CUBM package in R to fit CUB models

• Autores: Freddy Hernández Barajas, Olga C. Úsuga-Manco, Sebastián García Muñoz
• Localización: Comunicaciones en Estadística, ISSN 2027-3355, ISSN-e 2339-3076, Vol. 11, Nº. 2, 2018, págs. 219-238
• Idioma: inglés
• DOI: 10.15332/2422474x.3857
• Enlaces
  • Texto completo (pdf)
• Resumen

  • The class of CUB models is commonly used by practitioners to model ordinal data, in this paper we propose the cubm package which provides the class of CUB models in the R system for statistical computing. The cubm package allows to specify a formula for each parameter of the model, the Maximum Likelihood (ML) estimation is performed by optimization via the functions nlminb, optim and DEoptim and the variance-covariance matrix can be obtained by numerical approximation of the Hessian matrix or by bootstrap method. The utility of the package is illustrated by an application and a simulation study.

• Referencias bibliográficas
  • Arboretti, R. & Bordignon, P. (2016), ‘Consumer preferences in food packaging: Cub models and conjoint analysis’, British Food Journal...
  • Ardia, D., Boudt, K., Carl, P., Mullen, K. M. & Peterson, B. G. (2011), ‘Differential Evolution with DEoptim: An application to non-convex...
  • Boatto, V., Rossetto, L., Bordignon, P., Arboretti, R., Salmaso, L., Griffith, C. & Griffith, C. (2016), ‘Cheese perception in the north...
  • Cafarelli, B. & Crocetta, C. (2016), An evaluation of the student satisfaction based on cub models, in ‘Topics in Theoretical and Applied...
  • Capecchi, S. (2015), ‘Modelling the perception of conflict in working conditions’, Electronic Journal of Applied Statistical Analysis 8(3),...
  • Capecchi, S. & Piccolo, D. (2014), Modelling the latent components of personal happiness, in ‘Mathematical and Statistical Methods for...
  • Corduas, M. (2011), Assessing similarity of rating distributions by kullback-leibler divergence, in ‘Classification and Multivariate Analysis...
  • D’Elia, A. & Piccolo, D. (2005), ‘A mixture model for preference data analysis’, Computational Statistics Data Analysis 49(3), 917-934.
  • Gambacorta, R. & Iannario, M. (2013), ‘Measuring job satisfaction with cub models’, Labour 27(2), 198-224.
  • Gambacorta, R., Iannario, M. & Valliant, R. (2014), ‘Design-based inference in a mixture model for ordinal variables for a two stage stratified...
  • Gilbert, P. & Varadhan, R. (2016), numDeriv: Accurate Numerical Derivatives. R package version 2016.8-1.*https://CRAN.R-project.org/package=numDeriv.
  • Grilli, L., Iannario, M., Piccolo, D. & Rampichini, C. (2014), ‘Latent class cub models’, Advances in Data Analysis and Classification...
  • Iannario, M. (2008), ‘Dummy covariates in cub models’, Statistica 68(2), 179-200.
  • Iannario, M. (2010), ‘On the identifiability of a mixture model for ordinal data’, Metron 68(1), 87-94.
  • Iannario, M. (2012), ‘Hierarchical cub models for ordinal variables’, Communications in Statistics-Theory and Methods 41(16-17), 3110-3125.
  • Iannario, M. (2014), ‘Modelling uncertainty and overdispersion in ordinal data’, Communications in Statistics-Theory and Methods 43(4), 771-786.
  • Iannario, M., Manisera, M., Piccolo, D. & Zuccolotto, P. (2012), ‘Sensory analysis in the food industry as a tool for marketing decisions’,...
  • Iannario, M. & Piccolo, D. (2010), ‘A new statistical model for the analysis of customer satisfaction’, Quality Technology & Quantitative...
  • Iannario, M. & Piccolo, D. (2012), ‘Cub models: Statistical methods and empirical evidence’, Modern Analysis of Customer Surveys pp. 231-258.
  • Iannario, M. & Piccolo, D. (2015), ‘A generalized framework for modelling ordinal data’, Statistical Methods & Applications pp. 1-27.
  • Iannario, M., Piccolo, D. & Simone, R. (2016), CUB: A Class of Mixture Models for Ordinal Data. R package version 1.0. *https://CRAN.R-project.org/package=CUB
  • Innario, M. (2012), ‘Cube models for interpreting ordered categorical data with overdispersion’, Quaderni di statistica 14(14), 137-140.
  • Manisera, M. & Zuccolotto, P. (2013), ‘Nonlinear cub models: some stylized facts’,Quaderni di Statistica 15, 111-130.
  • Manisera, M. & Zuccolotto, P. (2014), ‘Modeling rating data with nonlinear cub models’, Computational Statistics & Data Analysis 78,...
  • Manisera, M. & Zuccolotto, P. (2015), ‘Visualizing multiple results from nonlinear cub models with r grid viewports’, Electronic Journal...
  • Manisera, M. & Zuccolotto, P. (2016), ‘Treatment of "don’t knowresponses in a mixture model for rating data’, METRON 74(1), 99-115.
  • McLachlan, G. & Krishnan, T. (1997), ‘The em algorithm and extensions’.
  • Mullen, K., Ardia, D., Gil, D., Windover, D. & Cline, J. (2011), ‘DEoptim: An R package for global optimization by differential evolution’,...
  • Oberski, D. & Vermunt, J. (2015), ‘The cub model and its variations are restricted loglinear latent class models’, EJASA .
  • Piccolo, D. (2003a), ‘Computational issues in the em algorithm for ranks model estimation with covariates’, Quaderni di Statistica 5, 1-22.
  • Piccolo, D. (2003b), ‘On the moments of a mixture of uniform and shifted binomial random variables’, Quaderni di Statistica 5(1), 85-104.
  • Piccolo, D. (2015), ‘Inferential issues on cube models with covariates’, Communications in Statistics-Theory and Methods 44(23), 5023-5036.