Deterministic and Robust Algorithms for Data Analysis.

Description

This packages contains various robust and deterministic algorithms for data analysis.

Details

Index:

DetMCD                  Robust and deterministic estimation of location 
                        and scatter.
DetMCD-package           Robust and Deterministic Algothms for Data 
                        Analysis
plot.DetMCD          Diagnostic plots for DetMCD
DetMCD_CS               Internal function for DetMCD
DetMCD_RW               Internal function for DetMCD
DetMCD_SP               Internal function for DetMCD
xtractR_M               Internal function for DetMCD
quanff                  Internal function for DetMCD

Author(s)

Kaveh Vakili [aut, cre],

Maintainer: Kaveh Vakili <vakili.kaveh.email@gmail.com>

Robust and Deterministic Location and Scatter Estimation via DetMCD

Description

Computes a robust and deterministic multivariate location and scatter estimate with a high breakdown point, using the DetMCD (Deterministic Minimum Covariance Determinant) algorithm.

Usage

  DetMCD(X,h=NULL,alpha=0.75,scale_est="Auto",tol=1e-07)

Arguments

Details

DetMCD computes the MCD estimator of a multivariate data set in a deterministic way. This estimator is given by the subset of h observations with smallest covariance determinant. The MCD location estimate is then the mean of those h points, and the MCD scatter estimate is their covariance matrix. The default value of h is roughly 0.75n (where n is the total number of observations), but the user may choose each value between n/2 and n. Based on the raw estimates, weights are assigned to the observations such that outliers get zero weight. The reweighted MCD estimator is then given by the mean and covariance matrix of the cases with non-zero weight.

To compute the MCD estimator, six initial robust h-subsets are constructed based on robust transformations of variables or robust and fast-to-compute estimators of multivariate location and shape. Then C-steps are applied on these h-subsets until convergence. Note that the resulting algorithm is not fully affine equivariant, but it is often faster than the FAST-MCD algorithm which is affine equivariant. Note that this function can not handle exact fit situations: if the raw covariance matrix is singular, the program is stopped. In that case, it is recommended to apply the FastMCD function.

The MCD method is intended for continuous variables, and assumes that the number of observations n is at least 5 times the number of variables p. If p is too large relative to n, it would be better to first reduce p by variable selection or robust principal components (see the functions PcaHubert).

Value

A list with components:

Author(s)

Vakili Kaveh (includes section of the help file from the LIBRA implementation).

References

Hubert, M., Rousseeuw, P.J. and Verdonck, T. (2012), "A deterministic algorithm for robust location and scatter", Journal of Computational and Graphical Statistics, Volume 21, Number 3, Pages 618–637.

Verboven, S., Hubert, M. (2010). Matlab library LIBRA, Wiley Interdisciplinary Reviews: Computational Statistics, 2, 509–515.

Examples

## generate data
set.seed(1234)  # for reproducibility
alpha<-0.5
n<-101
p<-5
#generate correlated data
D<-diag(rchisq(p,df=1))
W<-matrix(0.9,p,p)
diag(W)<-1
W<-D
V<-chol(W)
x<-matrix(rnorm(n*p),nc=p)
x<-scale(x)


result<-DetMCD(x,scale_est="tau",alpha=alpha)
plot(result, which = "dd")

#compare to robustbase:
result<-DetMCD(x,scale_est="qn",alpha=alpha)
resultsRR<-covMcd(x,nsamp='deterministic',scalefn=qn,alpha=alpha)
#should be the same:
result$crit
resultsRR$crit


#Example with several values of alpha:
alphas<-seq(0.5,1,l=6)
results<-DetMCD(x,scale_est="qn",alpha=alphas)
plot(results, h.val = 2, which = "dd")

DetMCD_CS

Description

Internal function. Computes the Csteps for the DetMCD algorithm.

Usage

  DetMCD_CS(Data,scale_est,h,out1)

Arguments

Value

returns a list.

Author(s)

Vakili Kaveh

See Also

DetMCD,DetMCD_SP.

