lmrob.control {robustbase} | R Documentation |
Tuning parameters for lmrob
, the MM-type regression
estimator and the associated S-, M- and D-estimators. Using
setting="KS2011"
sets the defaults as suggested by Koller and Stahel
(2011).
lmrob.control(setting, seed = NULL, nResample = 500, tuning.chi = NULL, bb = 0.5, tuning.psi = NULL, max.it = 50, groups = 5, n.group = 400, k.fast.s = 1, best.r.s = 2, k.max = 200, refine.tol = 1e-7, rel.tol = 1e-7, trace.lev = 0, compute.rd = FALSE, method = 'MM', psi = c('bisquare', 'lqq', 'welsh', 'optimal', 'hampel', 'ggw'), numpoints = 10, cov = '.vcov.avar1', ...)
setting |
a string specifying alternative default values. Leave
empty for the defaults or use |
seed |
an integer vector, the seed to be used for random
re-sampling used in obtaining candidates for the initial
S-estimator; see |
nResample |
number of re-sampling candidates to be used to find the initial S-estimator. Currently defaults to 500 which works well in most situations (see references). |
tuning.chi |
tuning constant vector for the S-estimator.
Sensible defaults are set according to |
bb |
expected value under the normal model of the
“chi” (rather rho) function with tuning
constant equal to |
tuning.psi |
tuning constant vector for the re-descending M-estimator.
Depending on the value of |
max.it |
integer specifying the maximum number of IRWLS iterations. |
groups |
(for the fast-S algorithm): Number of random subsets to use when the data set is large. |
n.group |
(for the fast-S algorithm): Size of each of the
|
k.fast.s |
(for the fast-S algorithm): Number of local improvement steps (“I-steps”) for each re-sampling candidate. |
best.r.s |
(for the fast-S algorithm): Number of of best candidates to be iterated further (i.e., “refined”); is denoted t in Salibian-Barrera \& Yohai(2006). |
k.max |
(for the fast-S algorithm): maximal number of refinement steps for the “fully” iterated best candidates. |
refine.tol |
(for the fast-S algorithm): relative convergence tolerance for the fully iterated best candidates. |
rel.tol |
(for the RWLS iterations of the MM algorithm): relative convergence tolerance for the parameter vector. |
trace.lev |
integer indicating if the progress of the MM-algorithm
should be traced (increasingly); default |
compute.rd |
logical indicating if robust distances (based on
the MCD robust covariance estimator |
method |
string specifying the estimator-chain. |
psi |
string specifying the type ψ-function
used. See Details of |
numpoints |
Number of points used in Gauss quadrature. |
cov |
Function or string with function name to be used to
calculate covariance matrix estimate. See Details of
|
... |
Further arguments are added to the control list. |
The option setting="KS2011"
alters the default
arguments. They are changed to method = 'SMDM', psi = 'lqq',
max.it = 500, k.max = 2000, cov = '.vcov.w'
. The defaults of all
the remaining arguments are not changed.
By default, tuning.chi
and tuning.psi
are set to
yield an MM-estimate with break-down point 0.5 and efficiency of
95\% at the normal. They are:
psi | tuning.chi | tuning.psi |
bisquare | 1.54764 | 4.685061 |
welsh | 0.5773502 | 2.11 |
ggw | c(-0.5, 1.5, NA, 0.5) |
c(-0.5, 1.5, 0.95, NA) |
lqq | c(-0.5, 1.5, NA, 0.5) |
c(-0.5, 1.5, 0.95, NA) |
optimal | 0.4047 | 1.060158 |
hampel | c(1.5, 3.5, 8)*0.2119163 |
c(1.5, 3.5, 8)*0.9014
|
The values for the tuning constant for the ggw
psi function are
hard coded. The constants vector has four elements: minimal slope, b
(controlling the bend at the maximum of the curve), efficiency,
break-down point. Use NA
for an unspecified value, see examples
in the tables.
The constants for the hampel
psi function are chosen to have a
redescending slope of -1/3. Constants for a slope of -1/2
would be
psi | tuning.chi | tuning.psi |
hampel | c(2, 4, 8)*0.1981319 |
c(2, 4, 8)*0.690794
|
Alternative coefficients for an efficiency of 85\% at the normal are given in the table below.
psi | tuning.psi |
bisquare | 3.443689 |
welsh | 1.456 |
ggw | c(-0.5, 1.5, 0.85, NA) |
optimal | 0.8684 |
hampel (-1/3) | c(1.5, 3.5, 8)*0.5704545 |
hampel (-1/2) | c(2, 4, 8)*0.4769578
|
Matias Salibian-Barrera, Martin Maechler and Manuel Koller
Koller, M. and Stahel, W.A. (2011), Sharpening Wald-type inference in robust regression for small samples, Computational Statistics & Data Analysis 55(8), 2504–2515.
lmrob
, also for references and examples.
## Show the default settings: str(lmrob.control())