Title:	Scalable Robust Estimators with High Breakdown Point for Incomplete Data
Version:	0.5-3
VersionNote:	Released 0.5-2 on 2024-08-17 on CRAN
Description:	Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point for Incomplete Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
Maintainer:	Valentin Todorov <valentin.todorov@chello.at>
Depends:	R (≥ 2.10), rrcov (≥ 1.3.7), robustbase (≥ 0.92.1), methods
Imports:	stats4, lattice, norm, cluster
Suggests:	grid
LazyLoad:	yes
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/valentint/rrcovNA
BugReports:	https://github.com/valentint/rrcovNA/issues
Packaged:	2025-04-21 19:32:44 UTC; valen
NeedsCompilation:	yes
Author:	Valentin Todorov [aut, cre]
Repository:	CRAN
Date/Publication:	2025-04-22 07:30:02 UTC

Class "CovNA" – a base class for estimates of multivariate location and scatter for incomplete data

Description

The class CovNA represents an estimate of the multivariate location and scatter of a data set. The objects of class CovNA contain the classical estimates and serve as base for deriving other estimates, i.e. different types of robust estimates.

Objects from the Class

Objects can be created by calls of the form new("CovNA", ...), but the usual way of creating CovNA objects is a call to the function CovNA which serves as a constructor.

Slots

call:

Object of class "language"

cov:

covariance matrix

center:

location

n.obs:

number of observations used for the computation of the estimates

mah:

mahalanobis distances

det:

determinant

flag:

flags (FALSE if suspected an outlier)

method:

a character string describing the method used to compute the estimate: "Classic"

singularity:

a list with singularity information for the covariance matrix (or NULL of not singular)

X:

data

Extends

Class "Cov-class", directly.

Methods

getDistance

signature(obj = "CovNA"): distances

getFlag

signature(obj = "CovNA"): Flags observations as outliers if the corresponding mahalanobis distance is larger then qchisq(prob, p) where prob defaults to 0.975.

summary

signature(object = "CovNA"): calculate summary information

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

showClass("CovNA")

Classical Estimates of Multivariate Location and Scatter for incomplete data (EM Algorithm)

Description

Computes the classical estimates of multivariate location and scatter. Returns an S4 class CovNAClassic with the estimated center, cov, Mahalanobis distances and weights based on these distances.

Usage

    CovNAClassic(x, unbiased=TRUE)
    CovNA(x, unbiased=TRUE)

Arguments

Value

An object of class "CovNAClassic".

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

Cov-class, CovClassic-class, CovNAClassic-class

Examples

data(bush10)
cv <- CovNAClassic(bush10)
cv
summary(cv)

Class "CovNAClassic" - classical estimates of multivariate location and scatter for incomplete data (EM algorithm)

Description

The class CovNAClassic represents an estimate of the multivariate location and scatter of an incomplete data set. The class CovNAClassic objects contain the classical estimates.

Objects from the Class

Objects can be created by calls of the form new("CovNAClassic", ...), but the usual way of creating CovNAClassic objects is a call to the function CovNAClassic which serves as a constructor.

Slots

call:

Object of class "language"

cov:

covariance matrix

center:

location

n.obs:

number of observations used for the computation of the estimates

mah:

mahalanobis distances

method:

a character string describing the method used to compute the estimate: "Classic"

singularity:

a list with singularity information for the ocvariance matrix (or NULL of not singular)

X:

data

Methods

plot

signature(x = "CovNAClassic"): plot the object

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

data(bush10)
cv <- CovNAClassic(bush10)
cv
summary(cv)

Robust Location and Scatter Estimation via MCD for incomplete data

Description

Computes a robust multivariate location and scatter estimate with a high breakdown point for incomplete data, using the ‘Fast MCD’ (Minimum Covariance Determinant) estimator.

