This function sets tuning parameters for the MM estimator implemented in lmrobdetMM and
the Distance Constrained Maximum Likelihood regression estimators
computed by lmrobdetDCML.
lmrobdet.control(bb = 0.5, efficiency = 0.99, family = "optimal",
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.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")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
bb. Defaults to 0.5.
desired asymptotic efficiency of the final regression M-estimator. Defaults to 0.85.
string specifying the name of the family of loss function to be used (current valid options are "bisquare", "optimal" and "modopt"). Incomplete entries will be matched to the current valid options.
tuning parameters for the regression M-estimator computed with a rho function
as specified with argument family. If missing, it is computed inside lmrobdet.control to match
the value of efficiency according to the family of rho functions specified in family.
Appropriate values for tuning.psi for a given desired efficiency for Gaussian errors
can be constructed using the functions bisquare, modopt and optimal.
tuning constant for the function used to compute the M-scale
used for the initial S-estimator. If missing, it is computed inside lmrobdet.control to match
the value of bb according to the family of rho functions specified in family.
logical value indicating whether robust leverage distances need to be computed.
logical value indicating whether a finite-sample correction should be applied
to the M-scale parameter bb.
determines how categorical and continuous variables are split. See
splitFrame.
string specifying the initial value for the M-step of the MM-estimator. Valid
options are 'S', for an S-estimator and 'MS' for an M-S estimator which is
appropriate when there are categorical explanatory variables in the model.
maximum number of IRWLS iterations for the MM-estimator
relative covergence tolerance for the S-estimator
relative covergence tolerance for the IRWLS iterations for the MM-estimator
number of refinement steps for the Pen~a-Yohai candidates
(for the S algorithm): relative tolerance for matrix inversion. Hence, this corresponds to solve.default's tol.
positive values (increasingly) provide details on the progress of the MM-algorithm
For pyinit, proportion of observations to remove based on PSCs. The effective proportion of removed
observations is adjusted according to the sample size to be prosac*(1-p/n). See pyinit.
For pyinit, how to clean the data based on large residuals. If
"threshold", all observations with scaled residuals larger than C.res will
be removed, if "proportion", observations with the largest prop residuals will
be removed. See pyinit.
See parameter resid_keep_method above. See pyinit.
See parameter resid_keep_method above. See pyinit.
Maximum number of iterations. See pyinit.
Relative tolerance for convergence. See pyinit.
String indicating the loss function used for the M-scale. See pyinit.
A list with the necessary tuning parameters.
# NOT RUN {
data(coleman, package='robustbase')
m2 <- lmrobdetMM(Y ~ ., data=coleman, control=lmrobdet.control(refine.PY=50))
m2
summary(m2)
# }
Run the code above in your browser using DataLab