rlm {MASS}  R Documentation 
Fit a linear model by robust regression using an M estimator.
rlm(x, ...) ## S3 method for class 'formula': rlm(formula, data, weights, ..., subset, na.action, method = c("M", "MM", "model.frame"), wt.method = c("inv.var", "case"), model = TRUE, x.ret = TRUE, y.ret = FALSE, contrasts = NULL) ## Default S3 method: rlm(x, y, weights, ..., w = rep(1, nrow(x)), init, psi = psi.huber, scale.est, k2 = 1.345, method = c("M", "MM"), wt.method = c("inv.var", "case"), maxit = 20, acc = 1e4, test.vec = "resid", lqs.control = NULL) psi.huber(u, k = 1.345, deriv = 0) psi.hampel(u, a = 2, b = 4, c = 8, deriv = 0) psi.bisquare(u, c = 4.685, deriv = 0)
formula 
a formula of the form y ~ x1 + x2 + ... .

data 
data frame from which variables specified in formula are
preferentially to be taken.

weights 
prior weights for each case. 
subset 
An index vector specifying the cases to be used in fitting. 
na.action 
A function to specify the action to be taken if NA s are found. The
default action is for the procedure to fail. An alternative is
na.omit , which leads to omission of cases with missing values on any
required variable.

x 
a matrix or data frame containing the explanatory variables. 
y 
the response: a vector of length the number of rows of x .

method 
currently either Mestimation or find the model frame. MM estimation is Mestimation with Tukey's biweight initialized by a specific Sestimator. See the details section. 
wt.method 
are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is twice as variable? 
model 
should the model frame be returned in the object? 
x.ret 
should the model matrix be returned in the object? 
y.ret 
should the response be returned in the object? 
contrasts 
optional contrast specifications: se lm .

w 
(optional) initial downweighting for each case. 
init 
(optional) initial values for the coefficients OR a method to find
initial values OR the result of a fit with a coef component. Known
methods are "ls" (the default) for an initial leastsquares fit
using weights w*weights , and "lts" for an unweighted
leasttrimmed squares fit with 200 samples.

psi 
the psi function is specified by this argument. It must give
(possibly by name) a function g(x, ..., deriv) that for deriv=0
returns psi(x)/x and for deriv=1 returns psi'(x). Tuning constants
will be passed in via ... .

scale.est 
method of scale estimation: rescaled MAD of the residuals or Huber's
proposal 2 (which can be selected by either "Huber" or
"proposal 2" ).

k2 
tuning constant used for Huber proposal 2 scale estimation. 
maxit 
the limit on the number of IWLS iterations. 
acc 
the accuracy for the stopping criterion. 
test.vec 
the stopping criterion is based on changes in this vector. 
... 
additional arguments to be passed to rlm.default or to the psi
function.

lqs.control 
An optional list of control values for lqs .

u 
numeric vector of evalation points. 
k,a,b,c 
tuning constants 
deriv 
0 or 1 : compute values of the psi function or of its
first derivative.

Fitting is done by iterated reweighted least squares (IWLS).
Psi functions are supplied for the Huber, Hampel and Tukey bisquare
proposals as psi.huber
, psi.hampel
and
psi.bisquare
. Huber's corresponds to a convex optimization
problem and gives a unique solution (up to collinearity). The other
two will have multiple local minima, and a good starting point is
desirable.
Selecting method = "MM"
selects a specific set of options which
ensures that the estimator has a high breakdown point. The initial set
of coefficients and the final scale are selected by an Sestimator
with k0 = 1.548
; this gives (for n >> p) breakdown point 0.5.
The final estimator is an Mestimator with Tukey's biweight and fixed
scale that will inherit this breakdown point provided c > k0
;
this is true for the default value of c
that corresponds to
95% relative efficiency at the normal.
An object of class "rlm"
inheriting from "lm"
.
The additional components not in an lm
object are
s 
the robust scale estimate used 
w 
the weights used in the IWLS process 
psi 
the psi function with parameters substituted 
conv 
the convergence criteria at each iteration 
converged 
did the IWLS converge? 
P. J. Huber (1981) Robust Statistics. Wiley.
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw and W. A. Stahel (1986) Robust Statistics: The Approach based on Influence Functions. Wiley.
A. Marazzi (1993) Algorithms, Routines and S Functions for Robust Statistics. Wadsworth & Brooks/Cole.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
data(stackloss) summary(rlm(stack.loss ~ ., stackloss)) rlm(stack.loss ~ ., stackloss, psi = psi.hampel, init = "lts") rlm(stack.loss ~ ., stackloss, psi = psi.bisquare)