Control Parameters for Robust Linear Regression
Allows the users to set values affecting the estimation procedure for
robust regression in lmRob.
lmRob.control(tlo = 1e-4, tua = 1.5e-06, mxr = 50, mxf = 50, mxs = 50,
tl = 1e-06, estim = "Final", initial.alg = "Auto", final.alg = "MM",
seed = 1313, level = 0.1, efficiency = 0.9,
weight = c("Optimal", "Optimal"), trace = TRUE)tlo |
the relative tolerance in the iterative algorithms. |
tua |
the tolerance used for the determination of pseudo-rank. |
mxr |
the maximum number of iterations in the refinement step. |
mxf |
the maximum number of iterations for computing final coefficient estimates. |
mxs |
the maximum number of iterations for computing scale estimate. |
tl |
the tolerance for scale denominators. If a scale estimate becomes less than |
estim |
parameter that determines the type of estimator to be computed. If |
initial.alg |
parameter that determines the algorithm for initial estimates. Valid choices are |
final.alg |
parameter that determines the type of the final estimates. Valid choices are |
seed |
seed parameter used in the random sampling and genetic algorithm for the computation of initial estimates. |
weight |
a character vector that determines the type of loss functions to be used. The first determines the loss function used for the initial estimates, and the second determines the loss function used for the final M-estimates. Valid choices are |
level |
the level of significance of the test for bias of the final MM-estimates, if desired later on. |
efficiency |
the asymptotic efficiency of the final estimate. |
trace |
a logical flag: if |
a list containing the values used for each of the control parameters.
data(stack.dat)
my.control <- lmRob.control(weight=c("Bisquare","Optimal"))
stack.bo <- lmRob(Loss ~ ., data = stack.dat, control = my.control)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.