Package CoImp. February 19, 2015
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1 Title Copula based imputation method Date Version Package CoImp February 19, 2015 Author Francesca Marta Lilja Di Lascio, Simone Giannerini Depends R (>= ), methods, copula Imports nnet, gtools, locfit Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications. Maintainer Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it> License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication :16:11 R topics documented: CoImp CoImp-class MAR MAR-class MCAR MCAR-class PerfMeasure PerfMeasure-class Index 15 1
2 2 CoImp CoImp Copula-Based Imputation Method Imputation method based on conditional copula functions. Usage CoImp(X, n.marg = 2, type.data = "continuous", smoothing = rep(0.5,n.marg), plot.marg = TRUE, plot.bar = TRUE, plot.legend = TRUE, args.legend = list(y = 110, cex = 0.8), model = list(normalcopula(0.5, dim=n.marg, dispstr="ex"), claytoncopula(10, dim=n.marg),gumbelcopula(10, dim=n.marg), frankcopula(10, dim=n.marg)),...) Arguments X Details a data matrix with missing values. Missing values should be denoted with NA. n.marg the number of variables in X. type.data smoothing plot.marg plot.bar plot.legend args.legend model the nature of the variables in X: discrete or continuous. values for the nearest neighbour component of the smoothing parameter of the lp function. logical: if TRUE plots the estimated marginal densities. logical: if TRUE shows a bar plot of the percentages of missing and available data for each margin. logical: see barplot. list of additional arguments to pass to legend. a list of copula models to be used for the imputation, see the Details section. This should be one of normal, frank, clayton and gumbel.... further parameters for fitcopula, lp and further graphical arguments. CoImp is an imputation method based on conditional copula functions that allows to impute missing observations according to the multivariate dependence structure of the generating process without any assumptions on the margins. This method can be used independently from the dimension and the kind (monotone or non monotone) of the missing patterns. Brief description of the approach: 1. estimate both the margins and the copula model on available data by means of the semiparametric sequential two-step inference for margins; 2. derive conditional density functions of the missing variables given non-missing ones through the corresponding conditional copulas obtained by using the Bayes rule;
3 CoImp 3 3. impute missing values by drawing observations from the conditional density functions derived at the previous step. The Monte Carlo method used is the Hit or Miss. The estimation approach for the copula fit is semiparametric: a range of nonparametric margins and parametric copula models can be selected by the user. Value An object of S4 class "CoImp", which is a list with the following elements: Missing.data.matrix the original missing data matrix to be imputed. Perc.miss the matrix of the percentage of missing and available data. Estimated.Model the estimated copula model on the available data. Estimation.Method the estimation method used for the copula Estimated.Model. Index.matrix.NA matrix indices of the missing data. Smooth.param the smoothing parameter alpha selected on the basis of the AIC. Imputed.data.matrix the imputed data matrix. Estimated.Model.Imp the estimated copula model on the imputed data matrix. Estimation.Method.Imp the estimation method used for the copula Estimated.Model.Imp.
4 4 CoImp-class # 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 <- 20 x.samp <- copula::rmvdc(n, mymvdc) # randomly introduce univariate and multivariate missing perc.mis <- 0.5 set.seed(11) miss.row <- sample(1:n, perc.mis*n, replace=true) miss.col <- sample(1:n.marg, perc.mis*n, replace=true) miss <- cbind(miss.row,miss.col) x.samp.miss <- replace(x.samp,miss,na) # impute missing values imp <- CoImp(x.samp.miss, n.marg=n.marg, smoothing = rep(0.6,n.marg), TRUE, TRUE, TRUE, type.data="continuous"); # methods show and plot show(imp) plot(imp) CoImp-class Class "CoImp" 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:
5 CoImp-class 5 model dimension parameter number the copula model selected and estimated on the complete cases. the dimension of the model. the estimated dependence parameter of the model. 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 dimension parameter number the copula model selected and estimated on the imputed cases. the dimension of the model. the estimated dependence parameter of the model. 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"):... See Also See Also CoImp, lp and copula.
6 6 MAR showclass("coimp") MAR Generation of multivariate missing at random (MAR) data 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 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.... further parameters for fitcopula. 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: perc.record.missing Object of class "numeric". A percentage value. db.missing Object of class "matrix". A data set with artificial multivariate MAR.
7 MAR-class 7 # 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 MAR-class Class "MAR" A class for MAR and its extensions Objects from the Class Slots Objects can be created by calls of the form new("mar",...). perc.record.missing: Object of class "numeric". A percentage value. db.missing: Object of class "matrix". A data set with artificial multivariate MAR with generic pattern.
8 8 MCAR Methods show signature(object = "MAR"):... See Also See Also CoImp, lp and copula. showclass("mar") MCAR Generation of multivariate MCAR data Usage Introduction of artificial missing completely at random (MCAR) data in a given data set. Missing values are multivariate and have generic pattern. MCAR(db.complete, perc.miss = 0.3, setseed = 13,...) 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.... further parameters for fitcopula.
