Title: R Object Oriented Programming for Statistical Distribution
Date: 2023-09-11
Version: 0.3.9
Description: Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) <doi:10.48550/arXiv.math-ph/0609050>.
URL: https://github.com/yrobink/ROOPSD
Depends: R (≥ 3.3)
License: CeCILL-2
Encoding: UTF-8
Imports: methods, R6, Lmoments, numDeriv
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2023-09-11 06:55:45 UTC; yrobin
Author: Yoann Robin [aut, cre]
Maintainer: Yoann Robin <yoann.robin.k@gmail.com>
Repository: CRAN
Date/Publication: 2023-09-11 07:20:15 UTC

AbstractDist

Description

Base class for OOP statistical distribution

Details

This class is only used to be herited

Public fields

ddist

[function] density function

pdist

[function] distribution function

qdist

[function] quantile function

rdist

[function] random generator function

ks.test

[ks.test] Goodness of fit with ks.test

fit_success

[bool] TRUE only if the fit is a success and is occurred

Active bindings

name

[string] name of the distribution

opt

[stats::optim result] Result of the MLE to find parameters

cov

[matrix] Covariance matrix of parameters, inverse of hessian

coef

[vector] Vector of coefficients

Methods

Public methods

Method new()

Create a new AbstractDist object.

Usage
AbstractDist$new(ddist, pdist, qdist, rdist, name, has_gr_nlll)
Arguments
ddist

[function] Density function, e.g. dnorm

pdist

[function] Distribution function, e.g. pnorm

qdist

[function] Quantile function, e.g. qnorm

rdist

[function] Random generator function, e.g. rnorm

name

[str] name of the distribution

has_gr_nlll

[bool] If the derived class has defined the gradient of the negative log-likelihood

Returns

A new 'AbstractDist' object.

Method rvs()

Generation sample from the histogram

Usage
AbstractDist$rvs(n)
Arguments
n

[integer] Number of samples drawn

Returns

[vector] A vector of samples

Method density()

Density function

Usage
AbstractDist$density(x)
Arguments
x

[vector] Values to compute the density

Returns

[vector] density

Method logdensity()

Log density function

Usage
AbstractDist$logdensity(x)
Arguments
x

[vector] Values to compute the log-density

Returns

[vector] log of density

Method cdf()

Cumulative Distribution Function

Usage
AbstractDist$cdf(q)
Arguments
q

[vector] Quantiles to compute the CDF

Returns

[vector] cdf values

Method sf()

Survival Function

Usage
AbstractDist$sf(q)
Arguments
q

[vector] Quantiles to compute the SF

Returns

[vector] sf values

Method icdf()

Inverse of Cumulative Distribution Function

Usage
AbstractDist$icdf(p)
Arguments
p

[vector] Probabilities to compute the CDF

Returns

[vector] icdf values

Method isf()

Inverse of Survival Function

Usage
AbstractDist$isf(p)
Arguments
p

[vector] Probabilities to compute the SF

Returns

[vector] isf values

Method fit()

Fit method

Usage
AbstractDist$fit(Y, n_max_try = 100)
Arguments
Y

[vector] Dataset to infer the histogram

n_max_try

[integer] Because the optim function can fails, the fit is retry n_try times.

Returns

'self'

Method qgradient()

Gradient of the quantile function

Usage
AbstractDist$qgradient(p, lower.tail = TRUE)
Arguments
p

[vector] Probabilities

lower.tail

[bool] If CDF or SF.

Returns

[vector] gradient

Method qdeltaCI()

Confidence interval of the quantile function

Usage
AbstractDist$qdeltaCI(p, Rt = FALSE, alpha = 0.05)
Arguments
p

[vector] Probabilities

Rt

[bool] if Probabilities or return times

alpha

[double] level of confidence interval

Returns

[list] Quantiles, and confidence interval

Method pdeltaCI()

Confidence interval of the CDF function

Usage
AbstractDist$pdeltaCI(x, Rt = FALSE, alpha = 0.05)
Arguments
x

[vector] Quantiles

Rt

[bool] if Probabilities or return times

alpha

[double] level of confidence interval

Returns

[list] CDF, and confidence interval

Method diagnostic()

Diagnostic of the fitted law

Usage
AbstractDist$diagnostic(Y, alpha = 0.05)
Arguments
Y

[vector] data to check

alpha

[double] level of confidence interval

Returns

[NULL]

Method clone()

The objects of this class are cloneable with this method.