Usage

CovNAMcd(x, alpha = 1/2, nsamp = 500, seed = NULL, trace = FALSE, 
use.correction = TRUE, impMeth = c("norm" , "seq", "rseq"), control)

Arguments

Details

This function computes the minimum covariance determinant estimator of location and scatter and returns an S4 object of class CovMcd-class containing the estimates. The implementation of the function is similar to the existing R function covMcd() which returns an S3 object. The MCD method looks for the h (> n/2) observations (out of n) whose classical covariance matrix has the lowest possible determinant. The raw MCD estimate of location is then the average of these h points, whereas the raw MCD estimate of scatter is their covariance matrix, multiplied by a consistency factor and a finite sample correction factor (to make it consistent at the normal model and unbiased at small samples). Both rescaling factors are returned also in the vector raw.cnp2 of length 2. Based on these raw MCD estimates, a reweighting step is performed which increases the finite-sample efficiency considerably - see Pison et al. (2002). The rescaling factors for the reweighted estimates are returned in the vector cnp2 of length 2. Details for the computation of the finite sample correction factors can be found in Pison et al. (2002). The finite sample corrections can be suppressed by setting use.correction=FALSE. The implementation in rrcov uses the Fast MCD algorithm of Rousseeuw and Van Driessen (1999) to approximate the minimum covariance determinant estimator.

Value

An S4 object of class CovNAMcd which is a subclass of the virtual class CovNARobust.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

V. Todorov, M. Templ and P. Filzmoser. Detection of multivariate outliers in business survey data with incomplete information. Advances in Data Analysis and Classification, 5 37–56, 2011.

P. J. Rousseeuw and K. van Driessen (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

    data(bush10)
    mcd <- CovNAMcd(bush10)
    mcd
    summary(mcd)

    plot(mcd)
    plot(mcd, which="pairs")
    plot(mcd, which="xydistance")
    plot(mcd, which="xyqqchi2")   

MCD Estimates of Multivariate Location and Scatter for incomplete data

Description

This class, derived from the virtual class "CovRobust" accomodates MCD Estimates of multivariate location and scatter computed by the ‘Fast MCD’ algorithm.

Objects from the Class

Objects can be created by calls of the form new("CovMcd", ...), but the usual way of creating CovMcd objects is a call to the function CovMcd which serves as a constructor.

Slots

alpha:

Object of class "numeric" - the size of the subsets over which the determinant is minimized (the default is (n+p+1)/2)

quan:

Object of class "numeric" - the number of observations on which the MCD is based. If quan equals n.obs, the MCD is the classical covariance matrix.

best:

Object of class "Uvector" - the best subset found and used for computing the raw estimates. The size of best is equal to quan

raw.cov:

Object of class "matrix" the raw (not reweighted) estimate of location

raw.center:

Object of class "vector" - the raw (not reweighted) estimate of scatter

raw.mah:

Object of class "Uvector" - mahalanobis distances of the observations based on the raw estimate of the location and scatter

raw.wt:

Object of class "Uvector" - weights of the observations based on the raw estimate of the location and scatter

raw.cnp2:

Object of class "numeric" - a vector of length two containing the consistency correction factor and the finite sample correction factor of the raw estimate of the covariance matrix

cnp2:

Object of class "numeric" - a vector of length two containing the consistency correction factor and the finite sample correction factor of the final estimate of the covariance matrix.

iter, crit, wt:

from the "CovRobust-class" class.

call, cov, center, n.obs, mah, method, singularity, X:

from the "Cov-class" class.

Extends

Class "CovRobust-class", directly. Class "Cov-class", by class "CovRobust-class".

Methods

No methods defined with class "CovMcd" in the signature.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovMcd, Cov-class, CovRobust-class

Examples

showClass("CovNAMcd")

Robust Location and Scatter Estimation - Ortogonalized Gnanadesikan-Kettenring (OGK) for incomplete data

Description

Computes a robust multivariate location and scatter estimate with a high breakdown point for incomplete data, using the pairwise algorithm proposed by Marona and Zamar (2002) which in turn is based on the pairwise robust estimator proposed by Gnanadesikan-Kettenring (1972).

Usage

CovNAOgk(x, niter = 2, beta = 0.9, impMeth = c("norm" , "seq", "rseq"), control)

Arguments

Details

The method proposed by Marona and Zamar (2002) allowes to obtain positive-definite and almost affine equivariant robust scatter matrices starting from any pairwise robust scatter matrix. The default robust estimate of covariance between two random vectors used is the one proposed by Gnanadesikan and Kettenring (1972) but the user can choose any other method by redefining the function in slot vrob of the control object CovControlOgk. Similarly, the function for computing the robust univariate location and dispersion used is the tau scale defined in Yohai and Zamar (1998) but it can be redefined in the control object.

