Description Objects from the Class Slots Extends Methods Author(s) See Also Examples
Class of Euclidean random variables.
Objects can be created by calls of the form new("EuclRandVariable", ...)
.
More frequently they are created via the generating function
EuclRandVariable
.
Map
Object of class "list"
: list of functions.
Domain
Object of class "OptionalrSpace"
:
domain of the random variable.
Range
Object of class "EuclideanSpace"
:
range of the random variable.
Class "RandVariable"
, directly.
signature(from = "EuclRandVariable", to = "EuclRandMatrix")
:
create a "EuclRandMatrix"
object from a Euclidean random variable.
signature(from = "EuclRandVariable", to = "EuclRandVarList")
:
create a "EuclRandVarList"
object from a Euclidean random variable.
signature(object = "EuclRandVariable")
:
replacement function for the slot Range
.
signature(x = "EuclRandVariable")
: generates
a new Euclidean random variable by extracting elements of
the slot Map
of x
.
signature(RandVar = "EuclRandVariable", x = "numeric", distr = "missing")
:
evaluate the slot Map
of RandVar
at x
.
signature(RandVar = "EuclRandVariable", x = "matrix", distr = "missing")
:
evaluate the slot Map
of RandVar
at rows of x
.
signature(RandVar = "EuclRandVariable", x = "numeric", distr = "Distribution")
:
evaluate the slot Map
of RandVar
at x
assuming
a probability space with distribution distr
. In case x
does not lie in the support of distr
NA
is returned.
signature(RandVar = "EuclRandVariable", x = "matrix", distr = "Distribution")
:
evaluate the slot Map
of RandVar
at rows of x
assuming a probability space with distribution distr
. For those
rows of x
which do not lie in the support of distr
NA
is returned.
signature(RandVar = "EuclRandVariable", distr = "Distribution")
:
image distribution of distr
under RandVar
. Returns
an object of class "DistrList"
.
signature(object = "EuclRandVariable")
:
dimension of the Euclidean random variable.
signature(x = "EuclRandVariable")
:
returns an object of class "EuclRandMatrix"
where the
rhe results of the functions in the slot Map
of x
are transposed.
signature(x = "matrix", y = "EuclRandVariable")
:
matrix multiplication of x
and y
. Generates
an object of class "EuclRandMatrix"
.
signature(x = "EuclRandVariable", y = "matrix")
:
matrix multiplication of x
and y
. Generates
an object of class "EuclRandMatrix"
.
signature(x = "numeric", y = "EuclRandVariable")
:
generates an object of class "EuclRandMatrix"
(1 x 1 matrix)
by multiplying (scalar/innner product) x
and y
.
signature(x = "EuclRandVariable", y = "numeric")
:
generates an object of class "EuclRandMatrix"
(1 x 1 matrix)
by multiplying (scalar/innner product) x
and y
.
signature(x = "EuclRandVariable", y = "EuclRandVariable")
:
generates an object of class "EuclRandMatrix"
(1 x 1 matrix)
by multiplying (scalar/innner product) x
and y
.
signature(x = "EuclRandVariable", y = "EuclRandMatrix")
:
matrix multiplication of x
and y
. Generates
an object of class "EuclRandMatrix"
.
signature(x = "EuclRandMatrix", y = "EuclRandVariable")
:
matrix multiplication of x
and y
. Generates
an object of class "EuclRandMatrix"
.
signature(e1 = "numeric", e2 = "EuclRandVariable")
:
Given a numeric vector e1
, a Euclidean random variable e2
and an arithmetic operator op
, the Euclidean random variable
e1 op e2
is returned.
signature(e1 = "EuclRandVariable", e2 = "numeric")
:
Given a numeric vector e2
, a Euclidean random variable e1
and an arithmetic operator op
, the Euclidean random variable
e1 op e2
is returned.
signature(e1 = "EuclRandVariable", e2 = "EuclRandVariable")
:
Given two Euclidean random variables e1
, e2
and an
arithmetic operator op
, the Euclidean random variable
e1 op e2
is returned.
signature(x = "EuclRandVariable")
:
Given a "Math"
group generic fct
, the Euclidean random
variable fct(x)
is returned.
signature(object = "UnivariateDistribution", fun = "EuclRandVariable", cond = "missing")
:
expectation of fun
under univariate distributions.
signature(object = "AbscontDistribution", fun = "EuclRandVariable", cond = "missing")
:
expectation of fun
under absolutely continuous univariate distributions.
signature(object = "DiscreteDistribution", fun = "EuclRandVariable", cond = "missing")
:
expectation of fun
under discrete univariate distributions.
signature(object = "MultivariateDistribution", fun = "EuclRandVariable", cond = "missing")
:
expectation of fun
under multivariate distributions.
signature(object = "DiscreteMVDistribution", fun = "EuclRandVariable", cond = "missing")
:
expectation of fun
under discrete multivariate distributions.
signature(object = "UnivariateCondDistribution", fun = "EuclRandVariable", cond = "numeric")
:
conditional expectation of fun
under conditional univariate distributions.
signature(object = "UnivariateCondDistribution", fun = "EuclRandVariable", cond = "numeric")
:
conditional expectation of fun
under absolutely continuous conditional univariate distributions.
signature(object = "UnivariateCondDistribution", fun = "EuclRandVariable", cond = "numeric")
:
conditional expectation of fun
under discrete conditional univariate distributions.
Matthias Kohl Matthias.Kohl@stamats.de
EuclRandVariable
, RandVariable-class
,
EuclRandMatrix-class
, EuclRandVarList-class
,
Distribution-class
, Arith
,
Math
, E
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | L1 <- list(function(x){x}, function(x){x^2}, function(x){x^3}, function(x){x^4})
L2 <- list(function(x){exp(x)}, function(x){abs(x)},
function(x){sin(x)}, function(x){floor(x)})
R1 <- new("EuclRandVariable", Map = L1, Domain = Reals(), Range = Reals())
dimension(R1)
Map(R1)
Range(R1)
R1[2]
Map(R1[3])
Map(R1[c(1,2,4)])
Map(R1[2:4])
set.seed(123)
evalRandVar(R1, rnorm(1))
x <- as.matrix(rnorm(10))
res.R1 <- evalRandVar(R1, x)
res.R1[2,,] # results for Map(R1)[[2]](x)
res.R1[2,1,] # results for Map(R1)[[2]](x[1,])
R2 <- EuclRandVariable(L2, Domain = Reals(), dimension = 1)
dimension(R2)
DL1 <- imageDistr(R2, Norm())
plot(DL1)
Domain(R2) <- EuclideanSpace(dimension = 2)
Range(R2) <- EuclideanSpace(dimension = 2)
dimension(R2)
(X <- matrix(c(x, rnorm(10)), ncol = 2))
res.R2 <- evalRandVar(R2, X)
res.R2[3,,1] # results for Map(R2)[[3]](X[,1])
Map(log(abs(R2))) # "Math" group generic
# "Arith" group generic
Map(3 + R1)
Map(c(1,3,5) * R1)
try(1:5 * R1) # error
Map(1:2 * R2)
Map(R2 - 5)
Map(R1 ^ R1)
|
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