6, maxit=1000, show.envelope=false) Arguments y X family method maxit error show.envelope alpha theta the response vector of length n where n is the total of observations. the matrix of explanatory variables of dimension nx(p + 1) where n is the total of observations and p is the number of variables. its defines the distribution to ber used: sn, stn, ssl, scn or sep. the method to calculate the maximum likelihood estimates: EM algorithm or direct maximum likelihood estimates via Newton-Raphson. Maximum number of iterations. accuracy the convergence maximum error. TRUE or FALSE. Indicates if envelope graph should be built for the fitted model. Default is FALSE. 1 - alpha is level of confidence. Estimated parameter vector Value The function returns a list with 8 elements detailed as iter tetha SE table loglik AIC BIC time number of iterations. estimated parameter vector. Standard Error estimates. Table containing the inference for the estimated parameters. Log-likelihood value. Akaike information criterion. Bayesian information criterion. processing time.
6 Index Topic datasets ais, 3 Topic package ssmn-package, 2 ais, 3 dssmn (ssmn), 4 envel (ssmn.est), 5 pssmn (ssmn), 4 qssmn (ssmn), 4 rssmn (ssmn), 4 ssmn, 2, 4 ssmn (ssmn.est), 5 ssmn-package, 2 ssmn.est, 5 6
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