Distribution: Generalised Distribution Object
In distr6: The Complete R6 Probability Distributions Interface
Distribution R Documentation
Generalised Distribution Object
Description
A generalised distribution object for defining custom probability distributions
as well as serving as the parent class to specific, familiar distributions.
Value
Returns R6 object of class Distribution.
Public fields
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
Active bindings
decoratorsReturns decorators currently used to decorate the distribution.
traitsReturns distribution traits.
valueSupportDeprecated, use $traits$valueSupport.
variateFormDeprecated, use $traits$variateForm.
typeDeprecated, use $traits$type.
propertiesReturns distribution properties, including skewness type and symmetry.
supportDeprecated, use $properties$type.
symmetryDeprecated, use $properties$symmetry.
supReturns supremum (upper bound) of the distribution support.
infReturns infimum (lower bound) of the distribution support.
dmaxReturns maximum of the distribution support.
dminReturns minimum of the distribution support.
kurtosisTypeDeprecated, use $properties$kurtosis.
skewnessTypeDeprecated, use $properties$skewness.
Methods
Public methods
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Method new()
Creates a new instance of this R6 class.
Usage
Distribution$new(
name = NULL,
short_name = NULL,
type,
support = NULL,
symmetric = FALSE,
pdf = NULL,
cdf = NULL,
quantile = NULL,
rand = NULL,
parameters = NULL,
decorators = NULL,
valueSupport = NULL,
variateForm = NULL,
description = NULL,
.suppressChecks = FALSE
)
Arguments
namecharacter(1)
Full name of distribution.
short_namecharacter(1)
Short name of distribution for printing.
type([set6::Set])
Distribution type.
support([set6::Set])
Distribution support.
symmetriclogical(1)
Symmetry type of the distribution.
pdffunction(1)
Probability density function of the distribution. At least one of pdf and
cdf must be provided.
cdffunction(1)
Cumulative distribution function of the distribution. At least one of pdf and
cdf must be provided.
quantilefunction(1)
Quantile (inverse-cdf) function of the distribution.
randfunction(1)
Simulation function for drawing random samples from the distribution.
parameters([param6::ParameterSet])
Parameter set for defining the parameters in the distribution, which should be set before
construction.
decorators(character())
Decorators to add to the distribution during construction.
valueSupport(character(1))
The support type of the distribution, one of "discrete", "continuous", "mixture".
If NULL, determined automatically.
variateForm(character(1))
The variate type of the distribution, one of "univariate", "multivariate", "matrixvariate".
If NULL, determined automatically.
description(character(1))
Optional short description of the distribution.
.suppressChecks(logical(1))
Used internally.
Method strprint()
Printable string representation of the Distribution. Primarily used internally.
Usage
Distribution$strprint(n = 2)
Arguments
n(integer(1))
Number of parameters to display when printing.
Method print()
Prints the Distribution.
Usage
Distribution$print(n = 2, ...)
Arguments
n(integer(1))
Passed to $strprint.
...ANY
Unused. Added for consistency.
Method summary()
Prints a summary of the Distribution.
Usage
Distribution$summary(full = TRUE, ...)
Arguments
full(logical(1))
If TRUE (default) prints a long summary of the distribution,
otherwise prints a shorter summary.
...ANY
Unused. Added for consistency.
Method parameters()
Returns the full parameter details for the supplied parameter.
Usage
Distribution$parameters(id = NULL)
Arguments
idDeprecated.
Method getParameterValue()
Returns the value of the supplied parameter.
Usage
Distribution$getParameterValue(id, error = "warn")
Arguments
idcharacter()
id of parameter value to return.
error(character(1))
If "warn" then returns a warning on error, otherwise breaks if "stop".
Method setParameterValue()
Sets the value(s) of the given parameter(s).
Usage
Distribution$setParameterValue(
...,
lst = list(...),
error = "warn",
resolveConflicts = FALSE
)
Arguments
...ANY
Named arguments of parameters to set values for. See examples.
lst(list(1))
Alternative argument for passing parameters. List names should be parameter names and list values
are the new values to set.
error(character(1))
If "warn" then returns a warning on error, otherwise breaks if "stop".
resolveConflicts(logical(1))
If FALSE (default) throws error if conflicting parameterisations are provided, otherwise
automatically resolves them by removing all conflicting parameters.