DetMCD_RW

Description

Internal function. Carries the re-weighting part of the DetMCD algorithm.

Usage

  DetMCD_RW(ll,hlst,Xw,out2,scale_est,alpha)

Arguments

Value

returns a list.

Author(s)

Vakili Kaveh

See Also

DetMCD,DetMCD_CS.

DetMCD_SP

Description

Internal function. Computes the starting points for the DetMCD algorithm.

Usage

  DetMCD_SP(Data,scale_est,tol)

Arguments

Value

returns a list.

Author(s)

Vakili Kaveh

See Also

DetMCD.

Test function for the qn

Description

Test function for the qn used in DetR.

Usage

  inQn(x)

Arguments

Value

the value of the qn estimator of scale.

Author(s)

Kaveh Vakili

References

see pcaPP::qn and citation("pcaPP").

Examples

set.seed(123)	#for reproductibility
x<-rnorm(101)
inQn(x)
#should be the same:
pcaPP::qn(x)

Robust Diagnostic Plots For DetMCD

Description

Shows the Mahalanobis distances based on robust and classical estimates of the location and the covariance matrix in different plots. The following plots are available:

• index plot of the robust and mahalanobis distances

• distance-distance plot

• Chisquare QQ-plot of the robust and mahalanobis distances

• plot of the tolerance ellipses (robust and classic)

• Scree plot - Eigenvalues comparison plot

This function is a minimally modified adaptation of "robustbase::covPlot". See citation("robustbase").

Usage

## S3 method for class 'DetMCD'
plot(x,h.val=1,
     which = c("all", "dd", "distance", "qqchi2",
               "tolEllipsePlot", "screeplot"),
     classic = FALSE, ask = (which == "all" && dev.interactive()),
     cutoff = NULL, id.n, labels.id = rownames(x), cex.id = 0.75,
     label.pos = c(4,2), tol = 1e-07, ...)

Arguments

.

.

Details

These functions produce several plots based on the robust and classical location and covariance matrix. Which of them to select is specified by the attribute which. The plot method for "mcd" objects is calling covPlot() directly, whereas covPlot() should also be useful for plotting other (robust) covariance estimates. The possible options are:

distance

index plot of the robust distances

dd

distance-distance plot

qqchi2

a qq-plot of the robust distances versus the quantiles of the chi-squared distribution

tolEllipsePlot

a tolerance ellipse plot, via tolEllipsePlot

screeplot

an eigenvalues comparison plot - screeplot

The Distance-Distance Plot, introduced by Rousseeuw and van Zomeren (1990), displays the robust distances versus the classical Mahalanobis distances. The dashed line is the set of points where the robust distance is equal to the classical distance. The horizontal and vertical lines are drawn at values equal to the cutoff which defaults to square root of the 97.5% quantile of a chi-squared distribution with p degrees of freedom. Points beyond these lines can be considered outliers.

References

P. J. Rousseeuw and van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association 85, 633–639.

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

See Also

DetMCD

Examples

data(Animals, package ="MASS")
brain <- Animals[c(1:24, 26:25, 27:28),]
detmcd <- DetMCD(log(brain))

plot(detmcd, which = "distance", classic = TRUE)# 2 plots
plot(detmcd, which = "dd")
plot(detmcd, which = "tolEllipsePlot", classic = TRUE)
op <- par(mfrow = c(2,3))
plot(detmcd)## -> which = "all" (5 plots)
par(op)

quanff

Description

Internal function. Converts alpha values to h values.

Usage

  quanff(alpha,n,p)

Arguments

Value

returns an integer.

Author(s)

Vakili Kaveh

References

Hubert, M., Rousseeuw, P.J. and Verdonck, T. (2012), "A deterministic algorithm for robust location and scatter", Journal of Computational and Graphical Statistics, in press.

Examples

quanff(0.75,n=100,p=5);

xtractR_M

Description

Internal function. Formats the output for the DetMCD algorithm.

Usage

  xtractR_M(out2,X)

Arguments

Value

returns a list.

Author(s)

Vakili Kaveh

See Also

DetMCD,DetMCD_RW.