Usage
AbstractDist$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Exponential

Description

Exponential distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> Exponential

Active bindings

rate

[double] rate of the exponential law

params

[vector] params of the exponential law

Methods

Public methods

Inherited methods

Method new()

Create a new Exponential object.

Usage
Exponential$new(rate = 1)
Arguments
rate

[double] Rate of the exponential law

Returns

A new 'Exponential' object.

Method clone()

The objects of this class are cloneable with this method.

Usage
Exponential$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
rate = 0.5
expl = ROOPSD::Exponential$new( rate = rate )
X    = expl$rvs( n = 1000 )

## And fit parameters
expl$fit(X)

GEV

Description

GEV distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> GEV

Active bindings

loc

[double] location of the GEV law

scale

[double] scale of the GEV law

shape

[double] shape of the GEV law

params

[vector] params of the GEV law

Methods

Public methods

Inherited methods

Method new()

Create a new GEV object.

Usage
GEV$new(loc = 0, scale = 1, shape = -0.1)
Arguments
loc

[double] location parameter

scale

[double] scale parameter

shape

[double] shape parameter

Returns

A new 'GEV' object.

Method qgradient()

Gradient of the quantile function

Usage
GEV$qgradient(p, lower.tail = TRUE)
Arguments
p

[vector] Probabilities

lower.tail

[bool] If CDF or SF.

Returns

[vector] gradient

Method pgradient()

Gradient of the CDF function

Usage
GEV$pgradient(x, lower.tail = TRUE)
Arguments
x

[vector] Quantiles

lower.tail

[bool] If CDF or SF.

Returns

[vector] gradient

Method clone()

The objects of this class are cloneable with this method.

Usage
GEV$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
loc   = 0
scale = 0.5
shape = -0.3
gev = ROOPSD::GEV$new( loc = loc , scale = scale , shape = shape )
X   = gev$rvs( n = 1000 )

## And fit parameters
gev$fit(X)

GPD

Description

GPD distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> GPD

Active bindings

loc

[double] location of the GPD law, fixed

scale

[double] scale of the GPD law

shape

[double] shape of the GPD law

params

[vector] params of the GPD law

Methods

Public methods

Inherited methods

Method new()

Create a new GPD object.

Usage
GPD$new(loc = 0, scale = 1, shape = -0.1)
Arguments
loc

[double] location parameter

scale

[double] scale parameter

shape

[double] shape parameter

Returns

A new 'GPD' object.

Method fit()

Fit method

Usage
GPD$fit(Y, loc = NULL)
Arguments
Y

[vector] Dataset to infer the histogram

loc

[double] location parameter, if NULL used min(Y)

Returns

'self'

Method clone()

The objects of this class are cloneable with this method.

Usage
GPD$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
loc   = 0
scale = 0.5
shape = -0.3
gpd = ROOPSD::GPD$new( loc = loc , scale = scale , shape = shape )
X   = gpd$rvs( n = 1000 )

## And fit parameters
gpd$fit( X , loc = 0 )

Gamma

Description

Gamma distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> Gamma

Active bindings

shape

[double] shape of the gamma law

scale

[double] scale of the gamma law

params

[vector] params of the gamma law

Methods

Public methods

Inherited methods

Method new()

Create a new Gamma object.

Usage
Gamma$new(shape = 0.5, scale = 1)
Arguments
shape

[double] shape parameter

scale

[double] scale parameter

Returns

A new 'Gamma' object.

Method clone()

The objects of this class are cloneable with this method.

