| CvMDist {distrEx} | R Documentation |
Generic function for the computation of the Cramer - von Mises distance d_{mu} of two distributions P and Q where the distributions are defined on a finite-dimensional Euclidean space (R^m, B^m) with B^m the Borel-sigma-algebra on R^m. The Cramer - von Mises distance is defined as
d_{mu}(P,Q)^2=\int (P({y in R^m | y <= x})-Q({y in R^m | y <= x}))^2 mu(dx)
where <= is coordinatewise on R^m.
CvMDist(e1, e2, ...) ## S4 method for signature 'UnivariateDistribution,UnivariateDistribution' CvMDist(e1, e2, mu = e1, useApply = FALSE, ...) ## S4 method for signature 'numeric,UnivariateDistribution' CvMDist(e1, e2, mu = e1, ...)
e1 |
object of class |
e2 |
object of class |
... |
further arguments to be used e.g. by |
useApply |
logical; to be passed to |
mu |
object of class |
Cramer - von Mises distance of e1 and e2
Cramer - von Mises distance of two univariate distributions.
Cramer - von Mises distance between the empirical formed from a data set (e1) and a univariate distribution.
Matthias Kohl Matthias.Kohl@stamats.de,
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.
ContaminationSize, TotalVarDist,
HellingerDist, KolmogorovDist,
Distribution-class
CvMDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
mixCoeff=c(0.2,0.8)))
CvMDist(Norm(), UnivarMixingDistribution(Norm(1,2),Norm(0.5,3),
mixCoeff=c(0.2,0.8)),mu=Norm())
CvMDist(Norm(), Td(10))
CvMDist(Norm(mean = 50, sd = sqrt(25)), Binom(size = 100))
CvMDist(Pois(10), Binom(size = 20))
CvMDist(rnorm(100),Norm())
CvMDist((rbinom(50, size = 20, prob = 0.5)-10)/sqrt(5), Norm())
CvMDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5))
CvMDist(rbinom(50, size = 20, prob = 0.5), Binom(size = 20, prob = 0.5), mu = Pois())