robustCorr {maigesPack} | R Documentation |
This function is intended to calculate robust correlation values between pairs of rows of numerical matrix or between two numerical vectors.
robustCorr(x, y=NULL)
x |
numerical matrix or vector. If a matrix the method calculates
the robust correlations between all pairs of rows. If x is a
vector, y must be specified as another vector of same length
as x and the robust correlation between them is calculate. |
y |
optional numeric vector, must be specified if x is a vector. |
This function calculates a robust correlation value in a procedure
similar to the leave-one-out used for cross-validation of
classification results. The algorithm removes one point at a time and
calculates a usual Pearson correlation value. Them, with a vector
\bold{r}
of correlation values that has the same length as the
columns of x
(or vectors x
and y
), the algorithm
decides by the min
(r) or max
(r), according
with that one that is more distant from the median value.
If x
is a matrix, the method return a list with two square
matrices, the first one containing the robust correlation values
between all pairs of rows from x
and the second containing the
index of the point removed from calculation. If x
is a vector,
y
must be specified and the function return a list with the
robust correlation value between them and the index of the point removed.
Gustavo H. Esteves <gesteves@vision.ime.usp.br>
cor
from package stats.
x <- runif(50, 0, 1) y <- rbeta(50, 1, 2) robustCorr(x, y) z <- matrix(rnorm(100, 0, 1), 4, 25) robustCorr(z)