Usage
Gamma$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
scale = 1.5
shape = 0.5
gaml = ROOPSD::Gamma$new( scale = scale , shape = shape )
X    = gaml$rvs( n = 1000 )

## And fit parameters
gaml$fit(X)

Normal

Description

Normal distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> Normal

Active bindings

mean

[double] mean of the normal law

sd

[double] standard deviation of the normal law

params

[vector] params of the normal law

Methods

Public methods

Inherited methods

Method new()

Create a new Normal object.

Usage
Normal$new(mean = 0, sd = 1)
Arguments
mean

[double] Mean of the normal law

sd

[double] Standard deviation of the normal law

Returns

A new 'Normal' object.

Method clone()

The objects of this class are cloneable with this method.

Usage
Normal$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
mean  = 1
sd    = 0.5
norml = ROOPSD::Normal$new( mean = mean , sd = sd )
X     = norml$rvs( n = 1000 )

## And fit parameters
norml$fit(X)

Uniform

Description

Uniform distribution in OOP way. Based on AbstractDist

Details

See AbstractDist for generic methods

Super class

ROOPSD::AbstractDist -> Uniform

Active bindings

min

[double] min of the uniform law

max

[double] max of the uniform law

params

[vector] params of the uniform law

Methods

Public methods

Inherited methods

Method new()

Create a new Uniform object.

Usage
Uniform$new(min = 0, max = 1)
Arguments
min

[double] Min of the uniform law

max

[double] Max of the uniform law

Returns

A new 'Uniform' object.

Method clone()

The objects of this class are cloneable with this method.

Usage
Uniform$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
min = -1
max = 1
unifl = ROOPSD::Uniform$new( min = min , max = max )
X     = unifl$rvs( n = 1000 )

## And fit parameters
unifl$fit(X)

dgev

Description

Density function of Generalized Extreme Value distribution

Usage

dgev(x, loc = 0, scale = 1, shape = 0, log = FALSE)

Arguments

x

[vector] Vector of values

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

log

[bool] Return log of density if TRUE, default is FALSE

Value

[vector] Density of GEV at x

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
x = base::seq( -5 , 5 , length = 1000 )
y = dgev( x , loc = loc , scale = scale , shape = shape )

dgpd

Description

Density function of Generalized Pareto Distribution

Usage

dgpd(x, loc = 0, scale = 1, shape = 0, log = FALSE)

Arguments

x

[vector] Vector of values

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

log

[bool] Return log of density if TRUE, default is FALSE

Value

[vector] Density of GPD at x

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
x = base::seq( -5 , 5 , length = 1000 )
y = dgpd( x , loc = loc , scale = scale , shape = shape )

mrv_histogram

Description

Multivariate rv_histogram distribution in OOP way.

Details

Used for a multivariate dataset, fit each marge

Public fields

n_features

[integer] Number of features (dimensions)

law_

[list] List of marginal distributions

Methods

Public methods

Method new()

Create a new mrv_histogram object.

Usage
mrv_histogram$new(...)
Arguments
...

If a param 'Y' is given, the fit method is called with '...'.

Returns

A new 'mrv_histogram' object.

Method fit()

Fit method for the histograms

Usage
mrv_histogram$fit(Y, bins = as.integer(100))
Arguments
Y

[vector] Dataset to infer the histogram

bins

[list or vector or integer] bins values

Returns

'self'

Method rvs()

Generation sample from the histogram

Usage
mrv_histogram$rvs(n = 1)
Arguments
n

[integer] Number of samples drawn

Returns

A matrix of samples

Method cdf()

Cumulative Distribution Function

Usage
mrv_histogram$cdf(q)
Arguments
q

[vector] Quantiles to compute the CDF

Returns

cdf values

Method sf()

Survival Function

Usage
mrv_histogram$sf(q)
Arguments
q

[vector] Quantiles to compute the SF

Returns

sf values

Method icdf()

Inverse of Cumulative Distribution Function

Usage
mrv_histogram$icdf(p)
Arguments
p

[vector] Probabilities to compute the CDF

Returns

icdf values

Method isf()

Inverse of Survival Function

Usage
mrv_histogram$isf(p)
Arguments
p

[vector] Probabilities to compute the SF

Returns

isf values

Method clone()

The objects of this class are cloneable with this method.