Examples
b = Binomial$new()
b$setParameterValue(size = 4, prob = 0.4)
b$setParameterValue(lst = list(size = 4, prob = 0.4))
Method pdf()
For discrete distributions the probability mass function (pmf) is returned, defined as
p_X(x) = P(X = x)
for continuous distributions the probability density function (pdf), f_X, is returned
f_X(x) = P(x < X ≤ x + dx)
for some infinitesimally small dx.
If available a pdf will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$pdf(..., log = FALSE, simplify = TRUE, data = NULL)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need
to be named. The length of each argument corresponds to the number of points to evaluate,
the number of arguments corresponds to the number of variables in the distribution.
See examples.
log(logical(1))
If TRUE returns the logarithm of the probabilities. Default is FALSE.
simplifylogical(1)
If TRUE (default) simplifies the return if possible to a numeric, otherwise returns a
data.table::data.table.
dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number
of points to evaluate and columns correspond with number of variables to evaluate. In the special
case of VectorDistributions of multivariate distributions, then the third dimension corresponds
to the distribution in the vector to evaluate.
Examples
b <- Binomial$new()
b$pdf(1:10)
b$pdf(1:10, log = TRUE)
b$pdf(data = matrix(1:10))
mvn <- MultivariateNormal$new()
mvn$pdf(1, 2)
mvn$pdf(1:2, 3:4)
mvn$pdf(data = matrix(1:4, nrow = 2), simplify = FALSE)
Method cdf()
The (lower tail) cumulative distribution function, F_X, is defined as
F_X(x) = P(X ≤ x)
If lower.tail is FALSE then 1 - F_X(x) is returned, also known as the
survival function.
If available a cdf will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$cdf(
...,
lower.tail = TRUE,
log.p = FALSE,
simplify = TRUE,
data = NULL
)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need
to be named. The length of each argument corresponds to the number of points to evaluate,
the number of arguments corresponds to the number of variables in the distribution.
See examples.
lower.tail(logical(1))
If TRUE (default), probabilities are X <= x, otherwise, P(X > x).
log.p(logical(1))
If TRUE returns the logarithm of the probabilities. Default is FALSE.
simplifylogical(1)
If TRUE (default) simplifies the return if possible to a numeric, otherwise returns a
data.table::data.table.
dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number
of points to evaluate and columns correspond with number of variables to evaluate. In the special
case of VectorDistributions of multivariate distributions, then the third dimension corresponds
to the distribution in the vector to evaluate.
Examples
b <- Binomial$new()
b$cdf(1:10)
b$cdf(1:10, log.p = TRUE, lower.tail = FALSE)
b$cdf(data = matrix(1:10))
Method quantile()
The quantile function, q_X, is the inverse cdf, i.e.
q_X(p) = F^(-1)_X(p) = inf{x ε R: F_X(x) ≥ p}
#nolint
If lower.tail is FALSE then q_X(1-p) is returned.
If available a quantile will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$quantile(
...,
lower.tail = TRUE,
log.p = FALSE,
simplify = TRUE,
data = NULL
)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need
to be named. The length of each argument corresponds to the number of points to evaluate,
the number of arguments corresponds to the number of variables in the distribution.
See examples.
lower.tail(logical(1))
If TRUE (default), probabilities are X <= x, otherwise, P(X > x).
log.p(logical(1))
If TRUE returns the logarithm of the probabilities. Default is FALSE.
simplifylogical(1)
If TRUE (default) simplifies the return if possible to a numeric, otherwise returns a
data.table::data.table.
dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number
of points to evaluate and columns correspond with number of variables to evaluate. In the special
case of VectorDistributions of multivariate distributions, then the third dimension corresponds
to the distribution in the vector to evaluate.
Examples
b <- Binomial$new()
b$quantile(0.42)
b$quantile(log(0.42), log.p = TRUE, lower.tail = TRUE)
b$quantile(data = matrix(c(0.1,0.2)))
Method rand()
The rand function draws n simulations from the distribution.
If available simulations will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$rand(n, simplify = TRUE)
Arguments
n(numeric(1))
Number of points to simulate from the distribution. If length greater than 1, then
n <- length(n),
simplifylogical(1)
If TRUE (default) simplifies the return if possible to a numeric, otherwise returns a
data.table::data.table.
