HuberizedDistribution: Distribution Huberization Wrapper

Description

A wrapper for huberizing any probability distribution at given limits.

Value

Returns an R6 object of class HuberizedDistribution.

Constructor

HuberizedDistribution$new(distribution, lower = NULL, upper = NULL)

Constructor Arguments

Argument	Type	Details
`distribution`	distribution	Distribution to huberize.
`lower`	numeric	Lower limit for huberization.

Public Variables

Variable	Return
`name`	Name of distribution.
`short_name`	Id of distribution.
`description`	Brief description of distribution.

Public Methods

Accessor Methods	Link
`wrappedModels(model = NULL)`	`wrappedModels`
`decorators()`	`decorators`
`traits()`	`traits`
`valueSupport()`	`valueSupport`
`variateForm()`	`variateForm`
`type()`	`type`
`properties()`	`properties`
`support()`	`support`
`symmetry()`	`symmetry`
`sup()`	`sup`
`inf()`	`inf`
`dmax()`	`dmax`
`dmin()`	`dmin`
`skewnessType()`	`skewnessType`
`kurtosisType()`	`kurtosisType`



d/p/q/r Methods	Link
`pdf(x1, ..., log = FALSE, simplify = TRUE)`	`pdf`
`cdf(x1, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE)`	`cdf`
`quantile(p, ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE)`	`quantile.Distribution`
`rand(n, simplify = TRUE)`	`rand`



Statistical Methods	Link
`prec()`	`prec`
`stdev()`	`stdev`
`median()`	`median.Distribution`
`iqr()`	`iqr`
`cor()`	`cor`



Parameter Methods	Link
`parameters(id)`	`parameters`
`getParameterValue(id, error = "warn")`	`getParameterValue`
`setParameterValue(..., lst = NULL, error = "warn")`	`setParameterValue`



Validation Methods	Link
`liesInSupport(x, all = TRUE, bound = FALSE)`	`liesInSupport`
`liesInType(x, all = TRUE, bound = FALSE)`	`liesInType`



Representation Methods	Link
`strprint(n = 2)`	`strprint`
`print(n = 2)`	`print`
`summary(full = T)`	`summary.Distribution`

Details

Huberizes a distribution at lower and upper limits, using the formula

$f_H(x) = F(x), if x \le lower$

$f_H(x) = f(x), if lower < x < upper$

$f_H(x) = F(x), if x \ge upper$

where f_H is the pdf of the truncated distribution H = Huberize(X, lower, upper) and $f_X$/$F_X$ is the pdf/cdf of the original distribution.

If lower or upper are NULL they are taken to be self$inf() and self$sup() respectively.

The pdf and cdf of the distribution are required for this wrapper, if unavailable decorate with FunctionImputation first.

Examples

Run this code

# NOT RUN {
hubBin <- HuberizedDistribution$new(
          Binomial$new(prob = 0.5, size = 10),
          lower = 2, upper = 4)
hubBin$getParameterValue("prob")
hubBin$pdf(2)

# }

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