Package flexcwm. R topics documented: July 11, Type Package. Title Flexible Cluster-Weighted Modeling. Version 1.0.
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1 Package flexcwm July 11, 2013 Type Package Title Flexible Cluster-Weighted Modeling Version 1.0 Date Author Incarbone G., Mazza A., Punzo A., Ingrassia S. Maintainer Angelo Mazza Description Flexible Cluster Weighted Models. License GPL-2 LazyLoad yes Depends R (>= ) Imports numderiv,mnormt,mclust,ellipse,mixture,flury NeedsCompilation no Repository CRAN Date/Publication :49:02 R topics documented: flexcwm-package gcwm tourism Index 7 1
2 2 gcwm flexcwm-package flexcwm - Flexible Cluster Weighted Models. Description Details This package allows for Flexible Cluster Weighted Modeling. Package: flexcwm Type: Package Version: 1.0 Date: License: GNU-2 Author(s) Incarbone G., Mazza A., Punzo A., Ingrassia S. Maintainer: Mazza Angelo <a.mazza@unict.it> References Ingrassia, S., Minotti, S. C., and Vittadini, G. (2012). Local statistical modeling via the clusterweighted approach with elliptical distributions. Journal of Classification, 29(3), Ingrassia, S., Minotti, S. C., Punzo, A., and Vittadini, G. (2012). Generalized linear Gaussian cluster-weighted modeling. arxiv.org e-print , available at: See Also gcwm, tourism gcwm Flexible Cluster-Weighted Modeling Description Run the Generalized Linear Gaussian Cluster-Weighted Model
3 gcwm 3 Usage gcwm (Y, X, familyy="gaussian", k=2, ic=c("bic", "AIC", "ICL"), my=1, method="nelder-mead", initialization="random.soft", start.z=null, iter.max=1000, threshold=1.0e-04, loglikplot=false, seed=null) Arguments Y X k ic familyy my method numerical vector for the response variable. matrix for the covariates. the exponential distribution used for Y x in each cluster; it can be: "Gaussian" "Poisson" "Binomial" "Gamma" Default value is "Gaussian". a vector containing the numbers of clusters to be tried. The one with the lowest information criterion is selected. Default value is 2. the information criteria by which the best model is selected when lencgth(k)>1. Possible values are "BIC" "AIC" "ICL" When familyy="binomial", it sets the sample size. Default value is 1 (Bernoulli distribution). optimization method used in the M-step of the EM algorithm (see optim). Default value is "Nelder-Mead". initialization initialization strategy for the EM-algorithm. It can be: start.z "random.soft" "random.hard" "manual" Default value is "random.soft". matrix of soft or hard classification: it is used only if initialization="manual". iter.max maximum number of iterations in the EM-algorithm. Default value is 200. threshold loglikplot seed threshold for Aitken acceleration procedure. Default value is 1.0e-04. if TRUE, the log-likelihood values against the iterations are plotted. Default value FALSE. the seed for the random number generator, when random initializations are used; if NULL, current seed is not changed. Default value is NULL.
4 4 gcwm Value This function returns a list of values related to the model selected. It contains: Y X familyy p k n npar my prior mux VarX PX beta muy dispy VarFunY VarY nuy PY iter.stop z group loglik AIC BIC ICL call response variable covariates exponential distribution used for Y x in each cluster number of covariates number of groups sample size number of parameters sample size, used when familyy="binomial" weights for the mixture components covariates means covariates variances marginal distribution of X for each cluster regression coefficients mean of Y dispersion parameter of Y variance function of Y variance of Y when familyy="gamma", the gamma distribution is parameterized according to muy and nuy (see McCullagh, P. and Nelder, J. 1989) conditional distribution of Y x for each cluster number of iterations performed in EM algorithm matrix of posterior probabilities classification vector final log-likelihood value an object of class call Author(s) Incarbone G., Mazza A., Punzo A., Ingrassia S.
5 tourism 5 References Ingrassia, S., Minotti, S. C., and Vittadini, G. (2012). Local statistical modeling via the clusterweighted approach with elliptical distributions. Journal of Classification, 29(3), Ingrassia, S., Minotti, S. C., Punzo, A., and Vittadini, G. (2012). Generalized linear Gaussian cluster-weighted modeling. arxiv.org e-print , available at: McCullagh, P. and Nelder, J. (1989). Generalized Linear Models. Chapman & Hall, Boca Raton, 2nd edition See Also flexcwm-package, tourism Examples data(tourism) Y <- tourism$overnights X <- tourism$attendance res <- gcwm(y=y,x=x,k=1:4,seed=1) plot(cbind(y,x),col=res$best$group) tourism tourism Description The tourism dataset consists of 180 monthly data concerning tourist overnights (variable overnights) and attendance at museums and monuments in Italy (variable attendance) over the 15-year period spanning from January 1996 to December Usage data(tourism) Format A dataset Source data pdf
6 6 tourism References Cellini, R., & Cuccia, T. (2013). Museum and monument attendance and tourism flow: A time series analysis approach. Applied Economics, 45, Ingrassia, S., Minotti, S. C., and Punzo, A. (2013). Model-based clustering via linear clusterweighted models. Computational Statistics and Data Analysis, DOI: /j.csda See Also flexcwm-package, gcwm
7 Index Topic datasets tourism, 5 flexcwm (flexcwm-package), 2 flexcwm-package, 2 gcwm, 2, 2, 6 optim, 3 tourism, 2, 5, 5 7
Package flexcwm. R topics documented: July 2, Type Package. Title Flexible Cluster-Weighted Modeling. Version 1.1.
Package flexcwm July 2, 2014 Type Package Title Flexible Cluster-Weighted Modeling Version 1.1 Date 2013-11-03 Author Mazza A., Punzo A., Ingrassia S. Maintainer Angelo Mazza Description
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