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Type: Package
Title: Prediction for Future Data from Mixture Distributions Gamma, Beta, Weibull and Normal
Version: 0.1.2
Author: O. M. Khaled [aut], K. S. Khalil [aut, cre], M. H. Harby [aut]
Maintainer: K. S. Khalil <kekox38@gmail.com>
Description: Functions to get prediction intervals and prediction points of future observations from mixture distributions like gamma, beta, Weibull and normal.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
Imports: stats, zipfR
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2024-11-28 12:49:09 UTC; Karim
Repository: CRAN
Date/Publication: 2024-11-29 09:20:10 UTC

Prediction future points from mixture beta distribution

Description

Construct a prediction point for future observations from mixture beta distribution. Generic method is print.

Usage

bmixp(data, s, n, a ,parameters, conf=0.95)

## S3 method for class 'bmixp'
print(x, ...)

Arguments

data

A numeric vector.

s

A numeric value the order of prediction point.

n

A numeric vector for the size of all data.

a

A numeric value of mixing proportion.

parameters

A numeric vector of the parameter of distributions

conf

Confidence level for the test.

x

An object of class "bmixp".

...

Further argument to be passed to generic function

Details

Prediction of future observations if the data follows a mixture of two Beta distributions

Value

bmixp returns an object of class "bmixp", a list with the following components:

interval

the prediction interval.

lower

the lower bound of the interval.

upper

the upper bound of the interval.

r

the length of the data.

s

the order of the next observation.

n

the length of all the data.

parameters

the parameter estimate.

Generic function:

print

The print of a "bmixp" object shows the prediction point(s) for the future observation(s).

Author(s)

O. M. Khaled, K. S. Khalil and M. H. Harby.

References

H. M. Barakat, Magdy E. El-Adll, Amany E. Aly (2014), Prediction intervals of future observations for a sample random size from any continuous distribution. Mathematics and Computers in Simulation, volume 97, 1-13.

O. M. Khaled, K. S. Khalil and M. H. Harby (2023), PREDICTING FUTURE DATA FROM GAMMA-MIXTURE AND BETA-MIXTURE DISTRIBUTIONS AND APPLICATION TO THE RECOVERY RATE OF COVID-19. Advances and Applications in Statistics (AAIS), OCT, 2023.

See Also

PredictionR.

Examples

# prediction interval and point for the next observations based on mixture beta distribution
set.seed(123)
x1 <- 0.5*rbeta(7, 4, 2)+0.5*rbeta(7, 1, 3)
bmixp(x1,8,10,0.5,c(4,2,1,3),conf=0.95)

Prediction future points from mixture gamma distribution

Description

Construct a prediction point for future observations from mixture gamma distribution. Generic method is print.

Usage

gmixp(data, s, n, a ,parameters, conf=0.95)

## S3 method for class 'gmixp'
print(x, ...)

Arguments

data

A numeric vector.

s

A numeric value the order of prediction point.

n

A numeric vector for the size of all data.

a

A numeric value of mixing proportion.

parameters

A numeric vector of the parameter of distributions

conf

Confidence level for the test.

x

An object of class "gmixp".

...

Further argument to be passed to generic function

Details

Prediction of future observations if the data follows a mixture of two gamma distributions

Value

gmixp returns an object of class "gmixp", a list with the following components:

interval

the prediction interval.

lower

the lower bound of the interval.

upper

the upper bound of the interval.

r

the length of the data.

s

the order of the next observation.

n

the length of all the data.

parameters

the parameter estimate.

Generic function:

print

The print of a "gmixp" object shows the prediction point(s) for the future observation(s).

Author(s)

O. M. Khaled, K. S. Khalil and M. H. Harby.

References

H. M. Barakat, Magdy E. El-Adll, Amany E. Aly (2014), Prediction intervals of future observations for a sample random size from any continuous distribution. Mathematics and Computers in Simulation, volume 97, 1-13.

O. M. Khaled, K. S. Khalil and M. H. Harby (2023), PREDICTING FUTURE DATA FROM GAMMA-MIXTURE AND BETA-MIXTURE DISTRIBUTIONS AND APPLICATION TO THE RECOVERY RATE OF COVID-19. Advances and Applications in Statistics (AAIS), OCT, 2023.

See Also

PredictionR.