Usage
mrv_histogram$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
X = matrix( stats::rnorm( n = 10000 ) , ncol = 4 )

## And fit it
rvX = mrv_histogram$new()
rvX$fit(X)

pgev

Description

Cumulative distribution function (or survival function) of Generalized Extreme Value distribution

Usage

pgev(q, loc = 0, scale = 1, shape = 0, lower.tail = TRUE)

Arguments

q

[vector] Vector of quantiles

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

lower.tail

[bool] Return CDF if TRUE, else return survival function

Value

[vector] CDF (or SF) of GEV at x

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
x = base::seq( -5 , 5 , length = 1000 )
cdfx = pgev( x , loc = loc , scale = scale , shape = shape )

pgpd

Description

Cumulative distribution function (or survival function) of Generalized Pareto distribution

Usage

pgpd(q, loc = 0, scale = 1, shape = 0, lower.tail = TRUE)

Arguments

q

[vector] Vector of quantiles

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

lower.tail

[bool] Return CDF if TRUE, else return survival function

Value

[vector] CDF (or SF) of GPD at x

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
x = base::seq( -5 , 5 , length = 1000 )
cdfx = pgpd( x , loc = loc , scale = scale , shape = shape )

qgev

Description

Inverse of CDF (or SF) function of Generalized Extreme Value distribution

Usage

qgev(p, loc = 0, scale = 1, shape = 0, lower.tail = TRUE)

Arguments

p

[vector] Vector of probabilities

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

lower.tail

[bool] Return inverse of CDF if TRUE, else return inverse of survival function

Value

[vector] Inverse of CDF or SF of GEV for probabilities p

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
p = base::seq( 0.01 , 0.99 , length = 100 )
q = qgev( p , loc = loc , scale = scale , shape = shape )

qgpd

Description

Inverse of CDF (or SF) function of Generalized Pareto distribution

Usage

qgpd(p, loc = 0, scale = 1, shape = 0, lower.tail = TRUE)

Arguments

p

[vector] Vector of probabilities

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

lower.tail

[bool] Return inverse of CDF if TRUE, else return inverse of survival function

Value

[vector] Inverse of CDF or SF of GPD for probabilities p

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
p = base::seq( 0.01 , 0.99 , length = 100 )
q = qgpd( p , loc = loc , scale = scale , shape = shape )

rgev

Description

Random value generator of Generalized Extreme Value distribution

Usage

rgev(n = 1, loc = 0, scale = 1, shape = 0)

Arguments

n

[int] Numbers of values generated

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

Value

[vector] Random value following a GEV(loc,scale,shape)

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
gev = rgev( 100 , loc = loc , scale = scale , shape = shape )

rgpd

Description

Random value generator of Generalized Pareto distribution

Usage

rgpd(n = 1, loc = 0, scale = 1, shape = 0)

Arguments

n

[int] Numbers of values generated

loc

[vector] Location parameter

scale

[vector] Scale parameter

shape

[vector] Shape parameter

Value

[vector] Random value following a loc + GPD(scale,shape)

Examples

## Data
loc = 1
scale = 0.5
shape = -0.2
gev = rgpd( 100 , loc = loc , scale = scale , shape = shape )

rmultivariate_normal

Description

Generate sample from a multivariate normal distribution. The generator uses a singular values decomposition to draw samples from a normal distribution in the basis of the singular vector. Consequently, the covariance matrix can be singular.

