ExoticStatistics: Exotic Statistical Methods Decorator
In distr6: The Complete R6 Probability Distributions Interface
ExoticStatistics R Documentation
Exotic Statistical Methods Decorator
Description
This decorator adds methods for more complex statistical methods including p-norms,
survival and hazard functions and anti-derivatives. If possible analytical expressions are
exploited, otherwise numerical ones are used with a message.
Details
Decorator objects add functionality to the given Distribution object by copying methods
in the decorator environment to the chosen Distribution environment.
All methods implemented in decorators try to exploit analytical results where possible, otherwise
numerical results are used with a message.
Super class
distr6::DistributionDecorator -> ExoticStatistics
Methods
Public methods
-
-
-
-
-
-
-
-
-
Inherited methods
Method cdfAntiDeriv()
The cdf anti-derivative is defined by
acdf(a, b) = \int_a^b F_X(x) dx
where X is the distribution, F_X is the cdf of the distribution X and
a, b are the lower and upper limits of integration.
Usage
ExoticStatistics$cdfAntiDeriv(lower = NULL, upper = NULL)
Arguments
lower(numeric(1)
Lower bounds of integral.
upper(numeric(1)
Upper bounds of integral.
Method survivalAntiDeriv()
The survival anti-derivative is defined by
as(a, b) = \int_a^b S_X(x) dx
where X is the distribution, S_X is the survival function of the distribution
X and a, b are the lower and upper limits of integration.
Usage
ExoticStatistics$survivalAntiDeriv(lower = NULL, upper = NULL)
Arguments
lower(numeric(1)
Lower bounds of integral.
upper(numeric(1)
Upper bounds of integral.
Method survival()
The survival function is defined by
S_X(x) = P(X ≥ x) = 1 - F_X(x) = \int_x^∞ f_X(x) dx
where X is the distribution, S_X is the survival function, F_X is the cdf
and f_X is the pdf.
Usage
ExoticStatistics$survival(..., log = FALSE, simplify = TRUE, data = NULL)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need
to be named. The length of each argument corresponds to the number of points to evaluate,
the number of arguments corresponds to the number of variables in the distribution.
See examples.
log(logical(1))
If TRUE returns the logarithm of the probabilities. Default is FALSE.
simplifylogical(1)
If TRUE (default) simplifies the return if possible to a numeric, otherwise returns a
data.table::data.table.
dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number
of points to evaluate and columns correspond with number of variables to evaluate. In the special
case of VectorDistributions of multivariate distributions, then the third dimension corresponds
to the distribution in the vector to evaluate.
Method hazard()
The hazard function is defined by
h_X(x) = f_X/S_X
where X is the distribution, S_X is the survival function and f_X is the pdf.
Usage
ExoticStatistics$hazard(..., 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.
Method cumHazard()
The cumulative hazard function is defined analytically by
H_X(x) = -log(S_X)
where X is the distribution and S_X is the survival function.
Usage
ExoticStatistics$cumHazard(..., log = FALSE, simplify = TRUE, data = NULL)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need
to be named. The length of each argument corresponds to the number of points to evaluate,
the number of arguments corresponds to the number of variables in the distribution.
See examples.
log(logical(1))
If TRUE returns the logarithm of the probabilities. Default is FALSE.
simplifylogical(1)
If TRUE (default) simplifies the return if possible to a numeric, otherwise returns a
data.table::data.table.
dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number
of points to evaluate and columns correspond with number of variables to evaluate. In the special
case of VectorDistributions of multivariate distributions, then the third dimension corresponds
to the distribution in the vector to evaluate.
Method cdfPNorm()
The p-norm of the cdf is defined by
(\int_a^b |F_X|^p dμ)^{1/p}
where X is the distribution, F_X is the cdf and a, b
are the lower and upper limits of integration.
Returns NULL if distribution is not continuous.
Usage
ExoticStatistics$cdfPNorm(p = 2, lower = NULL, upper = NULL)
Arguments
p(integer(1))
Norm to evaluate.
lower(numeric(1)
Lower bounds of integral.
upper(numeric(1)
Upper bounds of integral.
Method pdfPNorm()
The p-norm of the pdf is defined by
(\int_a^b |f_X|^p dμ)^{1/p}
where X is the distribution, f_X is the pdf and a, b
are the lower and upper limits of integration.
Returns NULL if distribution is not continuous.
Usage
ExoticStatistics$pdfPNorm(p = 2, lower = NULL, upper = NULL)
Arguments
p(integer(1))
Norm to evaluate.
lower(numeric(1)
Lower bounds of integral.
upper(numeric(1)
Upper bounds of integral.
Method survivalPNorm()
The p-norm of the survival function is defined by
(\int_a^b |S_X|^p dμ)^{1/p}
where X is the distribution, S_X is the survival function and a, b
are the lower and upper limits of integration.
Returns NULL if distribution is not continuous.
Usage
ExoticStatistics$survivalPNorm(p = 2, lower = NULL, upper = NULL)
Arguments
p(integer(1))
Norm to evaluate.
lower(numeric(1)
Lower bounds of integral.
upper(numeric(1)
Upper bounds of integral.
Method clone()
The objects of this class are cloneable with this method.
