prob-methods: Methods for Function prob in Package `distr'
Methods
- prob
signature(object = "BinomParameter"): returns the slot prop of the parameter of the distribution - prob<-
signature(object = "BinomParameter"): modifies the slot prob of the parameter of the distribution - prob
signature(object = "Binom"): returns the slot prop of the parameter of the distribution - prob<-
signature(object = "Binom"): modifies the slot prob of the parameter of the distribution - prob
signature(object = "NbinomParameter"): returns the slot prop of the parameter of the distribution - prob<-
signature(object = "NbinomParameter"): modifies the slot prob of the parameter of the distribution - prob
signature(object = "Nbinom"): returns the slot prop of the parameter of the distribution - prob<-
signature(object = "Nbinom"): modifies the slot prob of the parameter of the distribution - prob
signature(object = "GeomParameter"): returns the slot prop of the parameter of the distribution (deprecated from 1.9 on)- prob<-
signature(object = "GeomParameter"): modifies the slot prob of the parameter of the distribution (deprecated from 1.9 on)- prob
signature(object = "Geom"): returns the slot prop of the parameter of the distribution - prob<-
signature(object = "Geom"): modifies the slot prob of the parameter of the distribution - prob
signature(object = "DiscreteDistribution"): returns the
(named) vector of probabilities for the support points of the distribution. - prob<-
signature(object = "DiscreteDistribution"):
generates a new object of class "DiscreteDistribution" with
the same support as object as well as the same
.withSim, .withArith, .lowerExact,
.logExact slots. - prob
signature(object = "UnivarLebDecDistribution"): returns a
$2 x n$ matrix where n is the length of the support of
the discrete part of the distribution; the first row named "cond"
gives the vector of probabilities for the support points of the discrete part
of the distribution (i.e.; conditional on being in the discrete part), the second
row named "abs" is like the first one but multiplied with discreteWeight
of the distribution, hence gives the absolute probabilities of the support points;
the columns are named by the support values.