The estimates obtained by the OGK method, similarly as in CovMcd are returned as 'raw' estimates. To improve the estimates a reweighting step is performed using the coverage parameter beta and these reweighted estimates are returned as 'final' estimates.

Value

An S4 object of class CovNAOgk which is a subclass of the virtual class CovNARobust.

Note

If the user does not specify a scale and covariance function to be used in the computations or specifies one by using the arguments smrob and svrob (i.e. the names of the functions as strings), a native code written in C will be called which is by far faster than the R version.

If the arguments mrob and vrob are not NULL, the specified functions will be used via the pure R implementation of the algorithm. This could be quite slow.

See CovControlOgk for details.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Yohai, R.A. and Zamar, R.H. (1998) High breakdown point estimates of regression by means of the minimization of efficient scale JASA 86, 403–413.

Gnanadesikan, R. and John R. Kettenring (1972) Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28, 81–124.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovNAMcd

Examples

data(bush10)
CovNAOgk(bush10)

## the following three statements are equivalent
c1 <- CovNAOgk(bush10, niter=1)
c2 <- CovNAOgk(bush10, control = CovControlOgk(niter=1))

## direct specification overrides control one:
c3 <- CovNAOgk(bush10, beta=0.95,
             control = CovControlOgk(beta=0.99))
c1

OGK Estimates of Multivariate Location and Scatter for incomplete data

Description

This class, derived from the virtual class "CovRobust" accomodates OGK Estimates of multivariate location and scatter computed by the algorithm proposed by Marona and Zamar (2002).

Objects from the Class

Objects can be created by calls of the form new("CovOgk", ...), but the usual way of creating CovOgk objects is a call to the function CovOgk which serves as a constructor.

Slots

raw.cov:

Object of class "matrix" the raw (not reweighted) estimate of covariance matrix

raw.center:

Object of class "vector" - the raw (not reweighted) estimate of the location vector

raw.mah:

Object of class "Uvector" - mahalanobis distances of the observations based on the raw estimate of the location and scatter

raw.wt:

Object of class "Uvector" - weights of the observations based on the raw estimate of the location and scatter

iter, crit, wt:

from the "CovRobust-class" class.

call, cov, center, n.obs, mah, method, singularity, X:

from the "Cov-class" class.

Extends

Class "CovRobust-class", directly. Class "Cov-class", by class "CovRobust-class".

Methods

No methods defined with class "CovNAOgk" in the signature.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovMcd-class, CovMest-class

Examples

showClass("CovNAOgk")

Robust Location and Scatter Estimation for incomplete data

Description

Computes a robust multivariate location and scatter estimate with a high breakdown point for incomplete data, using one of the available estimators.

Usage

CovNARobust(x, control, impMeth=c("norm" , "seq", "rseq"))

Arguments

Details

This function is based on imputation and than estimation with a selected high breakdown point method. Thus first imputation with the selected method will be performed and then the function CovRobustwill be called. For details see CovRobust.

Value

An object derived from a CovRobust object, depending on the selected estimator.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

V. Todorov, M. Templ and P. Filzmoser. Detection of multivariate outliers in business survey data with incomplete information. Advances in Data Analysis and Classification, 5 37–56, 2011.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

data(bush10)
CovNARobust(bush10)
CovNARobust(bush10, CovControlSest())

Class "CovNARobust" - virtual base class for robust estimates of multivariate location and scatter for incomplete data

Description

CovNARobust is a virtual base class used for deriving the concrete classes representing different robust estimates of multivariate location and scatter for incomplete data. Here are implemeted the standard methods common for all robust estimates like show, summary and plot. The derived classes can override these methods and can define new ones.

Objects from the Class

A virtual Class: No objects may be created from it.

Slots

iter:

number of iterations used to compute the estimates

crit:

value of the criterion function

wt:

weights

call, cov, center, n.obs, mah, method, singularity, X:

from the "Cov-class" class.

Extends

Classes "CovNA" and "CovRobust-class", directly.

