lmrob {robustbase} | R Documentation

## MM-type Estimators for Linear Regression

### Description

Computes fast MM-type estimators for linear (regression) models.

### Usage

    lmrob(formula, data, subset, weights, na.action, method = "MM",
          model = TRUE, x = !control$compute.rd, y = FALSE,
          singular.ok = TRUE, contrasts = NULL, offset = NULL,
          control = NULL, init = NULL, ...)

### Arguments

`formula` | a symbolic description of the model to be fit. See `lm` and `formula` for more details.
`data` | an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `lmrob` is called.
`subset` | an optional vector specifying a subset of observations to be used in the fitting process.
`weights` | an optional vector of weights to be used in the fitting process (in addition to the robustness weights computed in the fitting process).
`na.action` | a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset. The “factory-fresh” default is `na.omit`. Another possible value is `NULL`, no action. Value `na.exclude` can be useful.
`method` | string specifying the estimator-chain. `MM` is interpreted as `SM`. See Details, notably the currently recommended `setting = "KS2014"`.
`model, x, y` | logicals. If `TRUE` the corresponding components of the fit (the model frame, the model matrix, the response) are returned.
`singular.ok` | logical. If `FALSE` (the default in S but not in R) a singular fit is an error.
`contrasts` | an optional list. See the `contrasts.arg` of `model.matrix.default`.
`offset` | this can be used to specify an a priori known component to be included in the linear predictor during fitting. An `offset` term can be included in the formula instead or as well, and if both are specified their sum is used.
`control` | a `list` specifying control parameters; use the function `lmrob.control(.)` and see its help page.
`init` | an optional argument to specify or supply the initial estimate. See Details.
`...` | additional arguments can be used to specify control parameters directly instead of (but not in addition to!) via `control`.

### Details

Overview:

  This function computes an MM-type regression estimator as described in Yohai (1987) and Koller and Stahel (2011). By default it uses a bi-square redescending score function, and it returns a highly robust and highly efficient estimator (with 50% breakdown point and 95% asymptotic efficiency for normal errors). The computation is carried out by a call to `lmrob.fit()`.

  The argument `setting` of `lmrob.control` is provided to set alternative defaults as suggested in Koller and Stahel (2011) (`setting="KS2011"`; now do use its extension `setting="KS2014"`). For further details, see `lmrob.control`.

Initial Estimator `init`:

  The initial estimator may be specified using the argument `init`. This can either be a string, a function or a list. A string can be used to specify built in internal estimators (currently `S` and `M-S`, see See also below). A `function` taking arguments `x, y, control, mf` (where `mf` stands for `model.frame`) and returning a list containing at least the initial coefficients as `coefficients` and the initial scale estimate `scale`. Or a list giving the initial coefficients and scale as `coefficients` and `scale`. See also Examples.

  Note that if the `init` argument is a function or list, the `method` argument must not contain the initial estimator, e.g., use `MDM` instead of `SMDM`.

  The default, equivalent to `init = "S"`, uses as initial estimator an S-estimator (Rousseeuw and Yohai, 1984) which is computed using the Fast-S algorithm of Salibian-Barrera and Yohai (2006), calling `lmrob.S()`. That function, since March 2012, by default uses nonsingular subsampling which makes the Fast-S algorithm feasible for categorical data as well, see Koller (2012). Note that convergence problems may still show up as warnings, e.g.,

        S refinements did not converge (to refine.tol=1e-07) in 200 (= k.max) steps

  and often can simply be remedied by increasing (i.e. weakening) `refine.tol` or increasing the allowed number of iterations `k.max`, see `lmrob.control`.

Method `method`:

  The following chain of estimates is customizable via the `method` argument. There are currently two types of estimates available,

  `"M"`:

    corresponds to the standard M-regression estimate.

  `"D"`:

    stands for the Design Adaptive Scale estimate as proposed in Koller and Stahel (2011).

  The `method` argument takes a string that specifies the estimates to be calculated as a chain. Setting `method='SMDM'` will result in an intial S-estimate, followed by an M-estimate, a Design Adaptive Scale estimate and a final M-step. For methods involving a `D`-step, the default value of `psi` (see `lmrob.control`) is changed to `"lqq"`.

  By default, standard errors are computed using the formulas of Croux, Dhaene and Hoorelbeke (2003) (`lmrob.control` option `cov=".vcov.avar1"`). This method, however, works only for MM-estimates. For other `method` arguments, the covariance matrix estimate used is based on the asymptotic normality of the estimated coefficients (`cov=".vcov.w"`) as described in Koller and Stahel (2011). The var-cov computation can be skipped by `cov = "none"` and (re)done later by e.g., `vcov(<obj>, cov = ".vcov.w")`.

  As of robustbase version 0.91-0 (April 2014), the computation of robust standard errors for `method="SMDM"` has been changed. The old behaviour can be restored by setting the control parameter `cov.corrfact = "tauold"`.

### Value

An object of class `lmrob`; a list including the following components:

`coefficients` | The estimate of the coefficient vector
`scale` | The scale as used in the M estimator.
`residuals` | Residuals associated with the estimator.

