lmrob.control {robustbase} | R Documentation

## Tuning Parameters for lmrob() and Auxiliaries

### Description

Tuning parameters for `lmrob`, the MM-type regression estimator and the associated S-, M- and D-estimators. Using `setting="KS2011"` sets the defaults as suggested by Koller and Stahel (2011) and analogously for `"KS2014"`.

The `.M*.default` `function`s and `.M*.defaults` `list`s contain default tuning parameters for all the predefined psi functions, see also `Mpsi`, etc.

### Usage

    lmrob.control(setting, seed = NULL, nResample = 500,
                  tuning.chi = NULL, bb = 0.5, tuning.psi = NULL,
                  max.it = 50, groups = 5, n.group = 400,
                  k.fast.s = 1, best.r.s = 2,
                  k.max = 200, maxit.scale = 200, k.m_s = 20,
                  refine.tol = 1e-7, rel.tol = 1e-7, solve.tol = 1e-7,
                  trace.lev = 0,
                  mts = 1000, subsampling = c("nonsingular", "simple"),
                  compute.rd = FALSE, method = "MM", psi = "bisquare",
                  numpoints = 10, cov = NULL,
                  split.type = c("f", "fi", "fii"), fast.s.large.n = 2000,
                  eps.outlier = function(nobs) 0.1 / nobs,
                  eps.x = function(maxx) .Machine$double.eps^(.75)*maxx,
                  compute.outlier.stats = method,
                  warn.limit.reject = 0.5,
                  warn.limit.meanrw = 0.5, ...)

    .Mchi.tuning.defaults
    .Mchi.tuning.default(psi)
    .Mpsi.tuning.defaults
    .Mpsi.tuning.default(psi)