Methods

plot

signature(x = "CovNARobust"): plot the object

summary

signature(object = "CovNARobust"): display additional information for the object

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovNA, CovNAMcd, CovNAOgk, CovNASde , CovNASest

Examples

     data(hbk)
     hbk.x <- data.matrix(hbk[, 1:3])
     cv <- CovMest(hbk.x)               # it is not possible to create an object of
                                        # class CovRobust, since it is a VIRTUAL class
     cv
     summary(cv)                        # summary method for class CovRobust
     plot(cv)                           # plot method for class CovRobust

Stahel-Donoho Estimates of Multivariate Location and Scatter for incomplete data

Description

Compute a robust estimate of location and scale using the Stahel-Donoho projection based estimator

Usage

CovNASde(x, nsamp, maxres, tune = 0.95, eps = 0.5, prob = 0.99, 
impMeth = c("norm" , "seq", "rseq"), seed = NULL, trace = FALSE, control)

Arguments

Value

An S4 object of class CovNASde which is a subclass of the virtual class CovNARobust.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

R. A. Maronna and V.J. Yohai (1995) The Behavior of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association 90 (429), 330–341.

R. A. Maronna, D. Martin and V. Yohai (2006). Robust Statistics: Theory and Methods. Wiley, New York.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

data(bush10)
CovNASde(bush10)

## the following four statements are equivalent
c0 <- CovNASde(bush10)
c1 <- CovNASde(bush10, nsamp=2000)
c2 <- CovNASde(bush10, control = CovControlSde(nsamp=2000))
c3 <- CovNASde(bush10, control = new("CovControlSde", nsamp=2000))

## direct specification overrides control one:
c4 <- CovNASde(bush10, nsamp=100,
             control = CovControlSde(nsamp=2000))
c1
summary(c1)

Stahel-Donoho Estimates of Multivariate Location and Scatter for incomplete data

Description

This class, derived from the virtual class "CovRobust" accomodates Stahel-Donoho estimates of multivariate location and scatter.

Objects from the Class

Objects can be created by calls of the form new("CovSde", ...), but the usual way of creating CovSde objects is a call to the function CovSde which serves as a constructor.

Slots

iter, crit, wt:

from the "CovRobust-class" class.

call, cov, center, n.obs, mah, method, singularity, X:

from the "Cov-class" class.

Extends

Class "CovRobust-class", directly. Class "Cov-class", by class "CovRobust-class".

Methods

No methods defined with class "CovNASde" in the signature.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovSde, Cov-class, CovRobust-class

Examples

showClass("CovNASde")

S Estimates of Multivariate Location and Scatter for incomplete data

Description

Computes S-Estimates of multivariate location and scatter based on Tukey's biweight function for incomplete data using a fast algorithm similar to the one proposed by Salibian-Barrera and Yohai (2006) for the case of regression. Alternativley, the Ruppert's SURREAL algorithm, bisquare or Rocke type estimation can be used.

Usage

    CovNASest(x, bdp = 0.5, arp = 0.1, eps = 1e-5, maxiter = 120,
        nsamp = 500, impMeth = c("norm" , "seq", "rseq"), seed = NULL, 
        trace = FALSE, tolSolve = 1e-13,
        scalefn,
        method = c("sfast", "surreal", "bisquare", "rocke", "suser", "sdet"), control,
        t0, S0, initcontrol)

Arguments

Details

Computes biweight multivariate S-estimator of location and scatter. The computation will be performed by one of the following algorithms:

FAST-S

An algorithm similar to the one proposed by Salibian-Barrera and Yohai (2006) for the case of regression

SURREAL

Ruppert's SURREAL algorithm when method is set to 'surreal'

BISQUARE

Bisquare S-Estimate with method set to 'bisquare'

ROCKE

Rocke type S-Estimate with method set to 'rocke'

.

Value

An S4 object of class CovNASest which is a subclass of the virtual class CovNARobust.

Author(s)

Valentin Todorov valentin.todorov@chello.at, Matias Salibian-Barrera matias@stat.ubc.ca and Victor Yohai vyohai@dm.uba.ar. See also the code from Kristel Joossens, K.U. Leuven, Belgium and Ella Roelant, Ghent University, Belgium.

