HoeffD: Matrix of Hoeffding's D Statistics
In AndriSignorell/DescTools: Tools for Descriptive Statistics
HoeffD 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, 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 <f.harrell@vanderbilt.edu>
Department of Biostatistics
Vanderbilt University
References
Hoeffding W. (1948) A non-parametric test of independence. Ann Math Stat 19:546–57.
Hollander M., Wolfe D.A. (1973) Nonparametric Statistical Methods,
pp. 228–235, 423. New York: Wiley.
Press W.H., Flannery B.P., Teukolsky S.A., Vetterling, W.T. (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)
AndriSignorell/DescTools documentation built on Dec. 1, 2024, 5:13 a.m.
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| HoeffD | 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, 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 |
a numeric vector or matrix which will be concatenated to |
... |
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 <f.harrell@vanderbilt.edu>
Department of Biostatistics
Vanderbilt University
References
Hoeffding W. (1948) A non-parametric test of independence. Ann Math Stat 19:546–57.
Hollander M., Wolfe D.A. (1973) Nonparametric Statistical Methods, pp. 228–235, 423. New York: Wiley.
Press W.H., Flannery B.P., Teukolsky S.A., Vetterling, W.T. (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)
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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