.is.vector.lattice(x)
.is.consistent(lattice, support, eq.space = TRUE)
.make.lattice.es.vector(x)
.inArgs(arg, fct)
.isEqual(p0, p1, tol = min( getdistrOption("TruncQuantile")/2,
.Machine$double.eps^.7))
.isEqual01(x)
.isIn(p0, pmat, tol = min( getdistrOption("TruncQuantile")/2,
.Machine$double.eps^.7
))
.isInteger(x, tol = .Machine$double.eps)
.isNatural(x, tol = .Machine$double.eps)
.isNatural0(x, tol = .Machine$double.eps)
.setEqual(x, y, tol = 1e-7)
.presubs(inp, frompat, topat)
.makeD(object, argList, stand = NULL, fac = NULL)
.makeP(object, argList, sign = TRUE, correct = NULL, fac =
NULL, fac2 = NULL)
.makeQ(object, lastCall, sign = TRUE, Cont = TRUE)
.plusm(e1, e2, Dclass = "DiscreteDistribution")
.multm(e1, e2, Dclass = "DiscreteDistribution")
.notwithLArg(D)
.getObjName(i = 1)
.discretizeP(D, lower, upper, h)
.fm(x,f)
.fM(x,f)
.fM2(x,f)
.makeDd(x,y, yleft, yright)
.makePd(x,y, yleft, yright)
.makeQd(x,y, yleft, yright)
.makeQc(x,y, yleft, yright)
.makeDNew(x, dx, h = NULL, Cont = TRUE, standM = "sum")
.makePNew(x, dx, h = NULL, notwithLLarg = FALSE,
Cont = TRUE, myPf = NULL, pxl = NULL, pxu = NULL)
.makeQNew(x, px.l, px.u, notwithLLarg = FALSE, yL , yR, Cont = TRUE)
.mergegaps(gaps, support)
.mergegaps2(gaps1, gaps2)
.pmixfun(mixDistr, mixCoeff)
.dmixfun(mixDistr, mixCoeff, withStand = FALSE, supp = NULL)
.rmixfun(mixDistr, mixCoeff)
.qmixfun(mixDistr, mixCoeff, Cont = TRUE, pnew)
.loupmixfun(mixDistr)
.expm.d(e1)
.expm.c(e1)
.logm.d(e1)
.logm.c(e1)
.P2D (p, xx, ql, qu, ngrid = getdistrOption("DefaultNrGridPoints"))
.P2Q (p, xx, ql,qu, ngrid = getdistrOption("DefaultNrGridPoints"),
qL = -Inf, qU = Inf)
.D2P (d, xx, ql, qu, ngrid = getdistrOption("DefaultNrGridPoints"))
.Q2P (q, ngrid = getdistrOption("DefaultNrGridPoints"))
.csimpsum(fx)
.primefun(f,x, nm = NULL)
.IssueWarn(Arith,Sim)
devNew(...)Lattice)m(object)
where m is in d,p,qq(e1) is further transformedTRUE if object is continuouslog.p, lower.tail
arguments for p,q-methods of first operand?x,y, yleft, yright
(as approxfun):
if given: replaces approxfun as interpolation method for
continuos distributionsfixed = TRUE) to be matched in the
given character vector. Coerced by as.character to a
character string if pos.presubs.
Coerced to character if possible. For fixed = FALSE this
can include backreferences "\1"' to "\9" to
m with two columns,
such that t(m), interpreted as vector, is orderedUnivarDistrListnumeric; a probability vectorfunction(q, lower.tail = TRUE, log.p = FALSE
realizing slot p in a distribution object.TRUE a standardization is made such
that the sum of the values of the result evaluated at argument supp is 1numeric; if withStand is TRUE used
to standardize such that the result is a probability density.p of an object of class "AbscontDistribution"d of an object of class "AbscontDistribution"q of an object of class "AbscontDistribution"p, d to be
evaluated atgetdistrOption("TruncQuantile")-quantile of
the distribution; also, if argument xx is missing, left and right endpoint
of a regular grid of ngrid gridpoints to be used in place of xx..withArith of a distribution object,
or logically-``any'' of these slots in a collection of such objects.withSim of a distribution object,
or logically-``any'' of these slots in a collection of such objectslogical (length 1)logical (length 1)logical (length 1)Latticelogical (length 1)logicalnumeric of length 1DiscreteDistribution or
AbscontDistribution according to argument DClasscharacternumeric --- the probabilities for the grid-valuesx, y, yleft, yrightx, log = FALSEq, lower.tail = TRUE,
log.p = FALSEp, lower.tail = TRUE,
log.p = FALSElogical (same length as argument x)gaps-matrix, i.e.; a matrix m with two columns,
such that t(m), interpreted as vector, is orderedp for a mixing distribution, i.e. a function
function(q, lower.tail = TRUE, log.p = FALSE), which
is the cdf of the distributiond for a mixing distribution, i.e. a function
function(x, log = FALSE), which
is the density of the distributionq for a mixing distribution, i.e. a function
function(p, lower.tail = TRUE, log.p = FALSE), which
is the quantile function of the distributionr for a mixing distribution, i.e. a function
function(n) generating r.v.'s according to the distributionqL, the minimal value of
q(x)(0), ql, the minimal value of
q(x)(getdistrOption("TruncQuantile")), qU, the maximal value of
q(x)(1), qu, the maximal value of
q(x)(getdistrOption("TruncQuantile"), lower.tail = FALSE),
x running through the members of mixDistr in each case."DiscreteDistribution"."AbscontDistribution"..P2D{a density d as function function(x, log = FALSE)}
.P2Q{a quantile function q as function function(p,
lower.tail = TRUE, log.p = FALSE)}
.D2P, .Q2P{a cdf p as function function(q,
lower.tail = TRUE, log.p = FALSE)}
.csimpsum{a vector of evaluations of the prime function at the grid points}
.primefun{the prime function as a function}
.IssueWarn{a list with two warnings to be issued each of which may be empty}
devNew{returns the return value of the device opened, usually invisible 'NULL'}.is.vector.lattice checks whether a given vector x is equally
spaced.
