Minimum-methods {distr} | R Documentation |
Minimum and Maximum-methods
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"))
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 ? |
the corresponding distribution of the minimum / maximum
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"
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"
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"
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"
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"
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"
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"
plot(Maximum(Unif(0,1), Minimum(Unif(0,1), Unif(0,1)))) plot(Minimum(Exp(4),4)) ## a sometimes lengthy example... ## Not run: plot(Minimum(Norm(),Pois()))