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.
corDist(x, method = "pearson", diag = FALSE, upper = FALSE, abs = FALSE,
use = "pairwise.complete.obs", ...)
corDist
returns an object of class "dist"
; cf. dist
.
a numeric matrix or data frame
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.
logical value indicating whether the diagonal of the distance matrix should be printed by 'print.dist'.
logical value indicating whether the upper triangle of the distance matrix should be printed by 'print.dist'.
logical, compute absolute correlation distances
character, correponds to argument use
of function
cor
further arguments to functions covMcd
or covOGK
, respectively.
Matthias Kohl Matthias.Kohl@stamats.de
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.
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.
## only a dummy example
M <- matrix(rnorm(1000), ncol = 20)
D <- corDist(M)
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