Examples

# prediction interval and point for the next observations based on mixture gamma distribution
#
set.seed(123)
x1 <- 0.5*rgamma(7, 4, 2)+0.5*rgamma(7, 1, 3)
gmixp(x1, 8, 10,0.5,c(4,2,1,3),conf=0.95)

Prediction future points from mixture normal distribution

Description

Construct a prediction point for future observations from mixture normal distribution. Generic method is print.

Usage

nmixp(data, s, n, a ,parameters, conf=0.95)

## S3 method for class 'nmixp'
print(x, ...)

Arguments

data

A numeric vector.

s

A numeric value the order of prediction point.

n

A numeric vector for the size of all data.

a

A numeric value of mixing proportion.

parameters

A numeric vector of the parameter of distributions

conf

Confidence level for the test.

x

An object of class "nmixp".

...

Further argument to be passed to generic function

Details

Prediction of future observations if the data follows a mixture of two normal distributions

Value

nmixp returns an object of class "nmixp", a list with the following components:

interval

the prediction interval.

lower

the lower bound of the interval.

upper

the upper bound of the interval.

r

the length of the data.

s

the order of the next observation.

n

the length of all the data.

parameters

the parameter estimate.

Generic function:

print

The print of a "nmixp" object shows the prediction point(s) for the future observation(s).

Author(s)

O. M. Khaled, K. S. Khalil and M. H. Harby.

References

H. M. Barakat, Magdy E. El-Adll, Amany E. Aly (2014), Prediction intervals of future observations for a sample random size from any continuous distribution. Mathematics and Computers in Simulation, volume 97, 1-13.

O. M. Khaled, K. S. Khalil and M. H. Harby (2023), PREDICTING FUTURE DATA FROM GAMMA-MIXTURE AND BETA-MIXTURE DISTRIBUTIONS AND APPLICATION TO THE RECOVERY RATE OF COVID-19. Advances and Applications in Statistics (AAIS), OCT, 2023.

See Also

PredictionR.

Examples

# prediction interval and point for the next observations based on mixture normal distribution
#
set.seed(123)
x1 <- 0.5*rnorm(7, 4, 2)+0.5*rnorm(7, 1, 3)
nmixp(x1, 8, 10,0.5,c(4,2,1,3),conf=0.95)

Prediction future points from mixture weibull distribution

Description

Construct a prediction point for future observations from mixture weibull distribution. Generic method is print.

Usage

wmixp(data, s, n, a ,parameters, conf=0.95)

## S3 method for class 'wmixp'
print(x, ...)

Arguments

data

A numeric vector.

s

A numeric value the order of prediction point.

n

A numeric vector for the size of all data.

a

A numeric value of mixing proportion.

parameters

A numeric vector of the parameter of distributions

conf

Confidence level for the test.

x

An object of class "wmixp".

...

Further argument to be passed to generic function

Details

Prediction of future observations if the data follows a mixture of two weibull distributions

Value

wmixp returns an object of class "wmixp", a list with the following components:

interval

the prediction interval.

lower

the lower bound of the interval.

upper

the upper bound of the interval.

r

the length of the data.

s

the order of the next observation.

n

the length of all the data.

parameters

the parameter estimate.

Generic function:

print

The print of a "wmixp" object shows the prediction point(s) for the future observation(s).

Author(s)

O. M. Khaled, K. S. Khalil and M. H. Harby.

References

H. M. Barakat, Magdy E. El-Adll, Amany E. Aly (2014), Prediction intervals of future observations for a sample random size from any continuous distribution. Mathematics and Computers in Simulation, volume 97, 1-13.

O. M. Khaled, K. S. Khalil and M. H. Harby (2023), PREDICTING FUTURE DATA FROM GAMMA-MIXTURE AND BETA-MIXTURE DISTRIBUTIONS AND APPLICATION TO THE RECOVERY RATE OF COVID-19. Advances and Applications in Statistics (AAIS), OCT, 2023.

See Also

PredictionR.

Examples

# prediction interval and point for the next observations based on mixture weibull distribution
#
set.seed(123)
x1 <- 0.5*rweibull(7, 4, 2)+0.5*rweibull(7, 1, 3)
wmixp(x1, 8, 10,0.5,c(4,2,1,3),conf=0.95)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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