Usage

rmultivariate_normal(n, mean, cov)

Arguments

n

[integer] numbers of samples drawn

mean

[vector] mean of Normal law

cov

[matrix] covariance matrix

Value

[matrix]

Examples

mean = stats::runif( n = 2 , min = -5 , max = 5 )
cov  = ROOPSD::rspd_matrix(2)
X    = ROOPSD::rmultivariate_normal( 10000 , mean , cov )

rorthogonal_group

Description

Generate sample from the orthogonal group O(d)

Usage

rorthogonal_group(d, n = 1)

Arguments

d

[integer] Dimension of the matrix

n

[integer] numbers of samples drawn

Value

[array or matrix], dim = d * d * n or d * d if n == 1

Examples

M = ROOPSD::rorthogonal_group( 2 , 10 )

rspd_matrix

Description

Generate a random symetric positive definite matrix. The generator just draw matrix of the form O * diag(positive values) * t(O), where O is an orthogonal matrix from ROOPSD::rorthogonal_group. Note that the parameter gen = stats::rexp draw positive eigen values, but the code do not control if eigen values are positive. So you can accept negative eigen values using another generators.

Usage

rspd_matrix(d, n = 1, sort_eigenvalues = TRUE, gen = stats::rexp)

Arguments

d

[integer] Dimension of the matrix

n

[integer] numbers of samples drawn

sort_eigenvalues

[bool] If eigen values (i.e. variance) are sorted

gen

[function] Eigenvalues generator

Value

[array or matrix], dim = d * d * n or d * d if n == 1

Examples

mean = stats::runif( n = 2 , min = -5 , max = 5 )
cov  = ROOPSD::rspd_matrix(2)
X    = ROOPSD::rmultivariate_normal( 10000 , mean , cov )

rv_histogram

Description

rv_histogram distribution in OOP way.

Details

Use quantile to fit the histogram

Public fields

min

[double] min value for the estimation

max

[double] max value for the estimation

tol

[double] numerical tolerance

Methods

Public methods

Method new()

Create a new rv_histogram object.

Usage
rv_histogram$new(...)
Arguments
...

If a param 'Y' is given, the fit method is called with '...'.

Returns

A new 'rv_histogram' object.

Method rvs()

Generation sample from the histogram

Usage
rv_histogram$rvs(n)
Arguments
n

[integer] Number of samples drawn

Returns

A vector of samples

Method density()

Density function

Usage
rv_histogram$density(x)
Arguments
x

[vector] Values to compute the density

Returns

density

Method logdensity()

Log density function

Usage
rv_histogram$logdensity(x)
Arguments
x

[vector] Values to compute the log-density

Returns

the log density

Method cdf()

Cumulative Distribution Function

Usage
rv_histogram$cdf(q)
Arguments
q

[vector] Quantiles to compute the CDF

Returns

cdf values

Method icdf()

Inverse of Cumulative Distribution Function

Usage
rv_histogram$icdf(p)
Arguments
p

[vector] Probabilities to compute the CDF

Returns

icdf values

Method sf()

Survival Function

Usage
rv_histogram$sf(q)
Arguments
q

[vector] Quantiles to compute the SF

Returns

sf values

Method isf()

Inverse of Survival Function

Usage
rv_histogram$isf(p)
Arguments
p

[vector] Probabilities to compute the SF

Returns

isf values

Method fit()

Fit method for the histograms

Usage
rv_histogram$fit(Y, bins = as.integer(1000))
Arguments
Y

[vector] Dataset to infer the histogram

bins

[vector or integer] bins values

Returns

'self'

Method clone()

The objects of this class are cloneable with this method.

Usage
rv_histogram$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
X = numeric(10000)
X[1:5000] = stats::rnorm( n = 5000 , mean = 2 , sd = 1 )
X[5000:10000] = stats::rexp( n = 5000 , rate = 1 )

## And fit it
rvX = rv_histogram$new()
rvX$fit(X)

rv_mixture

Description

rv_mixture distribution in OOP way.

Details

No fit allowed.

Active bindings

l_dist

[list] List of distributions.

n_dist

[integer] Numbers of distribution.

weights

[vector] Weights of the distributions.