Usage
ExoticStatistics$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other decorators:
CoreStatistics,
FunctionImputation
Examples
decorate(Exponential$new(), "ExoticStatistics")
Exponential$new(decorators = "ExoticStatistics")
ExoticStatistics$new()$decorate(Exponential$new())
distr6 documentation built on March 28, 2022, 1:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| ExoticStatistics | R Documentation |
Exotic Statistical Methods Decorator
Description
This decorator adds methods for more complex statistical methods including p-norms, survival and hazard functions and anti-derivatives. If possible analytical expressions are exploited, otherwise numerical ones are used with a message.
Details
Decorator objects add functionality to the given Distribution object by copying methods in the decorator environment to the chosen Distribution environment.
All methods implemented in decorators try to exploit analytical results where possible, otherwise numerical results are used with a message.
Super class
distr6::DistributionDecorator -> ExoticStatistics
Methods
Public methods
Inherited methods
Method cdfAntiDeriv()
The cdf anti-derivative is defined by
acdf(a, b) = \int_a^b F_X(x) dx
where X is the distribution, F_X is the cdf of the distribution X and
a, b are the lower and upper limits of integration.
Usage
ExoticStatistics$cdfAntiDeriv(lower = NULL, upper = NULL)
Arguments
lower(numeric(1)
Lower bounds of integral.upper(numeric(1)
Upper bounds of integral.
Method survivalAntiDeriv()
The survival anti-derivative is defined by
as(a, b) = \int_a^b S_X(x) dx
where X is the distribution, S_X is the survival function of the distribution
X and a, b are the lower and upper limits of integration.
Usage
ExoticStatistics$survivalAntiDeriv(lower = NULL, upper = NULL)
Arguments
lower(numeric(1)
Lower bounds of integral.upper(numeric(1)
Upper bounds of integral.
Method survival()
The survival function is defined by
S_X(x) = P(X ≥ x) = 1 - F_X(x) = \int_x^∞ f_X(x) dx
where X is the distribution, S_X is the survival function, F_X is the cdf and f_X is the pdf.
Usage
ExoticStatistics$survival(..., log = FALSE, simplify = TRUE, data = NULL)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need to be named. The length of each argument corresponds to the number of points to evaluate, the number of arguments corresponds to the number of variables in the distribution. See examples.log(logical(1))
IfTRUEreturns the logarithm of the probabilities. Default isFALSE.simplifylogical(1)
IfTRUE(default) simplifies the return if possible to anumeric, otherwise returns a data.table::data.table.dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number of points to evaluate and columns correspond with number of variables to evaluate. In the special case of VectorDistributions of multivariate distributions, then the third dimension corresponds to the distribution in the vector to evaluate.
Method hazard()
The hazard function is defined by
h_X(x) = f_X/S_X
where X is the distribution, S_X is the survival function and f_X is the pdf.
Usage
ExoticStatistics$hazard(..., 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.
Method cumHazard()
The cumulative hazard function is defined analytically by
H_X(x) = -log(S_X)
where X is the distribution and S_X is the survival function.
Usage
ExoticStatistics$cumHazard(..., log = FALSE, simplify = TRUE, data = NULL)
Arguments
...(numeric())
Points to evaluate the function at Arguments do not need to be named. The length of each argument corresponds to the number of points to evaluate, the number of arguments corresponds to the number of variables in the distribution. See examples.log(logical(1))
IfTRUEreturns the logarithm of the probabilities. Default isFALSE.simplifylogical(1)
IfTRUE(default) simplifies the return if possible to anumeric, otherwise returns a data.table::data.table.dataarray
Alternative method to specify points to evaluate. If univariate then rows correspond with number of points to evaluate and columns correspond with number of variables to evaluate. In the special case of VectorDistributions of multivariate distributions, then the third dimension corresponds to the distribution in the vector to evaluate.
Method cdfPNorm()
The p-norm of the cdf is defined by
(\int_a^b |F_X|^p dμ)^{1/p}
where X is the distribution, F_X is the cdf and a, b
are the lower and upper limits of integration.
Returns NULL if distribution is not continuous.
Usage
ExoticStatistics$cdfPNorm(p = 2, lower = NULL, upper = NULL)
Arguments
p(integer(1))Norm to evaluate.lower(numeric(1)
Lower bounds of integral.upper(numeric(1)
Upper bounds of integral.
Method pdfPNorm()
The p-norm of the pdf is defined by
(\int_a^b |f_X|^p dμ)^{1/p}
where X is the distribution, f_X is the pdf and a, b
are the lower and upper limits of integration.
Returns NULL if distribution is not continuous.
Usage
ExoticStatistics$pdfPNorm(p = 2, lower = NULL, upper = NULL)
Arguments
p(integer(1))Norm to evaluate.lower(numeric(1)
Lower bounds of integral.upper(numeric(1)
Upper bounds of integral.
Method survivalPNorm()
The p-norm of the survival function is defined by
(\int_a^b |S_X|^p dμ)^{1/p}
where X is the distribution, S_X is the survival function and a, b
are the lower and upper limits of integration.
Returns NULL if distribution is not continuous.
Usage
ExoticStatistics$survivalPNorm(p = 2, lower = NULL, upper = NULL)
Arguments
p(integer(1))Norm to evaluate.lower(numeric(1)
Lower bounds of integral.upper(numeric(1)
Upper bounds of integral.
Method clone()
The objects of this class are cloneable with this method.
Usage
ExoticStatistics$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other decorators:
CoreStatistics,
FunctionImputation
Examples
decorate(Exponential$new(), "ExoticStatistics") Exponential$new(decorators = "ExoticStatistics") ExoticStatistics$new()$decorate(Exponential$new())
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.