Examples
b <- Binomial$new()
b$rand(10)
mvn <- MultivariateNormal$new()
mvn$rand(5)
Method prec()
Returns the precision of the distribution as 1/self$variance().
Usage
Distribution$prec()
Method stdev()
Returns the standard deviation of the distribution as sqrt(self$variance()).
Usage
Distribution$stdev()
Method median()
Returns the median of the distribution. If an analytical expression is available
returns distribution median, otherwise if symmetric returns self$mean, otherwise
returns self$quantile(0.5).
Usage
Distribution$median(na.rm = NULL, ...)
Arguments
na.rm(logical(1))
Ignored, addded for consistency.
...ANY
Ignored, addded for consistency.
Method iqr()
Inter-quartile range of the distribution. Estimated as
self$quantile(0.75) - self$quantile(0.25).
Usage
Distribution$iqr()
Method confidence()
1 or 2-sided confidence interval around distribution.
Usage
Distribution$confidence(alpha = 0.95, sides = "both", median = FALSE)
Arguments
alpha(numeric(1))
Level of confidence, default is 95%
sides(character(1))
One of 'lower', 'upper' or 'both'
median(logical(1))
If TRUE also returns median
Method correlation()
If univariate returns 1, otherwise returns the distribution correlation.
Usage
Distribution$correlation()
Method liesInSupport()
Tests if the given values lie in the support of the distribution.
Uses [set6::Set]$contains.
Usage
Distribution$liesInSupport(x, all = TRUE, bound = FALSE)
Arguments
xANY
Values to test.
alllogical(1)
If TRUE (default) returns TRUE if all x are in the distribution,
otherwise returns a vector of logicals corresponding to each element in
x.
boundlogical(1)
If TRUE then tests if x lie between the upper and lower bounds of the distribution,
otherwise tests if x lie between the maximum and minimum of the distribution.
Method liesInType()
Tests if the given values lie in the type of the distribution.
Uses [set6::Set]$contains.
Usage
Distribution$liesInType(x, all = TRUE, bound = FALSE)
Arguments
xANY
Values to test.
alllogical(1)
If TRUE (default) returns TRUE if all x are in the distribution,
otherwise returns a vector of logicals corresponding to each element in
x.
boundlogical(1)
If TRUE then tests if x lie between the upper and lower bounds of the distribution,
otherwise tests if x lie between the maximum and minimum of the distribution.
Method workingSupport()
Returns an estimate for the computational support of the distribution.
If an analytical cdf is available, then this is computed as the smallest interval
in which the cdf lower bound is 0 and the upper bound is 1, bounds are incremented in
10^i intervals. If no analytical cdf is available, then this is computed as the smallest
interval in which the lower and upper bounds of the pdf are 0, this is much less precise
and is more prone to error. Used primarily by decorators.
Usage
Distribution$workingSupport()
Method clone()
The objects of this class are cloneable with this method.
Usage
Distribution$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `Distribution$setParameterValue`
## ------------------------------------------------
b = Binomial$new()
b$setParameterValue(size = 4, prob = 0.4)
b$setParameterValue(lst = list(size = 4, prob = 0.4))
## ------------------------------------------------
## Method `Distribution$pdf`
## ------------------------------------------------
b <- Binomial$new()
b$pdf(1:10)
b$pdf(1:10, log = TRUE)
b$pdf(data = matrix(1:10))
mvn <- MultivariateNormal$new()
mvn$pdf(1, 2)
mvn$pdf(1:2, 3:4)
mvn$pdf(data = matrix(1:4, nrow = 2), simplify = FALSE)
## ------------------------------------------------
## Method `Distribution$cdf`
## ------------------------------------------------
b <- Binomial$new()
b$cdf(1:10)
b$cdf(1:10, log.p = TRUE, lower.tail = FALSE)
b$cdf(data = matrix(1:10))
## ------------------------------------------------
## Method `Distribution$quantile`
## ------------------------------------------------
b <- Binomial$new()
b$quantile(0.42)
b$quantile(log(0.42), log.p = TRUE, lower.tail = TRUE)
b$quantile(data = matrix(c(0.1,0.2)))
## ------------------------------------------------
## Method `Distribution$rand`
## ------------------------------------------------
b <- Binomial$new()
b$rand(10)
mvn <- MultivariateNormal$new()
mvn$rand(5)
distr6 documentation built on March 28, 2022, 1:05 a.m.
