Fit a linear model by robust regression using an M estimator.

`rlm(x, ...)`# S3 method for 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)

# S3 method for default
rlm(x, y, weights, ..., w = rep(1, nrow(x)),
init = "ls", psi = psi.huber,
scale.est = c("MAD", "Huber", "proposal 2"), k2 = 1.345,
method = c("M", "MM"), wt.method = c("inv.var", "case"),
maxit = 20, acc = 1e-4, 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)

An object of class `"rlm"`

inheriting from `"lm"`

.
Note that the `df.residual`

component is deliberately set to

`NA`

to avoid inappropriate estimation of the residual scale from
the residual mean square by `"lm"`

methods.

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?

- wresid
a working residual, weighted for

`"inv.var"`

weights only.

- formula
a formula of the form

`y ~ x1 + x2 + ...`

.- data
an optional data frame, list or environment from which variables specified in

`formula`

are preferentially to be taken.- weights
a vector of 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 ‘factory-fresh’ default action in R is`na.omit`

, and can be changed by`options(na.action=)`

.- 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 M-estimation or MM-estimation or (for the

`formula`

method only) find the model frame. MM-estimation is M-estimation with Tukey's biweight initialized by a specific S-estimator. 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 half 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: see

`lm`

.- w
(optional) initial down-weighting 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 least-squares fit using weights`w*weights`

, and`"lts"`

for an unweighted least-trimmed 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: re-scaled MAD of the residuals (default) 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 evaluation 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 re-weighted 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 S-estimator
with `k0 = 1.548`

; this gives (for \(n \gg p\))
breakdown point 0.5.
The final estimator is an M-estimator 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. Case weights are not
supported for `method = "MM"`

.

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.

`lm`

, `lqs`

.

```
summary(rlm(stack.loss ~ ., stackloss))
rlm(stack.loss ~ ., stackloss, psi = psi.hampel, init = "lts")
rlm(stack.loss ~ ., stackloss, psi = psi.bisquare)
```

Run the code above in your browser using DataLab