CoImp (version 2.0.2)

MCAR: Generation of multivariate MCAR data

Description

Introduction of artificial missing completely at random (MCAR) data in a given data set. Missing values are multivariate and have generic pattern.

Usage

MCAR(db.complete, perc.miss = 0.3, setseed = 13, mcols = NULL, ...)

Value

An object of S4 class "MCAR", which is a list with the following element:

db.missing

Object of class "matrix". A data set with artificial multivariate MCAR.

Arguments

db.complete

the complete data matrix.

perc.miss

the percentage of missing value to be generated.

setseed

the seed for the generation of the missing values.

mcols

the index of the columns in which to introduce MCAR values.

...

further parameters for fitCopula.

Author

F. Marta L. Di Lascio <[e-mail]>,

Simone Giannerini <[e-mail]>

Details

MCAR introduce artificial missing completely at random values in a given complete data set. Missing values are multivariate and have generic pattern.

References

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

CoImp, NPCoImp, fitCopula.

Examples

# generate data from a 4-variate Gumbel copula with different margins

set.seed(11)
n.marg <- 4
theta  <- 5
copula <- frankCopula(theta, dim = n.marg)
mymvdc <- mvdc(copula, c("norm", "gamma", "beta","gamma"), list(list(mean=7, sd=2),
list(shape=3, rate=2), list(shape1=4, shape2=1), list(shape=4, rate=3)))
n      <- 30
x.samp <- rMvdc(n, mymvdc)

# apply MCAR by introducing 30% of missing data

mcar   <- MCAR(db.complete = x.samp, perc.miss = 0.3, setseed = 11)

mcar

# same example as above but introducing missing only in the first and third column

mcar2   <- MCAR(db.complete = x.samp, perc.miss = 0.3, setseed = 11, mcols=c(1,3))

mcar2