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

Arguments

arg

a formal argument as character

fct

a function

a matrix

est

an estimator; usually a vector

a standard deviation

trafo

an object of class MatrixorFunction

dimension

a numeric

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

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 $\pm$IQR.fac$\times$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 EuclRandVa

a numeric: the number of gridpoints for constructing the $\mu$- resp. $P_\theta$-``primitive'' function

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

Value

.getLogderiva function in one argument x --- the negative logarithmic derivative of the density
.inArgslogical (length 1)
.csimpsumnumeric (of length half the input length)
.isUnitMatrixlogical (length 1)
.validTrafological (length 1)
.CvMMDCovariancecorresponding as. [co]variance of the corresponding Minimum CvM estimator or list withcomponents preIC and var ---see above
.show.with.sdinvisible()

concept

utilities

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. Still some checking / optimization / improvement needed. .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.