mv.1sample.est | R Documentation |
Estimates the multivariate location for different score functions and their asymptotic covariance matrices in the one sample case.
mv.1sample.est(X, score = "identity", stand = "outer", maxiter = 100,
eps = 1e-06, na.action = na.fail, ... )
X |
a numeric data frame or matrix. |
score |
the score to be used. Possible choices are |
stand |
the standardization method used. Possible choices are |
maxiter |
maximum number of iterations. Used only for |
eps |
convergence tolerance. Used only for |
... |
arguments that can be passed on to functions used for the estimation of location. |
na.action |
a function which indicates what should happen when the data contain 'NA's. Default is to fail. |
For identity scores the location estimate is the regular mean vector. For the spatial sign score it is the spatial median in the outer
standardization case and the Hettmansperger-Randles estimate in the inner standardization case. The rank estimate is the spatial Hodges-Lehmann estimator,
either regular (stand = "outer"
) or affine equivariant (stand = "inner"
).
Computation with outer standardization is faster than with inner standardization and especially the rank version might be slow and memory consuming.
For further details see chapters 3, 5, 6, 7 and 8 of the MNM book.
A list with class 'mvloc' containing the following components:
location |
the location estimate as a vector. |
vcov |
the asymptotic covariance matrix of the location estimate. |
est.name |
name of the location estimate. |
dname |
name of the data set. |
Klaus Nordhausen
Oja, H. (2010), Multivariate Nonparametric Methods with R, Springer.
Nordhausen, K. and Oja, H. (2011), Multivariate L1 Methods: The Package MNM, Journal of Statistical Software, 43, 1-28.
spatial.sign
, spatial.signrank
, spatial.median
, HR.Mest
set.seed(1)
X <- rmvt(100, diag(c(1, 2, 0.5)), 3)
est.Hot.X <- mv.1sample.est(X)
est.SS.o.X <- mv.1sample.est(X,"s")
est.SS.i.X <- mv.1sample.est(X,"s","i")
est.SR.o.X <- mv.1sample.est(X,"r")
est.SR.i.X <- mv.1sample.est(X,"r","i")
est.SR.o.X
summary(est.SR.o.X)
# plotting
plot(est.Hot.X, est.SS.i.X, est.SR.i.X, X)
# or
plot(est.Hot.X, est.SS.i.X, est.SR.i.X)
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