Methods

Public methods

Method new()

Create a new rv_mixture object.

Usage
rv_mixture$new(l_dist, weights = NULL)
Arguments
l_dist

[list] List of ROOPSD distributions.

weights

[vector] Weights of the distributions. If NULL, 1 / length(l_dist) is used.

Returns

A new 'rv_mixture' object.

Method rvs()

Generation sample from the histogram

Usage
rv_mixture$rvs(n)
Arguments
n

[integer] Number of samples drawn

Returns

A vector of samples

Method density()

Density function

Usage
rv_mixture$density(x)
Arguments
x

[vector] Values to compute the density

Returns

density

Method logdensity()

Log density function

Usage
rv_mixture$logdensity(x)
Arguments
x

[vector] Values to compute the log-density

Returns

the log density

Method cdf()

Cumulative Distribution Function

Usage
rv_mixture$cdf(q)
Arguments
q

[vector] Quantiles to compute the CDF

Returns

cdf values

Method icdf()

Inverse of Cumulative Distribution Function

Usage
rv_mixture$icdf(p)
Arguments
p

[vector] Probabilities to compute the CDF

Returns

icdf values

Method sf()

Survival Function

Usage
rv_mixture$sf(q)
Arguments
q

[vector] Quantiles to compute the SF

Returns

sf values

Method isf()

Inverse of Survival Function

Usage
rv_mixture$isf(p)
Arguments
p

[vector] Probabilities to compute the SF

Returns

isf values

Method clone()

The objects of this class are cloneable with this method.

Usage
rv_mixture$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Define the mixture
l_dist  = list( Exponential$new() , Normal$new( mean = 5 , sd = 1 ) )
weights = base::c( 0.2 , 0.8 )
rvX = rv_mixture$new( l_dist , weights )

## Draw samples
X   = rvX$rvs( 1000 )

rv_ratio_histogram

Description

rv_ratio_histogram distribution in OOP way.

Details

Fit separatly P( X < x | X > 0 ) and P(X=0)

Public fields

rvXp

[ROOPSD::rv_histogram] Describes P(X < x | X > x0)

x0

[double] location of mass: P( X = x0 )

p0

[double] p0 = P( X = x0 )

Methods

Public methods

Method new()

Create a new rv_ratio_histogram object.

Usage
rv_ratio_histogram$new(...)
Arguments
...

If a param 'Y' and 'x0' is given, the fit method is called with '...'.

Returns

A new 'rv_ratio_histogram' object.

Method rvs()

Generation sample from the histogram

Usage
rv_ratio_histogram$rvs(n)
Arguments
n

[integer] Number of samples drawn

Returns

A vector of samples

Method cdf()

Cumulative Distribution Function

Usage
rv_ratio_histogram$cdf(q)
Arguments
q

[vector] Quantiles to compute the CDF

Returns

cdf values

Method icdf()

Inverse of Cumulative Distribution Function

Usage
rv_ratio_histogram$icdf(p)
Arguments
p

[vector] Probabilities to compute the CDF

Returns

icdf values

Method sf()

Survival Function

Usage
rv_ratio_histogram$sf(q)
Arguments
q

[vector] Quantiles to compute the SF

Returns

sf values

Method isf()

Inverse of Survival Function

Usage
rv_ratio_histogram$isf(p)
Arguments
p

[vector] Probabilities to compute the SF

Returns

isf values

Method fit()

Fit method for the histograms

Usage
rv_ratio_histogram$fit(Y, x0, bins = as.integer(100))
Arguments
Y

[vector] Dataset to infer the histogram

x0

[double] Location of mass point

bins

[vector or integer] bins values

Returns

'self'

Method clone()

The objects of this class are cloneable with this method.

Usage
rv_ratio_histogram$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## Generate sample
X = numeric(10000)
X[1:2000] = 0
X[2001:10000] = stats::rexp( n = 8000 , rate = 1 )

## And fit it
rvX = rv_ratio_histogram$new()
rvX$fit( X , x0 = 0 )

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