VectorDistribution: Vectorise Distributions

Description

A wrapper for creating a vector of distributions.

Value

Returns an R6 object of class VectorDistribution.

Constructor

VectorDistribution$new(distlist = NULL, distribution = NULL, params = NULL, name = NULL, short_name = NULL, description = NULL)

Constructor Arguments

Argument	Type	Details
`distlist`	list	List of distributions.
`distribution`	distribution	Distribution to wrap.
`params`	a R object	Either list of parameters or matrix-type frame, see examples.
`name`	list	Optional new name for distribution.
`short_name`	list	Optional new short_name for distribution.
`description`	list	Optional new description for distribution.

Constructor Details

A vector distribution can either be constructed by a list of distributions passed to distlist or by passing the name of a distribution implemented in distr6 to distribution, as well as a list or table of parameters to params. The former case provides more flexibility in the ability to use multiple distributions but the latter is useful for quickly combining many distributions of the same type. See examples.

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`
`plot()`	Coming Soon.
`qqplot()`	Coming Soon.

Details

A vector of distributions has the following relationship $$f_V(X1 = x1,...,XN = xN) = f_{X1}(x1), ..., f_{XN}(xn)$$ $$F_V(X1 = x1,...,XN = xN) = F_{X1}(x1), ..., F_{XN}(xn)$$ where $f_V$/$F_V$ is the pdf/cdf of the vector of distributions $V$ and $X1,...,XN$ are distributions.

Examples

Run this code

# NOT RUN {
vecBin <- VectorDistribution$new(list(Binomial$new(prob = 0.5,
                           size = 10), Normal$new(mean = 15)))
vecBin$pdf(x1 = 2, x2 =3)
vecBin$cdf(1:5, 12:16)
vecBin$quantile(c(0.1,0.2),c(0.3,0.4))
vecBin$rand(10)

vecBin = VectorDistribution$new(distribution = Binomial,
       params = list(list(prob = 0.1, size = 2),
                   list(prob = 0.6, size = 4),
                   list(prob = 0.2, size = 6)))
vecBin$pdf(x1=1,x2=2,x3=3)
vecBin$cdf(x1=1,x2=2,x3=3)
vecBin$rand(10)

#Equivalently
vecBin = VectorDistribution$new(distribution = Binomial,
       params = data.table::data.table(prob = c(0.1,0.6,0.2), size = c(2,4,6)))
vecBin$pdf(x1=1,x2=2,x3=3)
vecBin$cdf(x1=1,x2=2,x3=3)
vecBin$rand(10)

# }

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