robustloggamma (version 1.0-2.1)

loggammarob.control: Tuning parameters for loggammarob

Description

Tuning parameters for 'loggammarob' for all four methods implemented.

Usage

loggammarob.control(method="oneWL", tuning.rho=1.547647,
  tuning.psi=6.08, lower=-7, upper=7, n=201, max.it=750,
  refine.tol=1e-6, solve.tol=1e-7, nResample=100, bw=0.3,
  smooth=NULL, raf=c("NED","GKL","PWD","HD","SCHI2"),
  tau=1, subdivisions=1000, lambda.step=TRUE, sigma.step=TRUE,
  step=1, minw=0.04, nexp=1000, reparam=NULL, bootstrap=FALSE,
  bootstrap.lambda=NULL, qthreshold=0.9, nTML=2000, xmax=1e100,
  iter=1, pcut=0.997, compute.rd=FALSE,
  eps.outlier= function(nobs) 0.1 / nobs)

Arguments

method

character. The method to be used. See Details below.

tuning.rho

numeric. Tuning constant c1 for the Tau-estimator.

tuning.psi

numeric. Tuning constant c2 for the Tau-estimator.

lower

numeric. The lower limit for the search grid of the shape parameter.

upper

numeric. The upper limit for the search grid of the shape parameter.

n

numeric. The number of subdivisions for the search grid of the shape parameter.

max.it

numeric. Maximum number of iterations.

refine.tol

numeric. Relative convergence tolerance for the fully iterated best candidates.

solve.tol

numeric. Relative tolerance for inversion. Hence, this corresponds to solve.default()'s tol.

nResample

integer. Number of re-sampling candidates to be used to find the initial estimator. Currently defaults to 100 which works well in most situations.

bw

numeric. Bandwidth used in the Weighted Likelihood steps.

smooth

NULL or numeric. When not NULL the parameter bw is set to smooth times the square root of the starting value of the scale parameter.

raf

character. Residual adjustment function used in the Weighted Likelihood steps

raf="NED": Negative Exponential Disparity RAF,

raf="GKL": Generalized Kullback-Leibler Divergence Family with parameter tau (see below) RAF.

raf="PWD": Power Divergence Family with parameter tau (see below) RAF.

raf="HD": Hellinger Distance RAF,

raf="SCHI2": Symmetric Chi-Squared Disparity RAF.

Default value is "NED".

tau

parameter used when raf is equal to "PWD" or "GKL".

subdivisions

numeric. Number of subdivisions used in the approximation of the smoothed model density in the Weighted Likelihood steps.

lambda.step

logical.

sigma.step

logical.

step

integer. Number of steps to be performed when method is "oneWL" (only implemented for the functions for non censored data).

minw

numeric. A scalar in the interval [0,1]. When method is "oneWL" the weights smaller than minw are set to zero.

nexp

integer. When method is "oneWL" number of quantile points used in the approximation of the Expected Jacobian matrix.

reparam

list. When method is "oneWL" a reparametrization is possible for the "sigma" parameter. See function sqrtloggamma for an example.

bootstrap

logical. To use loggammarob in boot

bootstrap.lambda

numeric. An initial estimates for the shape parameter. To use loggammarob in boot

qthreshold

numeric. A value in (0.5, 1] used for TQtau e TWQtau procedure. It is the quantile order to truncated the data on the right.

nTML

numeric. Number of elements to be considered in the grid for finding the cut points of the TML.

xmax

numeric. A threshold value for the log likelihood. Used for ML.

iter

numeric. Number of iterations to be performed. Only working for TML.

pcut

numeric. Fraction to determined the cut points of the TML.

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.

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.loggammacenslmrob.

References

C. Agostinelli, A. Marazzi and V.J. Yohai (2015) Robust estimates of the generalized loggamma distribution, Technometrics, Volume 56, Issue 1, 2014. DOI: 10.1080/00401706.2013.818578

C. Agostinelli, A. Marazzi, V.J. Yohai and A. Randriamiharisoa (2016)

Robust Estimation of the Generalized Loggamma Model. The R Package robustloggamma. Journal of Statistical Software. Accepted.

C. Agostinelli, I. Locatelli, A. Marazzi and V.J. Yohai (2015) Robust estimators of accelerated failure time regression with generalized log-gamma errors. Submitted.

See Also

loggammarob

Examples

# NOT RUN {
## Show the default settings:
  str(loggammarob.control())
# }