References

M. Salibian-Barrera and V. Yohai (2006) A fast algorithm for S-regression estimates, Journal of Computational and Graphical Statistics, 15, 414–427.

R. A. Maronna, D. Martin and V. Yohai (2006). Robust Statistics: Theory and Methods. Wiley, New York.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

library(rrcov)
data(bush10)
CovNASest(bush10)

## the following four statements are equivalent
c0 <- CovNASest(bush10)
c1 <- CovNASest(bush10, bdp = 0.25)
c2 <- CovNASest(bush10, control = CovControlSest(bdp = 0.25))
c3 <- CovNASest(bush10, control = new("CovControlSest", bdp = 0.25))

## direct specification overrides control one:
c4 <- CovNASest(bush10, bdp = 0.40,
             control = CovControlSest(bdp = 0.25))
c1
summary(c1)

## Use the SURREAL algorithm of Ruppert
cr <- CovNASest(bush10, method="surreal")
cr

## Use Bisquare estimation
cr <- CovNASest(bush10, method="bisquare")
cr

## Use Rocke type estimation
cr <- CovNASest(bush10, method="rocke")
cr

S Estimates of Multivariate Location and Scatter for incomplete data

Description

This class, derived from the virtual class "CovRobust" accomodates S Estimates of multivariate location and scatter computed by the ‘Fast S’ or ‘SURREAL’ algorithm.

Objects from the Class

Objects can be created by calls of the form new("CovSest", ...), but the usual way of creating CovSest objects is a call to the function CovSest which serves as a constructor.

Slots

iter, crit, wt:

from the "CovRobust-class" class.

call, cov, center, n.obs, mah, method, singularity, X:

from the "Cov-class" class.

Extends

Class "CovRobust-class", directly. Class "Cov-class", by class "CovRobust-class".

Methods

No methods defined with class "CovNASest" in the signature.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovSest, Cov-class, CovRobust-class

Examples

showClass("CovNASest")

Classical or robust Principal Components for incomplete data

Description

Computes classical and robust principal components for incomplete data using an EM algorithm as descibed by Serneels and Verdonck (2008)

Usage

PcaNA(x, ...)
## Default S3 method:
PcaNA(x, k = ncol(x), kmax = ncol(x), conv=1e-10, maxiter=100, 
    method=c("cov", "locantore", "hubert", "grid", "proj", "class"), cov.control=NULL,
    scale = FALSE, signflip = TRUE, crit.pca.distances = 0.975, trace=FALSE, ...)
## S3 method for class 'formula'
PcaNA(formula, data = NULL, subset, na.action, ...)

Arguments

Details

PcaNA, serving as a constructor for objects of class PcaNA is a generic function with "formula" and "default" methods. For details see the relevant references.

Value

An S4 object of class PcaNA which is a subclass of the virtual class Pca-class.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Serneels S & Verdonck T (2008), Principal component analysis for data containing outliers and missing elements. Computational Statistics and Data Analisys, 52(3), 1712–1727 .

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

Examples

## 1. With complete data
## PCA of the bushfire data
    data(bushfire)
    pca <- PcaNA(bushfire)
    pca

## Compare with the classical PCA
    prcomp(bushfire)

## or  
    PcaNA(bushfire, method="class")
    
## If you want to print the scores too, use
    print(pca, print.x=TRUE)

## Using the formula interface
    PcaNA(~., data=bushfire)

## To plot the results:

    plot(pca)                    # distance plot
    pca2 <- PcaNA(bushfire, k=2)  
    plot(pca2)                   # PCA diagnostic plot (or outlier map)
    
## Use the standard plots available for for prcomp and princomp
    screeplot(pca)    
    biplot(pca)  

################################################################      
## 2. Now the same wit incomplete data - bush10
    data(bush10)
    pca <- PcaNA(bush10)
    pca

## Compare with the classical PCA
    PcaNA(bush10, method="class")
    
## If you want to print the scores too, use
    print(pca, print.x=TRUE)

## Using the formula interface
    PcaNA(~., data=as.data.frame(bush10))

## To plot the results:

    plot(pca)                    # distance plot
    pca2 <- PcaNA(bush10, k=2)  
    plot(pca2)                   # PCA diagnostic plot (or outlier map)
    
## Use the standard plots available for for prcomp and princomp
    screeplot(pca)    
    biplot(pca)    
    

Class "PcaNA" Principal Components for incomplete data

Description

Contains the results of the computations of classical and robust principal components for incomplete data using an EM algorithm as descibed by Serneels and Verdonck (2008)

Objects from the Class

Objects can be created by calls of the form new("PcaNA", ...) but the usual way of creating PcaNA objects is a call to the function PcaNA which serves as a constructor.