`converged` | `TRUE` if the IRWLS iterations have converged.
`iter` | number of IRWLS iterations
`rweights` | the “robustness weights” ψ(r_i/S) / (r_i/S).
`fitted.values` | Fitted values associated with the estimator.

`init.S` | The list returned by `lmrob.S` or `lmrob.M.S` (for MM-estimates only)
`init` | A similar list that contains the results of intermediate estimates (not for MM-estimates).

`rank` | the numeric rank of the fitted linear model.
`cov` | The estimated covariance matrix of the regression coefficients
`df.residual` | the residual degrees of freedom.

`weights` | the specified weights (missing if none were used).
`na.action` | (where relevant) information returned by `model.frame` on the special handling of `NA`s.
`offset` | the offset used (missing if none were used).
`contrasts` | (only where relevant) the contrasts used.
`xlevels` | (only where relevant) a record of the levels of the factors used in fitting.
`call` | the matched call.
`terms` | the `terms` object used.

`model` | if requested (the default), the model frame used.
`x` | if requested, the model matrix used.
`y` | if requested, the response used.

In addition, non-null fits will have components `assign`, and `qr` relating to the linear fit, for use by extractor functions such as `summary`.

### Author(s)

(mainly:) Matias Salibian-Barrera and Manuel Koller

### References

Croux, C., Dhaene, G. and Hoorelbeke, D. (2003) Robust standard errors for robust estimators, Discussion Papers Series 03.16, K.U. Leuven, CES.

Koller, M. (2012) Nonsingular subsampling for S-estimators with categorical predictors, ArXiv e-prints https://arxiv.org/abs/1208.5595; extended version published as Koller and Stahel (2017), see `lmrob.control`.

Koller, M. and Stahel, W.A. (2011) Sharpening Wald-type inference in robust regression for small samples. Computational Statistics & Data Analysis 55(8), 2504–2515.

Maronna, R. A., and Yohai, V. J. (2000) Robust regression with both continuous and categorical predictors. Journal of Statistical Planning and Inference 89, 197–214.

Rousseeuw, P.J. and Yohai, V.J. (1984) Robust regression by means of S-estimators, In Robust and Nonlinear Time Series, J. Franke, W. Härdle and R. D. Martin (eds.). Lectures Notes in Statistics 26, 256–272, Springer Verlag, New York.

Salibian-Barrera, M. and Yohai, V.J. (2006) A fast algorithm for S-regression estimates, Journal of Computational and Graphical Statistics 15(2), 414–427.

Yohai, V.J. (1987) High breakdown-point and high efficiency estimates for regression. The Annals of Statistics 15, 642–65.

Yohai, V., Stahel, W.~A. and Zamar, R. (1991) A procedure for robust estimation and inference in linear regression; in Stahel and Weisberg (eds), Directions in Robust Statistics and Diagnostics, Part II, Springer, New York, 365–374; doi: 10.1007/978-1-4612-4444-8_20.

### See Also

`lmrob.control`; for the algorithms `lmrob.S`, `lmrob.M.S` and `lmrob.fit`; and for methods, `summary.lmrob`, for the extra “statistics”, notably R^2 (“R squared”); `predict.lmrob`, `print.lmrob`, `plot.lmrob`, and `weights.lmrob`.

### Examples

    data(coleman)
    set.seed(0)
    ## Default for a very long time:
    summary( m1 <- lmrob(Y ~ ., data=coleman) )

    ## Nowadays **strongly recommended** for routine use:
    summary(m2 <- lmrob(Y ~ ., data=coleman, setting = "KS2014") )
    ##                                       ------------------

    plot(residuals(m2) ~ weights(m2, type="robustness")) ##-> weights.lmrob()
    abline(h=0, lty=3)

    data(starsCYG, package = "robustbase")
    ## Plot simple data and fitted lines
    plot(starsCYG)
      lmST <-    lm(log.light ~ log.Te, data = starsCYG)
    (RlmST <- lmrob(log.light ~ log.Te, data = starsCYG))
    abline(lmST, col = "red")
    abline(RlmST, col = "blue")
    ## --> Least Sq.:/ negative slope  \ robust: slope ~= 2.2 % checked in ../tests/lmrob-data.R
    summary(RlmST) # -> 4 outliers; rest perfect
    vcov(RlmST)
    stopifnot(all.equal(fitted(RlmST),
                        predict(RlmST, newdata = starsCYG), tol = 1e-14))
    ## FIXME: setting = "KS2011"  or  setting = "KS2014"  **FAIL** here

    ##--- 'init' argument -----------------------------------
    ## 1)  string
    set.seed(0)
    m3 <- lmrob(Y ~ ., data=coleman, init = "S")
    stopifnot(all.equal(m1[-18], m3[-18]))
    ## 2) function
    initFun <- function(x, y, control, ...) { # no 'mf' needed
        init.S <- lmrob.S(x, y, control)
        list(coefficients=init.S$coef, scale = init.S$scale)
    }
    set.seed(0)
    m4 <- lmrob(Y ~ ., data=coleman, method = "M", init = initFun)
    ## list
    m5 <- lmrob(Y ~ ., data=coleman, method = "M",
                init = list(coefficients = m3$init$coef, scale = m3$scale))
    stopifnot(all.equal(m4[-17], m5[-17]))

[Package robustbase version 0.93-4 Index]