.inGaps(x,gapm)
.isReplicated(x, tol = .Machine$double.eps)
.NotInSupport(x,D)
.SingleDiscrete(x,D)
.makeLenAndOrder(x,ord)
.BinomCI.in(t,p.bi,x.i, del.i=0,D.i,n.i,alpha.i)
.BinomCI(x,p.b,D,n,alpha, silent0 = TRUE)
.BinomCI.nosym(x,p.b,D,n,alpha, silent0 = TRUE)
.q2kolmogorov(alpha,n,exact=(n<100), silent0 =" TRUE)" .q2pw(x,p.b,d,n,alpha,exact="(n<100),nosym=FALSE,">
.confqq(x,D, datax=FALSE, withConf.pw = TRUE, withConf.sim = TRUE, alpha, col.pCI, lty.pCI, lwd.pCI, pch.pCI, cex.pCI, col.sCI, lty.sCI, lwd.sCI, pch.sCI, cex.sCI, n,exact.sCI=(n<100),exact.pci=(n<100), nosym.pci =" FALSE," with.legend =" TRUE," legend.bg =" "white"," legend.pos =" "topleft"," legend.cex =" 0.8," legend.pref =" ""," legend.postf =" ""," legend.alpha =" alpha," qqb0 =" NULL," transf0="NULL," debug =" FALSE)
.deleteItemsMCL(mcl)
.distrExInstalledgaps of
an "AbscontDistribution" or "UnivarLebDecDistribution"
object.
"UnivariateDistribution"orderx.i.optim and uniroot --- the endpoints of the searched interval
are x.i+t/sqrt(n)+del.i/sqrt(n) and x.i-t/sqrt(n)+del.i/sqrt(n)."UnivariateDistribution"x.qqboundsNULL and then ignored)TRUE additional output to debug confidence bounds. silent in try-catches
within this function. x.x.x.x with entries in the
set ${0,1,2,3,4}$.length(ord."left" and "right"
with the corresponding pointwise confidence widths."left" and "right"
with the corresponding pointwise confidence widths."left" and "right"
with the corresponding pointwise confidence widths.invisible(NULL).inGaps produces a logical vector of same length as x with
entries TRUE if the corresponding component of x lies within a
gap as given by gap matrix gapm and FALSE otherwise..isReplicated produces a logical vector of same length as x with
entries TRUE if the corresponding component of x appears at least
twice within x and FALSE otherwise.
.NotInSupport produces a logical vector of same length as x with
entries TRUE if the corresponding component of x does not
lie within the support of D and FALSE otherwise.
.SingleDiscrete produces a numerical vector of same length as x with
values 0 if the corresponding component of x is discrete mass point
of D, 1 if the corresponding component of x lies within
the continuous support of D, 2 and 3
if the corresponding component of x
is a left resp. right end point of a gap of D, and 4 if
the corresponding component of x does not lie within the support of D
at all.
.makeLenAndOrder by standard recycling roules respectively by truncation
at the end, forces x to length length{ord} and then orders the
result according to ord.
.q2kolmogorov, in the finite sample version (exact==TRUE),
returns the corresponding alpha-quantile
of the exact Kolmogorov distribution multiplied by $sqrt(n)$, and
in the asymptotic version (exact==FALSE),
the the corresponding (upper) alpha-quantile
of the asymptotic Kolmogorov distribution. Doing so we make use of
C-function "pkolmogorov2x" (from ks.test in package stats)
and R-function pkstwo (again from ks.test in package stats).
.BinomCI.in in a non-vectorized form, computes,
for given t, x, $\code{alpha}$, $\code{del}$,
and for $X distributed as D$, the discrepancy
$$P(\sqrt{n} |X-x-\delta| \leq t) - \alpha$$
.BinomCI, in a vectorized form, computes,
for given x, $\code{alpha}$, $\code{del}$,
values t such that,
pointwise in x and for $X distributed as D$,
$$P(\sqrt{n} |X-x-\delta| \leq t) = \alpha$$
.BinomCI.nosym, in an outer loop, by varying del in the former
formula, tries to minimize the length of
a corresponding level alpha confidence interval containing the estimate.
.q2pw computes pointwise finite sample or asymptotic confidence widths
by means of binomial probabilities / quantiles, in the former case either
symmetric (default) or shortest asymmetric; in the asymptotic case, for
distributions without a Lebesgue density, for the corresponding
density value at the quantile appearing in the expression for the
asymptotic variance, we make an approximation of (D-E(D))/sd(D) by
the standard normal, using the density of the latter one; this latter approximation
is only available if .distrExInstalled == TRUE; otherwise the corresponding
columns will be filled with NA.
.confqq calls qqbound to compute the confidence intervals
and plots them; returns the return value of qqbound.
.deleteItemsMCL deletes arguments from a call list which
functions like plot, lines, points cannot digest;
this is necessary in the manipulation of an original call
to a specific qqplot method to pass on the ... argument
correctly to calls the mentioned functions.
.distrExInstalled is a constant logical --- TRUE if package
distrEx is installed.
ks.test, qqplot
, qqplot, qqplot