Slots

call, center, scale, loadings, eigenvalues, scores, k, sd, od, cutoff.sd, cutoff.od, flag, n.obs:

from the "Pca-class" class.

Ximp:

the data matrix with imputed missing values

Extends

Class "Pca-class", directly.

Methods

getQuan

signature(obj = "PcaNA"): ...

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Serneels S & Verdonck T (2008), Principal component analysis for data containing outliers and missing elements. Computational Statistics and Data Analisys, 52(3), 1712–1727 .

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

PcaRobust-class, Pca-class, PcaClassic, PcaClassic-class

Examples

showClass("PcaNA")

Class "SummaryCovNA" - summary of "CovNA" objects

Description

The "CovNA" object plus some additional summary information

Objects from the Class

Objects can be created by calls of the form new("SummaryCovNA", ...), but most often by invoking 'summary' on a "CovNA" object. They contain values meant for printing by 'show'.

Slots

No Slots defined with class "SummaryCovNA" in the signature.

Methods

No Methods defined with class "SummaryCovNA" in the signature.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovNA

Examples

showClass("SummaryCovNA")

Class "SummaryCovNARobust" - summary of "CovNARobust" objects

Description

Summary information for CovRobust objects meants for printing by 'show'

Objects from the Class

Objects can be created by calls of the form new("SummaryCovNARobust", ...), but most often by invoking 'summary' on an "CovNA" object. They contain values meant for printing by 'show'.

Slots

No Slots defined with class "SummaryCovNARobust" in the signature.

Extends

Class "SummaryCovNA", directly.

Methods

show

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

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. <doi:10.18637/jss.v032.i03>.

See Also

CovRobust-class, SummaryCov-class

Examples

data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
cv <- CovMest(hbk.x)
cv
summary(cv)

Campbell Bushfire Data with added missing data items (10 percent)

Description

This data set is based on the bushfire data set which was used by Campbell (1984) to locate bushfire scars - see bushfire in package robustbase. The original dataset contains satelite measurements on five frequency bands, corresponding to each of 38 pixels. The data set is very well studied (Maronna and Yohai, 1995; Maronna and Zamar, 2002). There are 12 clear outliers: 33-38, 32, 7-11 and 12 and 13 are suspect.

Usage

data(bush10)

Format

A data frame with 38 observations on 6 variables.

The original data set consists of 38 observations in 5 variables. Based on it four new data sets are created in which some of the data items are replaced by missing values with a simple "missing completely at random " mechanism. For this purpose independent Bernoulli trials are realized for each data item with a probability of success 0.1 where success means that the corresponding item is set to missing.)

Source

Maronna, R.A. and Yohai, V.J. (1995) The Behavoiur of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association 90, 330–341.

Beguin, C. and Hulliger, B. (2004) Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 127, 2, 275–294.

Examples

## The following code will result in exactly the same output
##  as the one obtained from the original data set
data(bush10)
plot(bush10)
CovNAMcd(bush10)


## Not run: 
##  This is the code with which the missing data were created:
##  Creates a data set with missing values (for testing purposes)
##  from a complete data set 'x'. The probability of
##  each item being missing is 'pr'.
##
getmiss <- function(x, pr=0.1){
    library(Rlab)
    n <- nrow(x)
    p <- ncol(x)
    bt <- rbern(n*p, pr)
    btmat <- matrix(bt, nrow=n)
    btmiss <- ifelse(btmat==1, NA, 0)
    x+btmiss
}

