hoeffd {Hmisc} | R Documentation

## Matrix of Hoeffding's D Statistics

### Description

Computes a matrix of Hoeffding's (1948) `D` statistics for all possible pairs of columns of a matrix. `D` is a measure of the distance between `F(x,y)` and `G(x)H(y)`, where `F(x,y)` is the joint CDF of `X` and `Y`, and `G` and `H` are marginal CDFs. Missing values are deleted in pairs rather than deleting all rows of `x` having any missing variables. The `D` statistic is robust against a wide variety of alternatives to independence, such as non-monotonic relationships. The larger the value of `D`, the more dependent are `X` and `Y` (for many types of dependencies). `D` used here is 30 times Hoeffding's original `D`, and ranges from -0.5 to 1.0 if there are no ties in the data. `print.hoeffd` prints the information derived by `hoeffd`. The higher the value of `D`, the more dependent are `x` and `y`.

### Usage

    hoeffd(x)
    hoeffd(x, y)
    ## S3 method for class 'hoeffd':
    print(x, ...)

### Arguments

`x` | a numeric matrix with at least 5 rows and at least 2 columns (if `y` is absent), or an object created by `hoeffd`
`y` | a numeric vector or matrix which will be concatenated to `x`
`...` | ignored

### Details

Uses midranks in case of ties, as described by Hollander and Wolfe. P-values are approximated by linear interpolation on the table in Hollander and Wolfe, which uses the asymptotically equivalent Blum-Kiefer-Rosenblatt statistic. For `P<.0001` or `>0.5`, `P` values are computed using a well-fitting linear regression function in `log P` vs. the test statistic. Ranks (but not bivariate ranks) are computed using efficient algorithms (see reference 3).

### Value

a list with elements `D`, the matrix of D statistics, `n` the matrix of number of observations used in analyzing each pair of variables, and `P`, the asymptotic P-values. Pairs with fewer than 5 non-missing values have the D statistic set to NA. The diagonals of `n` are the number of non-NAs for the single variable corresponding to that row and column.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu

### References

Hoeffding W. (1948): A non-parametric test of independence. Ann Math Stat 19:546–57.

Hollander M. and Wolfe D.A. (1973). Nonparametric Statistical Methods, pp. 228–235, 423. New York: Wiley.

Press WH, Flannery BP, Teukolsky SA, Vetterling, WT (1988): Numerical Recipes in C. Cambridge: Cambridge University Press.

### See Also

`rcorr`, `varclus`

### Examples

    x <- c(-2, -1, 0, 1, 2)
    y <- c(4,   1, 0, 1, 4)
    z <- c(1,   2, 3, 4, NA)
    q <- c(1,   2, 3, 4, 5)
    hoeffd(cbind(x,y,z,q))

    # Hoeffding's test can detect even one-to-many dependency
    set.seed(1)
    x <- seq(-10,10,length=200)
    y <- x*sign(runif(200,-1,1))
    plot(x,y)
    hoeffd(x,y)

[Package Contents]