pchc (version 0.4)

Outliers free data via the reweighted MCD: Outliers free data via the reweighted MCD

Description

Outliers free data via the reweighted MCD.

Usage

rmcd(x, alpha = NULL)

Arguments

x

A numerical matrix with the variables. If you have a data.frame (i.e. categorical data) turn them into a matrix using data.frame.to_matrix.

alpha

A number controlling the size of the subsets over which the determinant is minimized; roughly alpha*n observations are used for computing the determinant. Values between 0.5 and 1 are allowed.

Value

A list including:

poia

A vector with the indices of the vectors that were removed.

x

The outlier free data.

Details

The FEDHC algorithm.

References

Rousseeuw P. J. and Leroy A. M. (1987) Robust Regression and Outlier Detection. Wiley.

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

Pison G., Van Aelst S., and Willems G. (2002) Small Sample Corrections for LTS and MCD, Metrika 55: 111-123.

Hubert M., Rousseeuw P. J. and Verdonck, T. (2012) A deterministic algorithm for robust location and scatter. Journal of Computational and Graphical Statistics 21: 618-637.

Cerioli A. (2010). Multivariate outlier detection with high-breakdown estimators.Journal of the American Statistical Association 105(489): 147-156.

Cerchiello P. and Giudici P. (2016). Big data analysis for financial risk management. Journal of Big Data 3(1): 18.

See Also

fedhc.skel, pchc.skel, mmhc.skel

Examples

# NOT RUN {
x <- matrix( rnorm(200 * 20), nrow = 200 )
x1 <- matrix( rnorm(10 * 20, 10), nrow = 10 )
x <- rbind(x, x1)
a <- pchc::rmcd(x)
a$poia
# }