| AbscontDistribution-class {distr} | R Documentation |
The AbscontDistribution-class is the mother-class of the classes Beta, Cauchy,
Chisq, Exp, F, Gammad, Lnorm, Logis, Norm, T, Unif and
Weibull. Further absolutely continuous distributions can be defined either by declaration of
own random number generator, density, cumulative distribution and quantile functions, or as result of a
convolution of two absolutely continuous distributions or by application of a mathematical operator to an absolutely
continuous distribution. An additional way is, to specify only the random number generator. The function RtoDPQ then
approximates the three remaining slots d, p and q by random sampling.
Objects can be created by calls of the form new("AbscontDistribution", r, d, p, q).
The result of this call is an absolutely continuous distribution.
img:"Reals": the space of the image of this distribution which has dimension 1
and the name "Real Space" param:"Parameter": the parameter of this distribution, having only
the slot name "Parameter of an absolutely continuous distribution" r:"function": generates random numbersd:"function": density functionp:"function": cumulative distribution functionq:"function": quantile function
Class "UnivariateDistribution", directly.
Class "Distribution", by class "UnivariateDistribution".
signature(.Object = "AbscontDistribution"): initialize method signature(x = "AbscontDistribution"): application of a mathematical function, e.g. sin or
exp (does not work with log!), to this absolutely continouos distributionsignature(e1 = "AbscontDistribution"): application of `-' to this absolutely continuous distributionsignature(e1 = "AbscontDistribution", e2 = "numeric"): multiplication of this absolutely continuous
distribution by an object of class `numeric'signature(e1 = "AbscontDistribution", e2 = "numeric"): division of this absolutely continuous
distribution by an object of class `numeric'signature(e1 = "AbscontDistribution", e2 = "numeric"): addition of this absolutely continuous
distribution to an object of class `numeric'signature(e1 = "AbscontDistribution", e2 = "numeric"): subtraction of an object of class `numeric' from
this absolutely continuous distribution signature(e1 = "numeric", e2 = "AbscontDistribution"): multiplication of this absolutely continuous
distribution by an object of class `numeric'signature(e1 = "numeric", e2 = "AbscontDistribution"): addition of this absolutely continuous
distribution to an object of class `numeric'signature(e1 = "numeric", e2 = "AbscontDistribution"): subtraction of this absolutely continuous
distribution from an object of class `numeric'signature(e1 = "AbscontDistribution", e2 = "AbscontDistribution"): Convolution of two absolutely
continuous distributions. The slots p, d and q are approximated by grids.signature(e1 = "AbscontDistribution", e2 = "AbscontDistribution"): Convolution of two absolutely
continuous distributions. The slots p, d and q are approximated by grids.signature(object = "AbscontDistribution"): plots density, cumulative distribution and quantile
function
Thomas Stabla statho3@web.de,
Florian Camphausen fcampi@gmx.de,
Peter Ruckdeschel Peter.Ruckdeschel@uni-bayreuth.de,
Matthias Kohl Matthias.Kohl@stamats.de
Parameter-class
UnivariateDistribution-class
Beta-class
Cauchy-class
Chisq-class
Exp-class
Fd-class
Gammad-class
Lnorm-class
Logis-class
Norm-class
Td-class
Unif-class
Weibull-class
DiscreteDistribution-class
Reals-class
RtoDPQ
N = Norm() # N is a normal distribution with mean=0 and sd=1. E = Exp() # E is an exponential distribution with rate=1. A1 = E+1 # a new absolutely continuous distributions with exact slots d, p, q A2 = A1*3 # a new absolutely continuous distributions with exact slots d, p, q A3 = N*0.9 + E*0.1 # a new absolutely continuous distribution with approximated slots d, p, q r(A3)(1) # one random number generated from this distribution, e.g. -0.7150937 d(A3)(0) # The (approximated) density for x=0 is 0.4379882. p(A3)(0) # The (approximated) probability that x <= 0 is 0.4562021. q(A3)(.1) # The (approximated) 10 percent quantile is 0.1.