rlm {MASS} | R Documentation

## Robust Fitting of Linear Models

### Description

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

### Usage

    rlm(x, ...)

    ## S3 method for class 'formula':
    rlm(formula, data, weights, ..., subset, na.action,
        method = c("M", "MM", "model.frame"),
        wt.method = c("case", "inv.var"),
        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("case", "inv.var"),
        maxit = 20, acc = 1e-4, test.vec = "resid")

### Arguments

`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 M-estimation or 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 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 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 `"lqs"` 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 or Huber's 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.

### Details

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 >> 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.

### Value

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?

### References

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.

### See Also

`lm`, `lqs`.

### Examples

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

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