reexported-fun {georob}

Re-Exported Functions from R package imports

Description

The imported functions K, lmrob.control, and waldtest are re-exported for ease of use without attaching the respective packages.

Usage

K(dist, model)

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, scale.tol = 1e-10, solve.tol = 1e-7,
  zero.tol = 1e-10, 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,
  # only for outlierStats() :
  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, ...)

Arguments

dist

a numeric vector with distances.

model

an object of class “covmodel” that defines an isotropic covariance model, see covmodel.

setting

a string specifying alternative default values, see lmrob.control.

seed

NULL or an integer vector compatible with .Random.seed, see lmrob.control.

nResample

number of re-sampling candidates to be used to find the initial S-estimator, see lmrob.control.

tuning.chi

tuning constant vector for the S-estimator, see lmrob.control.

bb

expected value under the normal model of the “chi”, see lmrob.control

tuning.psi

tuning constant vector for the redescending M-estimator, see lmrob.control.

max.it

integer specifying the maximum number of IRWLS iterations, see lmrob.control.

groups

(for the fast-S algorithm): Number of random subsets to use when the data set is large, see lmrob.control.

n.group

(for the fast-S algorithm): Size of each of the groups above, see lmrob.control.

k.fast.s

(for the fast-S algorithm): Number of local improvement steps (“I-steps”) for each re-sampling candidate, see lmrob.control.

best.r.s

(for the fast-S algorithm): Number of of best candidates to be iterated further, see lmrob.control.

k.max

(for the fast-S algorithm): maximal number of refinement steps for the “fully” iterated best candidates, see lmrob.control.

maxit.scale

integer specifying the maximum number of C level find_scale() iterations (in fast-S and M-S algorithms), see lmrob.control.

k.m_s

(for the M-S algorithm): specifies after how many unsuccessful refinement steps the algorithm stops, see lmrob.control.

refine.tol

(for the fast-S algorithm): relative convergence tolerance for the fully iterated best candidates, see lmrob.control.

rel.tol

(for the RWLS iterations of the MM algorithm): relative convergence tolerance for the parameter vector, see lmrob.control.

scale.tol

(for the scale estimation iterations of the S algorithm): relative convergence tolerance for the scale \sigma(.), see lmrob.control, see lmrob.control.

solve.tol

(for the S algorithm): relative tolerance for inversionsee lmrob.control.

zero.tol

for checking 0-residuals in the S algorithm, non-negative number, see lmrob.control.

trace.lev

integer indicating if the progress of the MM-algorithm and the fast-S algorithms should be traced, see lmrob.control.

mts

maximum number of samples to try in subsampling algorithm, see lmrob.control.

subsampling

type of subsampling to be used, see lmrob.control.

compute.rd

a logical scalar indicating if robust distances (based on the MCD robust covariance estimator) are to be computed for the robust diagnostic plots, see lmrob.control.

method

string specifying the estimator-chain, see lmrob.control.

psi

string specifying the type \psi-function used., see lmrob.control.

numpoints

number of points used in Gauss quadrature, see lmrob.control

cov

function or string with function name to be used to calculate covariance matrix estimate, see lmrob.control.

split.type

determines how categorical and continuous variables are split, see lmrob.control.

fast.s.large.n

minimum number of observations required to switch from ordinary “fast S” algorithm to an efficient “large n” strategy, see lmrob.control

eps.outlier

limit on the robustness weight below which an observation is considered to be an outlier, see lmrob.control.

eps.x

limit on the absolute value of the elements of the design matrix below which an element is considered zero, see lmrob.control.

compute.outlier.stats

vector of character strings, each valid to be used as method argument, see lmrob.control

warn.limit.reject

see lmrob.control.

warn.limit.meanrw

limit of the mean robustness per factor level below which (\leq) a warning is produced. Set to NULL to disable warning.

...

some methods for the generic function waldtest require additional arguments, see respective help pages.

Details

The function K is required for computing block Kriging predictions by the function f.point.block.cov of the package constrainedKriging.

Furthermore, the function lmrob.control allows to pass tuning parameters to the function lmrob of the package robustbase, which is used for computing robust initial values of the regression coefficients.

Value

See help pages of K and lmrob.control for the output generated by these functions.