Distribution Convolution Wrapper

Calculates the convolution of two distribution via numerical calculations.

# S3 method for Distribution
+(x, y)

# S3 method for Distribution
-(x, y)

Arguments

x, y

Distribution

Value

Returns an R6 object of class Convolution.

Details

The convolution of two probability distributions \(X\), \(Y\) is the sum $$Z = X + Y$$ which has a pmf, $$P(Z = z) = \sum_x P(X = x)P(Y = z - x)$$ with an integration analogue for continuous distributions.

Currently distr6 supports the addition of discrete and continuous probability distributions, but only subtraction of continuous distributions.

See also

Other wrappers: DistributionWrapper, HuberizedDistribution, MixtureDistribution, ProductDistribution, TruncatedDistribution, VectorDistribution

Super classes

distr6::Distribution -> distr6::DistributionWrapper -> Convolution

Methods

Public methods

• Convolution$new()

• Convolution$clone()

Inherited methods

• distr6::Distribution$cdf()
• distr6::Distribution$confidence()
• distr6::Distribution$correlation()
• distr6::Distribution$getParameterValue()
• distr6::Distribution$iqr()
• distr6::Distribution$liesInSupport()
• distr6::Distribution$liesInType()
• distr6::Distribution$median()
• distr6::Distribution$parameters()
• distr6::Distribution$pdf()
• distr6::Distribution$prec()
• distr6::Distribution$print()
• distr6::Distribution$quantile()
• distr6::Distribution$rand()
• distr6::Distribution$setParameterValue()
• distr6::Distribution$stdev()
• distr6::Distribution$strprint()
• distr6::Distribution$summary()
• distr6::Distribution$workingSupport()
• distr6::DistributionWrapper$wrappedModels()

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Method new()

Creates a new instance of this R6 class.

Usage

Convolution$new(dist1, dist2, add = TRUE)

Arguments

dist1

([Distribution])
First Distribution in convolution, i.e. dist1 ± dist2.

dist2

([Distribution])
Second Distribution in convolution, i.e. dist1 ± dist2.

add

(logical(1))
If TRUE (default) then adds the distributions together, otherwise substracts.

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Method clone()

The objects of this class are cloneable with this method.

Usage

Convolution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

binom <- Bernoulli$new() + Bernoulli$new()
binom$pdf(2)
#> [1] 0.25
Binomial$new(size = 2)$pdf(2)
#> [1] 0.25
norm <- Normal$new(mean = 3) - Normal$new(mean = 2)
norm$pdf(1)
#> [1] 0.2820948
Normal$new(mean = 1, var = 2)$pdf(1)
#> [1] 0.2820948