corDist: Correlation Distance Matrix Computation

Description Usage Arguments Details Value Note Author(s) References Examples

Description

The function computes and returns the correlation and absolute correlation distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.

Usage

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corDist(x, method = "pearson", diag = FALSE, upper = FALSE, abs = FALSE,
        use = "pairwise.complete.obs", ...)

Arguments

x

a numeric matrix or data frame

method

the correlation distance measure to be used. This must be one of "pearson", "spearman", "kandall", "cosine", "mcd" or "ogk", respectively. Any unambiguous substring can be given.

diag

logical value indicating whether the diagonal of the distance matrix should be printed by 'print.dist'.

upper

logical value indicating whether the upper triangle of the distance matrix should be printed by 'print.dist'.

abs

logical, compute absolute correlation distances

use

character, correponds to argument use of function cor

...

further arguments to functions covMcd or covOGK, respectively.

Details

The function computes the Pearson, Spearman, Kendall or Cosine sample correlation and absolute correlation; confer Section 12.2.2 of Gentleman et al (2005). For more details about the arguments we refer to functions dist and cor. Moreover, the function computes the minimum covariance determinant or the orthogonalized Gnanadesikan-Kettenring estimator. For more details we refer to functions covMcd and covOGK, respectively.

Value

corDist returns an object of class "dist"; cf. dist.

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Gentleman R. Ding B., Dudoit S. and Ibrahim J. (2005). Distance Measures in DNA Microarray Data Analysis. In: Gentleman R., Carey V.J., Huber W., Irizarry R.A. and Dudoit S. (editors) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer.

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

P. J. Rousseeuw and K. van Driessen (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.

Maronna, R.A. and Zamar, R.H. (2002). Robust estimates of location and dispersion of high-dimensional datasets; Technometrics 44(4), 307-317.

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

Examples

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## only a dummy example
M <- matrix(rnorm(1000), ncol = 20)
D <- corDist(M)

Example output



MKmisc documentation built on Aug. 8, 2021, 5:06 p.m.