.is.consistent checks whether a given support vector support is
consistent to a given lattice lattice --- with or without checking
if support is equally spaced. .make.lattice.es.vector
makes an object of class Lattice out of a given (equally spaced) vector
x.
.inArgs checks whether an argument arg is a formal argument of
fct --- not vectorized.
.isEqual checks whether p0 and p1 are equal to given
tolerance.
.isIn checks whether p0 lies in any of the intervals given by
matrix pmat to given tolerance.
.isEqual01(x) checks whether x is 0 or 1 to given tolerance.
.setEqual sets all elements of x which are equal to some element of y
up to tolerance tol, to exactly the respective element of y.
.notwithLArg checks whether object D was generated by simulations
or if its slots p,q do not have lower.tail arguments.
.getObjName returns the name of the object in the ith operand.
.discretizeP discretizes D to a grid of probabilities from
lower to upper with width h.
.fm, .fM return the smallest / biggest value in (0,1) such that
f(x) is finite; .fM2 is a variant of .fM using a
lower.tail = FALSE argument.
.makeD, .makeP, .makeQ generate slots p,d,q for
binary operations e1 /op/ e2 for a distribution object e1
and a numeric e2 ---for the moment only /op/'s
+,-,*,/ are implemented.
.plusm, .multm more specifically use .makeD, .makeP,
.makeQ to generate slots p,d,q for +, *,
respectively.
.makeDd, .makePd, .makeQd provide discrete analogues to
approxfun for interpolation at non grid-values
.makeQc is an analogue to makeQd for absolutely continuous
distributions using approxfun.
.makeDNew generates slot d for a new distribution object.
In case of a discrete distribution it produces a step function with
stepfun (using .makeDd) and standardizes to 1 by summation.
In case of a continuous distribution it produces a density function with
approxfun and standardizes to 1 by integration if the latter fails,
it uses a trapezoid rule / summation for this purpose.
.makePNew generates slot p for a new distribution object.
In case of a discrete distribution it produces a step function from
cumsum applied to dx ---or from pxl if this is given, with
stepfun (using .makePd).
In case of a continuous distribution it produces a cdf with
approxfun. In case of RtoDPQ, approxfun is replaced by
myPf which calls ecdf directly.
.makeQNew generates slot q for a new distribution object.
In case of a discrete distribution it produces a step function
(using .makeQd). Special care is taken for left continuity...
In case of a continuous distribution it produces a quantile function with
approxfun.
.isInteger, .isNatural, and .isNatural0 test for each
coordinate of argument x whether it
is integer [natural / natural or 0] or not.
.mergegaps modifies the gaps matrix of an a.c. distribution according to
the support slot of a discrete distribution; if necessary, a gap interval
[a,b] is split into [a,c],[c,b] if a.
.mergegaps2 merges two gap matrices of two a.c. distributions X1
and X2 such that in the intervals of the resulting gap matrix,
neither X1 nor X2 carries mass.
.pmixfun, .dmixfun, .rmixfun, and .qmixfun
fill the slots p, d, r, and q
of a corresponding mixing distribution according to the arguments
in mixDistr, mixCoeff.
.loupmixfun finds commun lower and upper bounds for the support of
the mixing distribution.
.expm.d,.expm.c for discrete, resp. a.c. argument e1 fill the
slots p, d, r, and q
of the transformation exp(e1) exactly.
.logm.d,.logm.c for discrete, resp. a.c. argument e1 fill the
slots p, d, r, and q
of the transformation log(e1) exactly.
For objects of class AbscontDistribution,
.P2D and .P2Q reconstruct function slots d resp.
q from function slot p
by means of function D1ss from package sfsmisc ;
and of function .makeQNew, respectively. The other way round,
.D2P and .Q2P reconstruct function slot p from
from function slots d resp. q
by means of function .makePNew and explicite numeric inversion,
respectively.
.csimpsum is used internally in .makePNew to produce
a primitive 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. .primefun is similar but more flexible and
produces the prime function as a function.
devNew opens a new device. This function is for back compatibility
with R versions < 2.8.0. AbscontDistribution,
DiscreteDistribution,
LatticeDistribution,
RtoDPQ,
RtoDPQ.d,
convpow,
operators,
plot-methods
dev.new