### Arguments

`setting` | a string specifying alternative default values. Leave empty for the defaults or use `"KS2011"` or `"KS2014"` for the defaults suggested by Koller and Stahel (2011, 2017). See Details.
`seed` | `NULL` or an integer vector compatible with `.Random.seed`: the seed to be used for random re-sampling used in obtaining candidates for the initial S-estimator. The current value of `.Random.seed` will be preserved if `seed` is set, i.e. non-`NULL`; otherwise, as by default, `.Random.seed` will be used and modified as usual from calls to `runif()` etc.
`nResample` | number of re-sampling candidates to be used to find the initial S-estimator. Currently defaults to 500 which works well in most situations (see references).
`tuning.chi` | tuning constant vector for the S-estimator. If `NULL`, as by default, sensible defaults are set (depending on `psi`) to yield a 50% breakdown estimator. See Details.
`bb` | expected value under the normal model of the “chi” (rather rho) function with tuning constant equal to `tuning.chi`. This is used to compute the S-estimator.
`tuning.psi` | tuning constant vector for the redescending M-estimator. If `NULL`, as by default, this is set (depending on `psi`) to yield an estimator with asymptotic efficiency of 95% for normal errors. See Details.
`max.it` | integer specifying the maximum number of IRWLS iterations.
`groups` | (for the fast-S algorithm): Number of random subsets to use when the data set is large.
`n.group` | (for the fast-S algorithm): Size of each of the `groups` above. Note that this must be at least p.
`k.fast.s` | (for the fast-S algorithm): Number of local improvement steps (“I-steps”) for each re-sampling candidate.
`k.m_s` | (for the M-S algorithm): specifies after how many unsucessful refinement steps the algorithm stops.
`best.r.s` | (for the fast-S algorithm): Number of of best candidates to be iterated further (i.e., “refined”); is denoted t in Salibian-Barrera & Yohai(2006).
`k.max` | (for the fast-S algorithm): maximal number of refinement steps for the “fully” iterated best candidates.
`maxit.scale` | integer specifying the maximum number of C level `find_scale()` iterations.
`refine.tol` | (for the fast-S algorithm): relative convergence tolerance for the fully iterated best candidates.
`rel.tol` | (for the RWLS iterations of the MM algorithm): relative convergence tolerance for the parameter vector.
`solve.tol` | (for the S algorithm): relative tolerance for inversion. Hence, this corresponds to `solve.default()`'s `tol`.
`trace.lev` | integer indicating if the progress of the MM-algorithm should be traced (increasingly); default `trace.lev = 0` does no tracing.
`mts` | maximum number of samples to try in subsampling algorithm.
`subsampling` | type of subsampling to be used, a string: `"simple"` for simple subsampling (default prior to version 0.9), `"nonsingular"` for nonsingular subsampling. See also `lmrob.S`.
`compute.rd` | logical indicating if robust distances (based on the MCD robust covariance estimator `covMcd`) are to be computed for the robust diagnostic plots. This may take some time to finish, particularly for large data sets, and can lead to singularity problems when there are `factor` explanatory variables (with many levels, or levels with “few” observations). Hence, is `FALSE` by default.
`method` | string specifying the estimator-chain. `MM` is interpreted as `SM`. See Details of `lmrob` for a description of the possible values.
`psi` | string specifying the type ψ-function used. See Details of `lmrob`. Defaults to `"bisquare"` for S and MM-estimates, otherwise `"lqq"`.
`numpoints` | number of points used in Gauss quadrature.
`cov` | function or string with function name to be used to calculate covariance matrix estimate. The default is `if(method %in% c('SM', 'MM')) ".vcov.avar1" else ".vcov.w"`. See Details of `lmrob`.
`split.type` | determines how categorical and continuous variables are split. See `splitFrame`.
`fast.s.large.n` | minimum number of observations required to switch from ordinary “fast S” algorithm to an efficient “large n” strategy.
`eps.outlier` | limit on the robustness weight below which an observation is considered to be an outlier. Either a numeric(1) or a function that takes the number of observations as an argument. Used in `summary.lmrob` and `outlierStats`.
`eps.x` | limit on the absolute value of the elements of the design matrix below which an element is considered zero. Either a numeric(1) or a function that takes the maximum absolute value in the design matrix as an argument.
`compute.outlier.stats` | vector of `character` strings, each valid to be used as `method` argument. Used to specify for which estimators outlier statistics (and warnings) should be produced. Set to empty string if none are required.
`warn.limit.reject` | limit of ratio # rejected / # obs in level above (>=) which a warning is produced. Set to `NULL` to disable warning.
`warn.limit.meanrw` | limit of the mean robustness per factor level below which (<=) a warning is produced. Set to `NULL` to disable warning.
`...` | further arguments to be added as `list` components to the result, e.g., those to be used in `.vcov.w()`.

### Details

The option `setting="KS2011"` alters the default arguments. They are changed to `method = "SMDM"`, `psi = "lqq"`, `max.it = 500`, `k.max = 2000`, `cov = ".vcov.w"`. The defaults of all the remaining arguments are not changed.

The option `setting="KS2014"` builds upon `setting="KS2011"`. More arguments are changed to `best.r.s = 20, k.fast.s = 2, nResample = 1000`. This setting should produce more stable estimates for designs with `factor`s.

By default, and in `.Mpsi.tuning.default()` and `.Mchi.tuning.default()`, `tuning.chi` and `tuning.psi` are set to yield an MM-estimate with breakdown point 0.5 and efficiency of 95% at the normal.

If numeric `tuning.chi` or `tuning.psi` are specified, say `cc`, for `psi = "ggw"` or `"lqq"`, `.psi.const(cc, psi)` is used, see its help page.

To get the defaults, e.g., `.Mpsi.tuning.default(psi)` is equivalent to but more efficient than the formerly widely used `lmrob.control(psi = psi)$tuning.psi`.

