Minimum-methods {distr} R Documentation

## Methods for functions Minimum and Maximum in Package ‘distr’

### Description

Minimum and Maximum-methods

### Usage

Minimum(e1, e2, ...)
Maximum(e1, e2, ...)
## S4 method for signature 'AbscontDistribution,AbscontDistribution'
Minimum(e1,e2, ...)
## S4 method for signature 'DiscreteDistribution,DiscreteDistribution'
Minimum(e1,e2, ...)
## S4 method for signature 'AbscontDistribution,Dirac'
Minimum(e1,e2,
withSimplify = getdistrOption("simplifyD"))
## S4 method for signature 'AcDcLcDistribution,AcDcLcDistribution'
Minimum(e1,e2,
withSimplify = getdistrOption("simplifyD"))
## S4 method for signature 'AcDcLcDistribution,AcDcLcDistribution'
Maximum(e1,e2,
withSimplify = getdistrOption("simplifyD"))
## S4 method for signature 'AbscontDistribution,numeric'
Minimum(e1,e2, ...)
## S4 method for signature 'DiscreteDistribution,numeric'
Minimum(e1,e2, ...)
## S4 method for signature 'AcDcLcDistribution,numeric'
Minimum(e1,e2,
withSimplify = getdistrOption("simplifyD"))
## S4 method for signature 'AcDcLcDistribution,numeric'
Maximum(e1,e2,
withSimplify = getdistrOption("simplifyD"))


### Arguments

 e1 distribution object e2 distribution object or numeric ... further arguments (to be able to call various methods with the same arguments withSimplify logical; is result to be piped through a call to simplifyD?

### Value

the corresponding distribution of the minimum / maximum

### Methods

Minimum

signature(e1 = "AbscontDistribution", e2 = "AbscontDistribution"): returns the distribution of min(X1,X2), if X1,X2 are independent and distributed according to e1 and e2 respectively; the result is again of class "AbscontDistribution"

Minimum

signature(e1 = "DiscreteDistribution", e2 = "DiscreteDistribution"): returns the distribution of min(X1,X2), if X1,X2 are independent and distributed according to e1 and e2 respectively; the result is again of class "DiscreteDistribution"

Minimum

signature(e1 = "AbscontDistribution", e2 = "Dirac"): returns the distribution of min(X1,X2), if X1,X2 are distributed according to e1 and e2 respectively; the result is of class "UnivarLebDecDistribution"

Minimum

signature(e1 = "AcDcLcDistribution", e2 = "AcDcLcDistribution"): returns the distribution of min(X1,X2), if X1,X2 are distributed according to e1 and e2 respectively; the result is of class "UnivarLebDecDistribution"

Minimum

signature(e1 = "AcDcLcDistribution", e2 = "numeric"): if e2 = n, returns the distribution of min(X1,X2,...,Xn), if X1,X2, ..., Xn are i.i.d. according to e1; the result is of class "UnivarLebDecDistribution"

Maximum

signature(e1 = "AcDcLcDistribution", e2 = "AcDcLcDistribution"): returns the distribution of max(X1,X2), if X1,X2 are distributed according to e1 and e2 respectively; translates into -Minimum(-e1,-e2); the result is of class "UnivarLebDecDistribution"

Maximum

signature(e1 = "AcDcLcDistribution", e2 = "numeric"): if e2 = n, returns the distribution of max(X1,X2,...,Xn), if X1,X2, ..., Xn are i.i.d. according to e1; translates into -Minimum(-e1,e2); the result is of class "UnivarLebDecDistribution"

Huberize, Truncate

### Examples

## IGNORE_RDIFF_BEGIN
plot(Maximum(Unif(0,1), Minimum(Unif(0,1), Unif(0,1))))
plot(Minimum(Exp(4),4))
## IGNORE_RDIFF_END

## a sometimes lengthy example...
plot(Minimum(Norm(),Pois()))


[Package distr version 2.9.3 Index]