DiscreteDistribution {distr} | R Documentation |
Generates an object of class "DiscreteDistribution"
DiscreteDistribution(supp, prob, .withArith=FALSE, .withSim=FALSE, .lowerExact = TRUE, .logExact = FALSE, .DistrCollapse = getdistrOption("DistrCollapse"), .DistrCollapse.Unique.Warn = getdistrOption("DistrCollapse.Unique.Warn"), .DistrResolution = getdistrOption("DistrResolution")) DiscreteDistribution(supp)
supp |
numeric vector which forms the support of the discrete distribution. |
prob |
vector of probability weights for the
elements of supp . |
.withArith |
normally not set by the user, but if determining the entries supp , prob
distributional arithmetics was involved, you may set this to TRUE . |
.withSim |
normally not set by the user, but if determining the entries supp , prob
simulations were involved, you may set this to TRUE . |
.lowerExact |
normally not set by the user: whether the lower.tail=FALSE
part is calculated exactly, avoing a ``1-. ''. |
.logExact |
normally not set by the user: whether in determining slots d,p,q ,
we make particular use of a logarithmic representation to enhance accuracy. |
.DistrCollapse |
controls whether in generating a new discrete
distribution, support points closer together than .DistrResolution are
collapsed. |
.DistrCollapse.Unique.Warn |
controls whether there is a warning
whenever collapsing occurs or when two points are collapsed by a call to
unique() (default behaviour if .DistrCollapse is FALSE ) |
.DistrResolution |
minimal spacing between two mass points in a discrete distribution |
If prob
is missing, all elements in supp
are equally weighted.
Object of class "DiscreteDistribution"
Working with a computer, we use a finite interval as support which
carries at least mass 1-getdistrOption("TruncQuantile")
.
Also, we require that support points have distance at least
.DistrResoltion
, if this condition fails,
upon a suggestion by Jacob van Etten, jacobvanetten@yahoo.com,
we use the global option .DistrCollapse
to
decide whether we use collapsing or not. If we do so, we collapse support
points if they are too close to each other, taking
the (left most) median among them as new support point which accumulates
all the mass of the collapsed points.
With .DistrCollapse==FALSE
, we at least collapse
points according to the result of unique()
, and if after this
collapsing, the minimal distance is less than .DistrResoltion
,
we throw an error. By .DistrCollapse.Unique.Warn
,
we control, whether we throw a warning upon collapsing or not.
Peter Ruckdeschel Peter.Ruckdeschel@itwm.fraunhofer.de,
Matthias Kohl Matthias.Kohl@stamats.de
DiscreteDistribution-class
AbscontDistribution-class
RtoDPQ.d
# Dirac-measure at 0 D1 <- DiscreteDistribution(supp = 0) D1 # simple discrete distribution D2 <- DiscreteDistribution(supp = c(1:5), prob = c(0.1, 0.2, 0.3, 0.2, 0.2)) D2 plot(D2)