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| Distribution | R Documentation |
Generalised Distribution Object
Description
A generalised distribution object for defining custom probability distributions as well as serving as the parent class to specific, familiar distributions.
Value
Returns R6 object of class Distribution.
Public fields
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
Active bindings
decoratorsReturns decorators currently used to decorate the distribution.
traitsReturns distribution traits.
valueSupportDeprecated, use
$traits$valueSupport.variateFormDeprecated, use
$traits$variateForm.typeDeprecated, use
$traits$type.propertiesReturns distribution properties, including skewness type and symmetry.
supportDeprecated, use
$properties$type.symmetryDeprecated, use
$properties$symmetry.supReturns supremum (upper bound) of the distribution support.
infReturns infimum (lower bound) of the distribution support.
dmaxReturns maximum of the distribution support.
dminReturns minimum of the distribution support.
kurtosisTypeDeprecated, use
$properties$kurtosis.skewnessTypeDeprecated, use
$properties$skewness.
Methods
Public methods
Method new()
Creates a new instance of this R6 class.
Usage
Distribution$new( name = NULL, short_name = NULL, type, support = NULL, symmetric = FALSE, pdf = NULL, cdf = NULL, quantile = NULL, rand = NULL, parameters = NULL, decorators = NULL, valueSupport = NULL, variateForm = NULL, description = NULL, .suppressChecks = FALSE )
Arguments
namecharacter(1)
Full name of distribution.short_namecharacter(1)
Short name of distribution for printing.type([set6::Set])
Distribution type.support([set6::Set])
Distribution support.symmetriclogical(1)
Symmetry type of the distribution.pdffunction(1)
Probability density function of the distribution. At least one ofpdfandcdfmust be provided.cdffunction(1)
Cumulative distribution function of the distribution. At least one ofpdfandcdfmust be provided.quantilefunction(1)
Quantile (inverse-cdf) function of the distribution.randfunction(1)
Simulation function for drawing random samples from the distribution.parameters([param6::ParameterSet])
Parameter set for defining the parameters in the distribution, which should be set before construction.decorators(character())
Decorators to add to the distribution during construction.valueSupport(character(1))
The support type of the distribution, one of"discrete", "continuous", "mixture". IfNULL, determined automatically.variateForm(character(1))
The variate type of the distribution, one of"univariate", "multivariate", "matrixvariate". IfNULL, determined automatically.description(character(1))
Optional short description of the distribution..suppressChecks(logical(1))
Used internally.
Method strprint()
Printable string representation of the Distribution. Primarily used internally.
Usage
Distribution$strprint(n = 2)
Arguments
n(integer(1))
Number of parameters to display when printing.
Method print()
Prints the Distribution.
Usage
Distribution$print(n = 2, ...)
Arguments
n(integer(1))
Passed to$strprint....ANY
Unused. Added for consistency.
Method summary()
Prints a summary of the Distribution.
Usage
Distribution$summary(full = TRUE, ...)
Arguments
full(logical(1))
IfTRUE(default) prints a long summary of the distribution, otherwise prints a shorter summary....ANY
Unused. Added for consistency.
Method parameters()
Returns the full parameter details for the supplied parameter.
Usage
Distribution$parameters(id = NULL)
Arguments
idDeprecated.
Method getParameterValue()
Returns the value of the supplied parameter.
Usage
Distribution$getParameterValue(id, error = "warn")
Arguments
idcharacter()
id of parameter value to return.error(character(1))
If"warn"then returns a warning on error, otherwise breaks if"stop".
Method setParameterValue()
Sets the value(s) of the given parameter(s).
Usage
Distribution$setParameterValue( ..., lst = list(...), error = "warn", resolveConflicts = FALSE )
Arguments
...ANY
Named arguments of parameters to set values for. See examples.lst(list(1))
Alternative argument for passing parameters. List names should be parameter names and list values are the new values to set.error(character(1))
If"warn"then returns a warning on error, otherwise breaks if"stop".resolveConflicts(logical(1))
IfFALSE(default) throws error if conflicting parameterisations are provided, otherwise automatically resolves them by removing all conflicting parameters.
