| E {distrEx} | R Documentation |
Generic function for the computation of (conditional) expectations.
E(object, fun, cond, ...) ## S4 method for signature 'UnivariateDistribution, ## function, missing': E(object, fun, cond, useApply = TRUE, ...) ## S4 method for signature 'AbscontDistribution, function, ## missing': E(object, fun, cond, useApply = TRUE, ...) ## S4 method for signature 'DiscreteDistribution, function, ## missing': E(object, fun, cond, useApply = TRUE, ...) ## S4 method for signature 'AffLinDistribution, missing, ## missing': E(object) ## S4 method for signature 'MultivariateDistribution, ## function, missing': E(object, fun, cond, useApply = TRUE, ...) ## S4 method for signature 'DiscreteMVDistribution, ## function, missing': E(object, fun, cond, useApply = TRUE, ...) ## S4 method for signature 'AbscontCondDistribution, ## missing, numeric': E(object, cond, useApply = TRUE) ## S4 method for signature 'DiscreteCondDistribution, ## missing, numeric': E(object, cond, useApply = TRUE) ## S4 method for signature 'UnivariateCondDistribution, ## function, numeric': E(object, fun, cond, withCond = FALSE, useApply = TRUE, ...) ## S4 method for signature 'AbscontCondDistribution, ## function, numeric': E(object, fun, cond, withCond = FALSE, useApply = TRUE, ...) ## S4 method for signature 'DiscreteCondDistribution, ## function, numeric': E(object, fun, cond, withCond = FALSE, useApply = TRUE, ...) ## S4 method for signature 'DiscreteCondDistribution, ## function, numeric': E(object, fun, cond, withCond = FALSE, useApply = TRUE, ...) ## S4 method for signature 'Beta, missing, missing': E(object, fun, cond) ## S4 method for signature 'Binom, missing, missing': E(object, fun, cond) ## S4 method for signature 'Cauchy, missing, missing': E(object, fun, cond) ## S4 method for signature 'Chisq, missing, missing': E(object, fun, cond) ## S4 method for signature 'Dirac, missing, missing': E(object, fun, cond) ## S4 method for signature 'DExp, missing, missing': E(object, fun, cond) ## S4 method for signature 'Exp, missing, missing': E(object, fun, cond) ## S4 method for signature 'Fd, missing, missing': E(object, fun, cond) ## S4 method for signature 'Gammad, missing, missing': E(object, fun, cond) ## S4 method for signature 'Geom, missing, missing': E(object, fun, cond) ## S4 method for signature 'Hyper, missing, missing': E(object, fun, cond) ## S4 method for signature 'Logis, missing, missing': E(object, fun, cond) ## S4 method for signature 'Lnorm, missing, missing': E(object, fun, cond) ## S4 method for signature 'Nbinom, missing, missing': E(object, fun, cond) ## S4 method for signature 'Norm, missing, missing': E(object, fun, cond) ## S4 method for signature 'Pois, missing, missing': E(object, fun, cond) ## S4 method for signature 'Unif, missing, missing': E(object, fun, cond) ## S4 method for signature 'Td, missing, missing': E(object, fun, cond) ## S4 method for signature 'Weibull, missing, missing': E(object, fun, cond)
object |
object of class "Distribution" |
fun |
if missing the (conditional) expectation is computed
else the (conditional) expection of fun is computed. |
cond |
if not missing the conditional expectation
given cond is computed. |
... |
additional arguments to fun |
useApply |
logical: should sapply, respectively apply
be used to evaluate fun. |
withCond |
logical: is cond in the argument list of fun. |
The precision of the computations can be controlled via
certain global options; cf. distrExOptions.
The (conditional) expectation is computed.
distrExIntegrate. support
and sum.support and sum.fun under univariate distributions using
crude Monte-Carlo integration. fun under absolutely continuous
univariate distributions using distrExIntegrate. fun under discrete univariate
distributions using support and sum. fun under discrete multivariate
distributions. The computation is based on support and sum. cond.
The integral is computed using crude Monte-Carlo integration. cond. The computation
is based on distrExIntegrate. cond. The computation is based
on support and sum. fun under univariate conditional distributions
given cond. The integral is computed using crude Monte-Carlo integration. fun under absolutely continuous,
univariate conditional distributions given cond. The
computation is based on distrExIntegrate. fun under discrete, univariate
conditional distributions given cond. The computation is
based on support and sum. Matthias Kohl Matthias.Kohl@stamats.de and Peter Ruckdeschel peter.ruckdeschel@uni-bayreuth.de
distrExIntegrate, m1df, m2df,
Distribution-class
# mean of Exp(1) distribution
E <- Exp()
E(E) ## uses explicit terms
E(as(E,"AbscontDistribution")) ## uses numerical integration
E(as(E,"UnivariateDistribution")) ## uses simulations
E(E, fun = function(x){2*x^2}) ## uses simulations
# the same operator for discrete distributions:
P <- Pois(lambda=2)
E(P) ## uses explicit terms
E(as(P,"DiscreteDistribution")) ## uses sums
E(as(P,"UnivariateDistribution")) ## uses simulations
E(P, fun = function(x){2*x^2}) ## uses simulations
# second moment of N(1,4)
E(Norm(mean=1, sd=2), fun = function(x){x^2})
E(Norm(mean=1, sd=2), fun = function(x){x^2}, useApply = FALSE)
# conditional distribution of a linear model
D1 <- LMCondDistribution(theta = 1)
E(D1, cond = 1)
E(Norm(mean=1))
E(D1, function(x){x^2}, cond = 1)
E(Norm(mean=1), fun = function(x){x^2})
E(D1, function(x, cond){cond*x^2}, cond = 2, withCond = TRUE, useApply = FALSE)
E(Norm(mean=2), function(x){2*x^2})