Mahalanobis distances from center of indexed points
Computes the vector of (classical or robust)
Mahalanobis distances of all points of x
to the center of the points indexed (or weighted)
by gv. The latter also determine
the covariance matrix.
Thought for use within fixmahal.
mahalanofix(x, n = nrow(as.matrix(x)), p = ncol(as.matrix(x)), gv =
rep(1, times = n), cmax = 1e+10, method = "ml")
mahalanofuz(x, n = nrow(as.matrix(x)), p = ncol(as.matrix(x)),
gv = rep(1, times=n), cmax = 1e+10)x |
a numerical data matrix, rows are points, columns are variables. |
n |
positive integer. Number of points. |
p |
positive integer. Number of variables. |
gv |
for |
cmax |
positive number. used in |
method |
|
solvecov is used to invert the covariance matrix. The methods
"mcd" and "mve" in mahalanofix do not work properly
with point constellations with singular covariance matrices!
A list of the following components:
md |
vector of Mahalanobis distances. |
mg |
mean of the points indexed by |
covg |
covariance matrix of the points indexed by |
covinv |
|
coll |
logical. If |
Methods "mcd" and "mve" require library lqs.
x <- c(1,2,3,4,5,6,7,8,9,10) y <- c(1,2,3,8,7,6,5,8,9,10) mahalanofix(cbind(x,y),gv=c(0,0,0,1,1,1,1,1,0,0)) mahalanofix(cbind(x,y),gv=c(0,0,0,1,1,1,1,0,0,0)) mahalanofix(cbind(x,y),gv=c(0,0,0,1,1,1,1,1,0,0),method="mcd") mahalanofuz(cbind(x,y),gv=c(0,0,0.5,0.5,1,1,1,0.5,0.5,0))
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