MixtureDistribution: Mixture Distribution Wrapper

Description

Wrapper used to construct a mixture of two or more distributions.

Value

Returns an R6 object of class MixtureDistribution.

Constructor

MixtureDistribution$new(distlist, weights = NULL)

Constructor Arguments

Argument	Type	Details
`distlist`	list	List of distributions.
`weights`	numeric	Vector of weights. See Details.

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()`	`strprint`
`print()`	`print`
`summary(full = T)`	`summary.Distribution`
`plot()`	Coming Soon.
`qqplot()`	Coming Soon.

Details

A Mixture Distribution is a weighted combination of two or more distributions such that for pdf/cdfs of n distribution $f_1,...,f_n$/$F_1,...,F_n$ and a given weight associated to each distribution, $w_1,...,w_n$. The pdf of the mixture distribution $M(X1,...,XN)$, $f_M$ is given by $$f_M = \sum_i (f_i)(w_i)$$ and the cdf, F_M is given by $$F_M = \sum_i (F_i)(w_i)$$

If weights are given, they should be provided as a vector of numerics. If they don't sum to one then they are normalised automatically. If NULL, they are taken to be uniform, i.e. for n distributions, $w_i = 1/n, \ \forall \ i \ \in \ [1,n]$.

Examples

Run this code

# NOT RUN {
mixture <- MixtureDistribution$new(list(Binomial$new(prob = 0.5, size = 10), Binomial$new()),
                                   weights = c(0.2,0.8))
mixture$pdf(1)
mixture$cdf(1)

# }

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