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distrMod

internals

Internal functions of package distrMod

Description

These functions are used internally by package “distrMod”.

Usage

.inArgs(arg, fct)
.isUnitMatrix(m)
.csimpsum(fx)
.validTrafo(trafo, dimension, dimensionwithN)

.CvMMDCovariance(L2Fam, param, mu = distribution(L2Fam),
                 withplot = FALSE, withpreIC = FALSE,
                 N = 1021, rel.tol=.Machine$double.eps^0.3,
                 TruncQuantile = getdistrOption("TruncQuantile"), 
                 IQR.fac = 15, ..., diagnostic = FALSE)
.oldCvMMDCovariance(L2Fam, param, mu = distribution(L2Fam),
                 withplot = FALSE, withpreIC = FALSE,
                 N = getdistrOption("DefaultNrGridPoints")+1,
                 rel.tol=.Machine$double.eps^0.3,
                 TruncQuantile = getdistrOption("TruncQuantile"),
                 IQR.fac = 15, ...)
.CvMMDCovarianceWithMux(L2Fam, param, withplot = FALSE, withpreIC = FALSE,
                        N = 1021, rel.tol=.Machine$double.eps^0.3,
                        TruncQuantile = getdistrOption("TruncQuantile"),
                        IQR.fac = 15, ..., x=NULL)

.show.with.sd(est, s)
.getLogDeriv(distr, 
             lowerTruncQuantile = getdistrExOption("ElowerTruncQuantile"), 
             upperTruncQuantile = getdistrExOption("EupperTruncQuantile"), 
                         IQR.fac = getdistrExOption("IQR.fac"))
.deleteDim(x)

Arguments

`arg`	a formal argument as character
`fct`	a function
`m`	a matrix
`est`	an estimator; usually a vector
`s`	a standard deviation
`trafo`	an object of class `MatrixorFunction`
`dimension`	a numeric — length of main part of the parameter
`dimensionwithN`	a numeric — length of main and nuisance part of the parameter
`L2Fam`	an object of class `L2ParamFamily` — for which we want to determine the IC resp. the as. [co]variance of the corresponding Minimum CvM estimator
`param`	an object of class `ParamFamParameter`, the parameter value at which we want to determine the IC resp. the as. [co]variance of the corresponding Minimum CvM estimator
`mu`	an object of class `UnivariateDistribution`: integration measure (resp. distribution) for CvM distance
`rel.tol`	relative tolerance for `distrExIntegrate`.
`TruncQuantile`	quantile for quantile based integration range.
`lowerTruncQuantile`	lower quantile for quantile based integration range.
`upperTruncQuantile`	upper quantile for quantile based integration range.
`IQR.fac`	factor for scale based integration range (i.e.; median of the distribution +-`IQR.fac`*IQR).
`withplot`	logical: shall we plot corresponding ICs?
`withpreIC`	logical: shall we return a list with components `preIC` and `var` or just `var`; here `var` is the corresponding asymptotic variance and `preIC` the corresponding `EuclRandVarList` featuring as argument `Curve` in `IC`s of package RobAStBase
`N`	a numeric: the number of gridpoints for constructing the mu- resp. P_theta-“primitive” function
`fx`	a vector of function evaluations multiplied by the gridwidth
`distr`	an object of class `AbscontDistribution`
`...`	further argument to be passed through — so `.CvMMDCovariance` can digest more arguments.
`x`	in `deleteDim`: a possibly named vector, which may have a `dim` attribute; in `.CvMMDCovarianceWithMux`: `NULL` (default) or the vector with observations to build integration measure mu as the empirical cdf.
`diagnostic`	logical; if `TRUE`, the return value of `.CvMMDCovariance` obtains an attribute `"diagnostic"` (usually a lengthy list) with diagnostic information on the call and on the integration, the latter inherited from the calls to `distrExIntegrate` and `E` in this function. Depending on the actually used `E` method, this comprises entries `method` (`"integrate"` or `"GLIntegrate"`), `result` (the complete return value of the integration method), `args` (the args with which the integration method was called), and `time` (the time to compute the integral).

Details

.inArgs (borrowed from package distr) checks whether an argument arg is a formal argument of fct — not vectorized.

.csimpsum (borrowed from package distr) produces a primitimive function out of function evaluations by means of vectorized Simpson quadrature method, returning already the function values of the prime function on a grid; it is to mimick the behaviour of cumsum.

.isUnitMatrix checks whether the argument is a unit matrix.

.validTrafo checks whether the argument is a valid transformation.

.CvMMDCovariance determines the IC resp. the as. [co]variance of the corresponding Minimum CvM estimator (numerical integration is done on quantile scale).

.oldCvMMDCovariance determines the IC resp. the as. [co]variance of the corresponding Minimum CvM estimator (numerical integration is done on the original scale). .CvMMDCovariance is a wrapper to .CvMMDCovariance which uses emp. cdf as mu.

.show.with.sd is code borrowed from print.fitdistr in package MASS by B.D. Ripley. It pretty-prints estimates with corresponding sd's below.

.getLogDeriv determines numerically the negative logarithmic derivative of the density of distribution distr; to this end uses D1ss, D2ss from Martin Maechler's package sfsmisc.

.deleteDim deletes a possible dim argument (sets it to NULL) but retains all other possible attributes, in particular a name attribute.

Value

`.getLogderiv`	a function in one argument `x` — the negative logarithmic derivative of the density
`.inArgs`	`logical` (length 1)
`.csimpsum`	`numeric` (of length half the input length)
`.isUnitMatrix`	`logical` (length 1)
`.validTrafo`	`logical` (length 1)
`.CvMMDCovariance`	corresponding as. [co]variance of the corresponding Minimum CvM estimator or list withcomponents `preIC` and `var` —see above
`.show.with.sd`	`invisible()`
`.deleteDim`	vector `x` without `dim` attribute

Author(s)

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de Matthias Kohl Matthias.Kohl@stamats.de