Examples
b = Binomial$new() b$setParameterValue(size = 4, prob = 0.4) b$setParameterValue(lst = list(size = 4, prob = 0.4))
Method pdf()
For discrete distributions the probability mass function (pmf) is returned, defined as
p_X(x) = P(X = x)
for continuous distributions the probability density function (pdf), f_X, is returned
f_X(x) = P(x < X ≤ x + dx)
for some infinitesimally small dx.
If available a pdf will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$pdf(..., log = FALSE, simplify = TRUE, data = NULL)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need to be named. The length of each argument corresponds to the number of points to evaluate, the number of arguments corresponds to the number of variables in the distribution. See examples.log(logical(1))
IfTRUEreturns the logarithm of the probabilities. Default isFALSE.simplifylogical(1)
IfTRUE(default) simplifies the return if possible to anumeric, otherwise returns a data.table::data.table.dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number of points to evaluate and columns correspond with number of variables to evaluate. In the special case of VectorDistributions of multivariate distributions, then the third dimension corresponds to the distribution in the vector to evaluate.
Examples
b <- Binomial$new() b$pdf(1:10) b$pdf(1:10, log = TRUE) b$pdf(data = matrix(1:10)) mvn <- MultivariateNormal$new() mvn$pdf(1, 2) mvn$pdf(1:2, 3:4) mvn$pdf(data = matrix(1:4, nrow = 2), simplify = FALSE)
Method cdf()
The (lower tail) cumulative distribution function, F_X, is defined as
F_X(x) = P(X ≤ x)
If lower.tail is FALSE then 1 - F_X(x) is returned, also known as the
survival function.
If available a cdf will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$cdf( ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE, data = NULL )
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need to be named. The length of each argument corresponds to the number of points to evaluate, the number of arguments corresponds to the number of variables in the distribution. See examples.lower.tail(logical(1))
IfTRUE(default), probabilities areX <= x, otherwise,P(X > x).log.p(logical(1))
IfTRUEreturns the logarithm of the probabilities. Default isFALSE.simplifylogical(1)
IfTRUE(default) simplifies the return if possible to anumeric, otherwise returns a data.table::data.table.dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number of points to evaluate and columns correspond with number of variables to evaluate. In the special case of VectorDistributions of multivariate distributions, then the third dimension corresponds to the distribution in the vector to evaluate.
Examples
b <- Binomial$new() b$cdf(1:10) b$cdf(1:10, log.p = TRUE, lower.tail = FALSE) b$cdf(data = matrix(1:10))
Method quantile()
The quantile function, q_X, is the inverse cdf, i.e.
q_X(p) = F^(-1)_X(p) = inf{x ε R: F_X(x) ≥ p}
#nolint
If lower.tail is FALSE then q_X(1-p) is returned.
If available a quantile will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$quantile( ..., lower.tail = TRUE, log.p = FALSE, simplify = TRUE, data = NULL )
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need to be named. The length of each argument corresponds to the number of points to evaluate, the number of arguments corresponds to the number of variables in the distribution. See examples.lower.tail(logical(1))
IfTRUE(default), probabilities areX <= x, otherwise,P(X > x).log.p(logical(1))
IfTRUEreturns the logarithm of the probabilities. Default isFALSE.simplifylogical(1)
IfTRUE(default) simplifies the return if possible to anumeric, otherwise returns a data.table::data.table.dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number of points to evaluate and columns correspond with number of variables to evaluate. In the special case of VectorDistributions of multivariate distributions, then the third dimension corresponds to the distribution in the vector to evaluate.
Examples
b <- Binomial$new() b$quantile(0.42) b$quantile(log(0.42), log.p = TRUE, lower.tail = TRUE) b$quantile(data = matrix(c(0.1,0.2)))
Method rand()
The rand function draws n simulations from the distribution.
If available simulations will be returned using an analytic expression. Otherwise,
if the distribution has not been decorated with FunctionImputation, NULL is
returned.
Usage
Distribution$rand(n, simplify = TRUE)
Arguments
n(numeric(1))
Number of points to simulate from the distribution. If length greater than 1, thenn <- length(n),simplifylogical(1)
IfTRUE(default) simplifies the return if possible to anumeric, otherwise returns a data.table::data.table.