## End(Not run)

Consumer Expenditure Survey Data

Description

This data set has been derived from the Quarterly Interview Survey of the Consumer Expenditure Survey (CES) undertaken by the U.S. Department of Labor, Bureau of Labor Statistics and is available at https://www.bls.gov/cex/ where also more details about this survey can be found. The original data set comprises 869 households in 34 variables of which one is unique ID, five characterize the size of the household, further 6 variables contain other characteristics of the household like age, education ethnicity, etc. and 22 variables represent the household expenditures. We will consider a reduced set of only 8 expendature variables. This reduced data set was analyzed by Hubert at al. (2009)in the context of PCA and the first step of the analysis showed that all variables are highly skewed. They applied the robust PCA method of Serneels and Verdonck based on the EM algorithm, since some of the data are incomplete.

Usage

data(ces)

Format

A data frame with 869 observations on the following 8 variables:

EXP

Total household expenditure

FDHO

Food and nonalcoholic beverages consumed at home

FDAW

Food and nonalcoholic beverages consumed away from home

SHEL

Housing expenditure

TELE

Telephone services

CLOT

Clothing

HEAL

Health care

ENT

Entertainment

Source

https://www.bls.gov/cex/

References

Hubert, M, Rousseeuw, P.J. and Verdonck, T., (2009). Robust PCA for skewed data and its outlier map, Computational Statistics & Data Analysis, 53, 6, pp. 2264-2274

Examples

data(ces)
summary(ces)
plot(ces)

Impute missing multivariate normal data

Description

Draws missing elements of a data matrix under the multivariate normal model and a user-supplied parameter

Usage

impNorm(x)

Arguments

Details

This function simply uses imp.norm from package norm.

Value

a matrix of the same form as x, but with all missing values filled in with simulated values drawn from their predictive distribution given the observed data and the specified parameter.

References

See Section 5.4.1 of Schafer (1996).

See Also

prelim.norm, makeparam.norm, and rngseed.

Examples

data(bush10)
impNorm(bush10) #impute missing data under the MLE

Sequential imputation of missing values

Description

Impute missing multivariate data using sequential algorithm

Usage

impSeq(x, norm_impute=FALSE, check_data=FALSE, verbose=TRUE)

Arguments

Details

SEQimpute starts from a complete subset of the data set Xc and estimates sequentially the missing values in an incomplete observation, say x*, by minimizing the determinant of the covariance of the augmented data matrix X* = [Xc; x']. Then the observation x* is added to the complete data matrix and the algorithm continues with the next observation with missing values.

Value

A list containing the following elements:

References

S. Verboven, K. Vanden Branden and P. Goos (2007). Sequential imputation for missing values. Computational Biology and Chemistry, 31, 320–327.

Examples

    data(bush10)
    impSeq(bush10) # impute squentially missing data

Robust sequential imputation of missing values

Description

Impute missing multivariate data using robust sequential algorithm

Usage

impSeqRob(x, alpha=0.9, norm_impute=FALSE, check_data=FALSE, verbose=TRUE)

Arguments

Details

The nonrobust version SEQimpute starts from a complete subset of the data set Xc and estimates sequentially the missing values in an incomplete observation, say x*, by minimizing the determinant of the covariance of the augmented data matrix X* = [Xc; x']. Then the observation x* is added to the complete data matrix and the algorithm continues with the next observation with missing values. Since SEQimpute uses the sample mean and covariance matrix it will be vulnerable to the influence of outliers and it is improved by plugging in robust estimators of location and scatter. One possible solution is to use the outlyingness measure as proposed by Stahel (1981) and Donoho (1982) and successfully used for outlier identification in Hubert et al. (2005). We can compute the outlyingness measure for the complete observations only but once an incomplete observation is imputed (sequentially) we could compute the outlyingness measure for it too and use it to decide if this observation is an outlier or not. If the outlyingness measure does not exceed a predefined threshold the observation is included in the further steps of the algorithm.

Value

A list containing the following elements:

References

S. Verboven, K. Vanden Branden and P. Goos (2007). Sequential imputation for missing values. Computational Biology and Chemistry, 31, 320–327.

K. Vanden Branden and S. Verboven (2009). Robust Data Imputation. Computational Biology and Chemistry, 33, 7–13.

Examples

    data(bush10)
    impSeqRob(bush10) # impute squentially missing data