9 MCAR 9 Details MCAR introduce artificial missing completely at random values in a given complete data set. Missing values are multivariate and have generic pattern. 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. # 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 MCAR by introducing 30% of missing data mcar <- MCAR(db.complete = x.samp, perc.miss = 0.3, seed = 11) mcar
10 10 MCAR-class MCAR-class Class "MCAR" A class for MCAR and its extensions Objects from the Class Objects can be created by calls of the form new("mcar",...). Slots db.missing: Object of class "matrix". A data set with artificial multivariate MCAR. Methods show signature(object = "MCAR"):... See Also See Also CoImp, lp and copula. showclass("mcar")
11 PerfMeasure 11 PerfMeasure Performance measures for evaluating the goodness of an imputed database Set of measures useful to evaluate the goodness of the used imputation method. Usage PerfMeasure(db.complete, db.imputed, db.missing, n.marg = 2, model = list(normalcopula(0.5, dim=n.marg, dispstr="ex"), claytoncopula(10, dim=n.marg), gumbelcopula(10, dim=n.marg), frankcopula(10, dim=n.marg)),...) Arguments Details db.complete db.imputed db.missing n.marg model the complete data matrix. the imputed data matrix. the data matrix with NA data. the number of variables in db.complete. a list of copula models to be used for the imputation. See the Details section. This should be one of normal, frank, clayton and gumbel.... further parameters for fitcopula. PerfMeasure computes some measures useful for evaluating the goodness of the used imputation method. PerfMeasure requires in input the imputed, the complete and the missing data matrix and gives in output five different measures of performance. See below for details Value An object of S4 class "PerfMeasure", which is a list with the following elements: MARE RB RRMSE TID Object of class "numeric". The mean (on the replications performed) of the absolute relative error between the imputed and the corresponding original value. Object of class "numeric". The relative bias of the estimator for the dependence parameter. Object of class "numeric". The relative root mean squared error of the estimator for the dependence parameter. Object of class "vector". Upper and lower tail dependence indexes for bivariate copulas. Original function is in tailindex.
12 12 PerfMeasure # 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 <- 20 x.samp <- rmvdc(n, mymvdc) # randomly introduce univariate and multivariate missing perc.mis <- 0.5 set.seed(11) miss.row <- sample(1:n, perc.mis*n, replace=true) miss.col <- sample(1:n.marg, perc.mis*n, replace=true) miss <- cbind(miss.row,miss.col) x.samp.miss <- replace(x.samp,miss,na) # impute missing values imp <- CoImp(x.samp.miss, n.marg=n.marg, smoothing = rep(0.6,n.marg), TRUE, TRUE, TRUE, type.data="continuous"); imp # apply PerfMeasure to the imputed data set pm <- PerfMeasure(db.complete=x.samp, db.missing=x.samp.miss, db.imputed=imp@"imputed.data.matrix", n.marg=4) pm
13 PerfMeasure-class 13 str(pm) PerfMeasure-class Class "PerfMeasure" A class for PerfMeasure and its extensions Objects from the Class Objects can be created by calls of the form new("perfmeasure",...). Slots MARE: Object of class "numeric". The mean (on the replications performed) of the absolute relative error between the imputed and the corresponding original value. RB: Object of class "numeric". The relative bias of the estimator for the dependence parameter. RRMSE: Object of class "numeric". The relative root mean squared error of the estimator for the dependence parameter. TID: Object of class "vector". Upper and lower tail dependence indexes for bivariate copulas. Original function is in tailindex. Methods show signature(object = "PerfMeasure"):...
14 14 PerfMeasure-class See Also See Also CoImp, lp and copula. showclass("perfmeasure")
15 Index Topic classes CoImp-class, 4 MAR-class, 7 MCAR-class, 10 PerfMeasure-class, 13 Topic copula CoImp, 2 MAR, 6 MCAR, 8 PerfMeasure, 11 Topic imputation CoImp, 2 MAR, 6 MCAR, 8 PerfMeasure, 11 Topic multivariate CoImp, 2 MAR, 6 MCAR, 8 PerfMeasure, 11 show,coimp-method (CoImp-class), 4 show,perfmeasure-method (PerfMeasure-class), 13 tailindex, 11, 13 barplot, 2 CoImp, 2, 5, 8, 10, 14 CoImp-class, 4 copula, 5, 8, 10, 14 fitcopula, 2, 5, 6, 8, 11 legend, 2 lp, 2, 5, 8, 10, 14 MAR, 6 MAR-class, 7 MCAR, 8 MCAR-class, 10 PerfMeasure, 11 PerfMeasure-class, 13 plot,coimp,missing-method (CoImp-class), 4 15
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