| 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 |
... |
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 |
... |
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 |
... |
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 |
... |
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)