distrEx (version 2.9.5)

m2df: Generic function for the computation of clipped second moments

Description

Generic function for the computation of clipped second moments. The moments are clipped at upper.

Usage

m2df(object, upper, ...)
# S4 method for AbscontDistribution
m2df(object, upper, 
             lowerTruncQuantile = getdistrExOption("m2dfLowerTruncQuantile"),
             rel.tol = getdistrExOption("m2dfRelativeTolerance"), ...)

Value

The second moment of object clipped at upper is computed.

Arguments

object

object of class "Distribution"

upper

clipping bound

rel.tol

relative tolerance for distrExIntegrate.

lowerTruncQuantile

lower quantile for quantile based integration range.

...

additional arguments to E

Methods

object = "UnivariateDistribution":

uses call E(object, upp=upper, fun = function, ...).

object = "AbscontDistribution":

clipped second moment for absolutely continuous univariate distributions which is computed using integrate.

object = "LatticeDistribution":

clipped second moment for discrete univariate distributions which is computed using support and sum.

object = "AffLinDistribution":

clipped second moment for affine linear distributions which is computed on basis of slot X0.

object = "Binom":

clipped second moment for Binomial distributions which is computed using pbinom.

object = "Pois":

clipped second moment for Poisson distributions which is computed using ppois.

object = "Norm":

clipped second moment for normal distributions which is computed using dnorm and pnorm.

object = "Exp":

clipped second moment for exponential distributions which is computed using pexp.

object = "Chisq":

clipped second moment for ${\chi }^2$ distributions which is computed using pchisq.

Author

Matthias Kohl

Details

The precision of the computations can be controlled via certain global options; cf. distrExOptions.

See Also

m2df-methods, E-methods

Examples

# standard normal distribution
N1 <- Norm()
m2df(N1, 0)

# Poisson distribution
P1 <- Pois(lambda=2)
m2df(P1, 3)
m2df(P1, 3, fun = function(x)sin(x))

# absolutely continuous distribution
D1 <- Norm() + Exp() # convolution
m2df(D1, 2)
m2df(D1, Inf)
E(D1, function(x){x^2})