lmrobdet.control {RobStatTM} | R Documentation |
Tuning parameters for lmrobdetMM and lmrobdetDCML
Description
This function sets tuning parameters for the MM estimator implemented in lmrobdetMM
and
the Distance Constrained Maximum Likelihood regression estimators
computed by lmrobdetDCML
.
Usage
lmrobdet.control(
bb = 0.5,
efficiency = 0.95,
family = "mopt",
tuning.psi,
tuning.chi,
compute.rd = FALSE,
corr.b = TRUE,
split.type = "f",
initial = "S",
max.it = 100,
refine.tol = 1e-07,
rel.tol = 1e-07,
refine.S.py = 1e-07,
refine.PY = 10,
solve.tol = 1e-07,
trace.lev = 0,
psc_keep = 0.5,
resid_keep_method = "threshold",
resid_keep_thresh = 2,
resid_keep_prop = 0.2,
py_maxit = 20,
py_eps = 1e-05,
mscale_maxit = 50,
mscale_tol = 1e-06,
mscale_rho_fun = "bisquare"
)
Arguments
bb |
tuning constant (between 0 and 1/2) for the M-scale used to compute the initial S-estimator. It
determines the robusness (breakdown point) of the resulting MM-estimator, which is
|
efficiency |
desired asymptotic efficiency of the final regression M-estimator. Defaults to 0.95. |
family |
string specifying the name of the family of loss function to be used (current valid options are "bisquare", "opt" and "mopt"). Incomplete entries will be matched to the current valid options. Defaults to "mopt". |
tuning.psi |
tuning parameters for the regression M-estimator computed with a rho function
as specified with argument |
tuning.chi |
tuning constant for the function used to compute the M-scale
used for the initial S-estimator. If missing, it is computed inside |
compute.rd |
logical value indicating whether robust leverage distances need to be computed. |
corr.b |
logical value indicating whether a finite-sample correction should be applied
to the M-scale parameter |
split.type |
determines how categorical and continuous variables are split. See
|
initial |
string specifying the initial value for the M-step of the MM-estimator. Valid
options are |
max.it |
maximum number of IRWLS iterations for the MM-estimator |
refine.tol |
relative convergence tolerance for the S-estimator |
rel.tol |
relative convergence tolerance for the IRWLS iterations for the MM-estimator |
refine.S.py |
relative convergence tolerance for the local improvements of the Pena-Yohai candidates for the S-estimator |
refine.PY |
number of refinement steps for the Pen~a-Yohai candidates |
solve.tol |
(for the S algorithm): relative tolerance for matrix inversion. Hence, this corresponds to |
trace.lev |
positive values (increasingly) provide details on the progress of the MM-algorithm |
psc_keep |
For |
resid_keep_method |
For |
resid_keep_thresh |
See parameter |
resid_keep_prop |
See parameter |
py_maxit |
Maximum number of iterations. See |
py_eps |
Relative tolerance for convergence. See |
mscale_maxit |
Maximum number of iterations for the M-scale algorithm. See |
mscale_tol |
Convergence tolerance for the M-scale algorithm. See |
mscale_rho_fun |
String indicating the loss function used for the M-scale. See |
Details
The argument family
specifies the name of the family of loss
function to be used. Current valid options are "bisquare", "opt", "mopt",
"optV0" and "moptV0". "mopt" is a modified version of the optimal psi
function to make it strictly increasing close to 0, and to make the
corresponding weight function non-increasing.
Value
A list with the necessary tuning parameters.
Choice of Rho Loss Function
As of RobStatTM Versopm 1.0.7, the opt and mopt rhos functions are calculated using polynomials, rather than using the standard normal error function (erf) as in versions of RobStatTM prior to 1.0.7. The numerical results one now gets with the opt or mopt choices will differ by small amounts from those in earlier RobStatTM versions. Users who wish to replicate results from releases prior to 1.0.7 may do so using the family arguments family = "optV0" or family = "moptV0". Note that the derivative of the rho loss function, known as the "psi" function, is not the derivative of the rho polynomial,instead it is still the analytic optimal psi function whose formula is given in the second of the Vignettes referenced just below.
Related Vignettes
For further details, see the Vignettes "Polynomial Opt and mOpt Rho Functions", and "Optimal Bias Robust Regression Psi and Rho".
Author(s)
Matias Salibian-Barrera, matias@stat.ubc.ca
See Also
Examples
data(coleman, package='robustbase')
m2 <- lmrobdetMM(Y ~ ., data=coleman, control=lmrobdet.control(refine.PY=50))
m2
summary(m2)