Examples
b <- Binomial$new() b$rand(10) mvn <- MultivariateNormal$new() mvn$rand(5)
Method prec()
Returns the precision of the distribution as 1/self$variance().
Usage
Distribution$prec()
Method stdev()
Returns the standard deviation of the distribution as sqrt(self$variance()).
Usage
Distribution$stdev()
Method median()
Returns the median of the distribution. If an analytical expression is available
returns distribution median, otherwise if symmetric returns self$mean, otherwise
returns self$quantile(0.5).
Usage
Distribution$median(na.rm = NULL, ...)
Arguments
na.rm(logical(1))
Ignored, addded for consistency....ANY
Ignored, addded for consistency.
Method iqr()
Inter-quartile range of the distribution. Estimated as
self$quantile(0.75) - self$quantile(0.25).
Usage
Distribution$iqr()
Method confidence()
1 or 2-sided confidence interval around distribution.
Usage
Distribution$confidence(alpha = 0.95, sides = "both", median = FALSE)
Arguments
alpha(numeric(1))
Level of confidence, default is 95%sides(character(1))
One of 'lower', 'upper' or 'both'median(logical(1))
IfTRUEalso returns median
Method correlation()
If univariate returns 1, otherwise returns the distribution correlation.
Usage
Distribution$correlation()
Method liesInSupport()
Tests if the given values lie in the support of the distribution.
Uses [set6::Set]$contains.
Usage
Distribution$liesInSupport(x, all = TRUE, bound = FALSE)
Arguments
xANY
Values to test.alllogical(1)
IfTRUE(default) returnsTRUEif allxare in the distribution, otherwise returns a vector of logicals corresponding to each element inx.boundlogical(1)
IfTRUEthen tests ifxlie between the upper and lower bounds of the distribution, otherwise tests ifxlie between the maximum and minimum of the distribution.
Method liesInType()
Tests if the given values lie in the type of the distribution.
Uses [set6::Set]$contains.
Usage
Distribution$liesInType(x, all = TRUE, bound = FALSE)
Arguments
xANY
Values to test.alllogical(1)
IfTRUE(default) returnsTRUEif allxare in the distribution, otherwise returns a vector of logicals corresponding to each element inx.boundlogical(1)
IfTRUEthen tests ifxlie between the upper and lower bounds of the distribution, otherwise tests ifxlie between the maximum and minimum of the distribution.
Method workingSupport()
Returns an estimate for the computational support of the distribution.
If an analytical cdf is available, then this is computed as the smallest interval
in which the cdf lower bound is 0 and the upper bound is 1, bounds are incremented in
10^i intervals. If no analytical cdf is available, then this is computed as the smallest
interval in which the lower and upper bounds of the pdf are 0, this is much less precise
and is more prone to error. Used primarily by decorators.
Usage
Distribution$workingSupport()
Method clone()
The objects of this class are cloneable with this method.
Usage
Distribution$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------ ## Method `Distribution$setParameterValue` ## ------------------------------------------------ b = Binomial$new() b$setParameterValue(size = 4, prob = 0.4) b$setParameterValue(lst = list(size = 4, prob = 0.4)) ## ------------------------------------------------ ## Method `Distribution$pdf` ## ------------------------------------------------ b <- Binomial$new() b$pdf(1:10) b$pdf(1:10, log = TRUE) b$pdf(data = matrix(1:10)) mvn <- MultivariateNormal$new() mvn$pdf(1, 2) mvn$pdf(1:2, 3:4) mvn$pdf(data = matrix(1:4, nrow = 2), simplify = FALSE) ## ------------------------------------------------ ## Method `Distribution$cdf` ## ------------------------------------------------ b <- Binomial$new() b$cdf(1:10) b$cdf(1:10, log.p = TRUE, lower.tail = FALSE) b$cdf(data = matrix(1:10)) ## ------------------------------------------------ ## Method `Distribution$quantile` ## ------------------------------------------------ b <- Binomial$new() b$quantile(0.42) b$quantile(log(0.42), log.p = TRUE, lower.tail = TRUE) b$quantile(data = matrix(c(0.1,0.2))) ## ------------------------------------------------ ## Method `Distribution$rand` ## ------------------------------------------------ b <- Binomial$new() b$rand(10) mvn <- MultivariateNormal$new() mvn$rand(5)
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