Generation of multivariate missing at random (MAR) data

Description

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

Usage

MAR(db.complete, perc.miss = 0.3, setseed = 13, ...)

Arguments

Details

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

Value

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

Author(s)

Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>,

Simone Giannerini <simone.giannerini@unibo.it>

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. (201x) "Exploring Copulas for the Imputation of Complex Dependent Data". Under review.

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.

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      <- 50
x.samp <- rMvdc(n, mymvdc)

# apply MAR by introducing 30% of missing data

mar    <- MAR(db.complete = x.samp, perc.miss = 0.3, seed = 11)

mar

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CoImp)
Loading required package: copula
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/CoImp/MAR.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MAR
> ### Title: Generation of multivariate missing at random (MAR) data
> ### Aliases: MAR
> ### Keywords: imputation copula multivariate
> 
> ### ** 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      <- 50
> x.samp <- rMvdc(n, mymvdc)
> 
> # apply MAR by introducing 30% of missing data
> 
> mar    <- MAR(db.complete = x.samp, perc.miss = 0.3, seed = 11)
# weights:  84 (65 variable)
initial  value 131.952866 
iter  10 value 114.319541
iter  20 value 93.231986
iter  30 value 81.003803
iter  40 value 77.432959
iter  50 value 76.678644
iter  60 value 76.290102
iter  70 value 75.841339
iter  80 value 75.637246
iter  90 value 75.487270
iter 100 value 75.336695
final  value 75.336695 
stopped after 100 iterations
> 
> mar
An object of class "MAR"
Slot "perc.record.missing":
[1] 26

Slot "db.missing":
           [,1]      [,2]      [,3]      [,4]
 [1,]  5.966937 0.6937296 0.5305706 1.2118824
 [2,]  5.477936 0.9288640 0.5150143 0.4949318
 [3,]  9.976381 2.6692100        NA        NA
 [4,]  6.888685 1.7600194 0.7823500 1.0113803
 [5,]  3.628433 0.8100146 0.7102281 0.8478551
 [6,]  6.895156 1.7180514 0.9665612 1.3927726
 [7,]  7.354376 0.9179923 0.8124650 1.7454148
 [8,]  7.399243 0.9139131 0.7225338 0.7816251
 [9,]  6.461663 1.2354032 0.5513096 0.7880760
[10,]  6.814336 1.7336717 0.9263767 1.8312253
[11,]  9.097894 1.9931929 0.9627481 1.6991499
[12,]  5.446450 1.0184831 0.9438978 1.2199807
[13,]  7.924009 1.9975273 0.9433903 1.2505458
[14,]        NA 0.4378481 0.6480485 0.3242802
[15,]  7.949878 1.3034472 0.8053477 1.5037154
[16,]        NA 1.4831395        NA 0.7150905
[17,]        NA 0.5759644        NA        NA
[18,]  6.527263 0.7277481 0.8131407 0.8802033
[19,]  5.675309 2.4925278 0.8496751 0.6941416
[20,]  3.818380 0.3763987 0.7767131 0.5063563
[21,]  5.436949 0.8697335 0.8653798 0.9885767
[22,]  7.167009 2.2176979 0.9738580 1.4343013
[23,]        NA 1.7383487        NA 1.8237743
[24,]  3.429688 0.9427765 0.7102854 0.8887351
[25,]  8.234457 2.2780989 0.9525857 1.1375883
[26,]        NA        NA 0.9260042 2.3968852
[27,]  4.450936        NA        NA 0.9303377
[28,]  8.734755        NA        NA        NA
[29,]  9.184101 1.4889443 0.9372123 1.9182844
[30,]  8.604847 1.7325143 0.8893063 1.6961042
[31,]        NA 3.3361345 0.9389256 1.4852776
[32,]  8.636361 1.3668753 0.8670165 2.0932573
[33,]  3.669162 0.3441561 0.5348765 0.8329608
[34,]  8.253179 1.7551735 0.9310083 1.8587761
[35,]  7.327129 2.5566402 0.9409496 1.2298919
[36,]  5.961533 0.7937996 0.7655772 0.9888726
[37,]  4.891002 0.6748644 0.6973654 0.7516644
[38,]        NA        NA 0.7616119        NA
[39,]  7.677238 1.4926245 0.8330594 1.1269745
[40,] 10.165130 1.5522208 0.6160808 1.0114968
[41,]  8.721631 1.7246080 0.9985602 1.7605457
[42,]  5.781648 0.5967000 0.7081025 0.6994077
[43,]  5.454900 1.5180687 0.6134994 0.9564612
[44,]  8.931144        NA 0.9939651        NA
[45,]  3.710308 0.3188043        NA 1.6126164
[46,]  8.534144 2.4111056 0.9490516 1.9093087
[47,]  8.663845        NA 0.9659448        NA
[48,]  4.186640 0.7075278 0.7830599 0.5113352
[49,]  6.478190 0.4516585 0.5731004 0.7755582
[50,]  5.000664 1.1474243 0.5143085 0.5679166

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> 
> dev.off()
null device 
          1 
>