These defaults are:

`psi` | `tuning.chi` | `tuning.psi`
`bisquare` | `1.54764` | `4.685061`
`welsh` | `0.5773502` | `2.11`
`ggw` | `c(-0.5, 1.5, NA, 0.5)` | `c(-0.5, 1.5, 0.95, NA)`
`lqq` | `c(-0.5, 1.5, NA, 0.5)` | `c(-0.5, 1.5, 0.95, NA)`
`optimal` | `0.4047` | `1.060158`
`hampel` | `c(1.5, 3.5, 8)*0.2119163` | `c(1.5, 3.5, 8)*0.9014`

The values for the tuning constant for the `ggw` psi function are hard coded. The constants vector has four elements: minimal slope, b (controlling the bend at the maximum of the curve), efficiency, breakdown point. Use `NA` for an unspecified value, see examples in the tables.

The constants for the `"hampel"` psi function are chosen to have a redescending slope of -1/3. Constants for a slope of -1/2 would be

`psi` | `tuning.chi` | `tuning.psi`
`"hampel"` | `c(2, 4, 8) * 0.1981319` | `c(2, 4, 8) * 0.690794`

Alternative coefficients for an efficiency of 85% at the normal are given in the table below.

`psi` | `tuning.psi`
`bisquare` | `3.443689`
`welsh` | `1.456`
`ggw`, `lqq` | `c(-0.5, 1.5, 0.85, NA)`
`optimal` | `0.8684`
`hampel` (-1/3) | `c(1.5, 3.5, 8)* 0.5704545`
`hampel` (-1/2) | `c( 2, 4, 8) * 0.4769578`

### Value

`.Mchi.tuning.default(psi)` and `.Mpsi.tuning.default(psi)` return a short `numeric` vector of tuning constants which are defaults for the corresponding psi-function, see the Details. They are based on the named `list`s `.Mchi.tuning.defaults` and `.Mpsi.tuning.defaults`, respectively.

`lmrob.control()` returns a named `list` with over twenty components, corresponding to the arguments, where `tuning.psi` and `tuning.chi` are typically computed, as `.Mpsi.tuning.default(psi)` or `.Mchi.tuning.default(psi)`, respectively.

### Author(s)

Matias Salibian-Barrera, Martin Maechler and Manuel Koller

### References

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.

Koller, M. and Stahel, W.A. (2017) Nonsingular subsampling for regression S~estimators with categorical predictors, Computational Statistics 32(2): 631–646. doi: 10.1007/s00180-016-0679-x. Referred as `"KS2014"` everywhere in robustbase; A shorter first version, Koller (2012) has been available from https://arxiv.org/abs/1208.5595.

### See Also

`Mpsi`, etc, for the (fast!) psi function computations; `lmrob`, also for references and examples.

### Examples

    ## Show the default settings:
    str(lmrob.control())

    ## Artificial data for a  simple  "robust t test":
    set.seed(17)
    y <- y0 <- rnorm(200)
    y[sample(200,20)] <- 100*rnorm(20)
    gr <- as.factor(rbinom(200, 1, prob = 1/8))
    lmrob(y0 ~ 0+gr)

    ## Use  Koller & Stahel(2011)'s recommendation but a larger  'max.it':
    str(ctrl <- lmrob.control("KS2011", max.it = 1000))

    str(.Mpsi.tuning.defaults)
    stopifnot(identical(.Mpsi.tuning.defaults,
                       sapply(names(.Mpsi.tuning.defaults),
                              .Mpsi.tuning.default)))
    ## Containing (names!) all our (pre-defined) redescenders:
    str(.Mchi.tuning.defaults)

    ## Difference between settings:
    C11 <- lmrob.control("KS2011")
    C14 <- lmrob.control("KS2014")
    str(C14)
    ## Apart from `setting` itself, they only differ in three places:
    diffC <- names(which(!mapply(identical, C11,C14, ignore.environment=TRUE)))
    cbind(KS11 = unlist(C11[diffC[-1]]),
          KS14 = unlist(C14[diffC[-1]]))
    ##           KS11 KS14
    ## nResample  500 1000
    ## best.r.s     2   20
    ## k.fast.s     1    2

[Package robustbase version 0.92-8 Index]