CoImp-class {CoImp}

Class "CoImp"

Description

A class for CoImp and its extensions

Objects from the Class

Objects can be created by calls of the form new("CoImp", ...).

Slots

Missing.data.matrix:

Object of class "matrix". Original missing data matrix to be imputed.

Perc.miss:

Object of class "matrix". Missing and available data percentage for each variable.

Estimated.Model:

Object of class "list". The list contains:

model	the copula model selected and estimated on the complete cases.
dimension	the dimension of the model.
parameter	the estimated dependence parameter of the model.
number	the index of the estimated model in the list of models given in input.

Estimation.Method:

Object of class "character". The estimation method used for the copula model in Estimated.Model. Allowed methods are in fitCopula.

Index.matrix.NA:

Object of class "matrix". Matrix of row and column indexes of missing data.

Smooth.param:

Object of class "numeric". The values of the nearest neighbor component of the smoothing parameter of the lp function.

Imputed.data.matrix

Object of class "matrix". The imputed data matrix.

Estimated.Model.Imp

Object of class "list". The list contains:

model	the copula model selected and estimated on the imputed cases.
dimension	the dimension of the model.
parameter	the estimated dependence parameter of the model.
number	the index of the estimated model in the list of models given in input.

Estimation.Method.Imp

Object of class "character".The estimation method used for the copula model in Estimated.Model.Imp. Allowed methods are in fitCopula.

Methods

plot

signature(x = "CoImp", y = "missing"): ...

show

signature(object = "CoImp"): ...

Author(s)

Francesca Marta Lilja Di Lascio <[e-mail]>,

Simone Giannerini <[e-mail]>

References

Di Lascio, F.M.L. Giannerini, S. and Reale A. (201x) "A multivariate technique based on conditional copula specification for the imputation of complex dependent data". Working paper.

Di Lascio, F.M.L., Giannerini, S. and Reale, A. (2015) "Exploring Copulas for the Imputation of Complex Dependent Data". Statistical Methods & Applications, 24(1), p. 159-175. DOI 10.1007/s10260-014-0287-2.

Di Lascio, F.M.L., Giannerini, S. and Reale, A. (2014) "Imputation of complex dependent data by conditional copulas: analytic versus semiparametric approach", Book of proceedings of the 21st International Conference on Computational Statistics (COMPSTAT 2014), p. 491-497. ISBN 9782839913478.

Bianchi, G. Di Lascio, F.M.L. Giannerini, S. Manzari, A. Reale, A. and Ruocco, G. (2009) "Exploring copulas for the imputation of missing nonlinearly dependent data". Proceedings of the VII Meeting Classification and Data Analysis Group of the Italian Statistical Society (Cladag), Editors: Salvatore Ingrassia and Roberto Rocci, Cleup, p. 429-432. ISBN: 978-88-6129-406-6.

See Also

See Also CoImp, MCAR, MAR, lp and copula.

Examples

showClass("CoImp")