R: Generic function for the computation of clipped second...

m2df {distrEx} | R Documentation

## 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 signature 'AbscontDistribution'
    m2df(object, upper,
                 lowerTruncQuantile = getdistrExOption("m2dfLowerTruncQuantile"),
                 rel.tol = getdistrExOption("m2dfRelativeTolerance"), ...)

### 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`

### Details

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

### Value

The second moment of `object` clipped at `upper` is computed.

### 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(s)

Matthias Kohl Matthias.Kohl@stamats.de

### 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})

[Package distrEx version 2.7.0 Index]