| Title: | Prediction for Future Data from Mixture Distributions Gamma, Beta, Weibull and Normal |
|---|---|
| Description: | Functions to get prediction intervals and prediction points of future observations from mixture distributions like gamma, beta, Weibull and normal. |
| Authors: | O. M. Khaled [aut], K. S. Khalil [aut, cre], M. H. Harby [aut] |
| Maintainer: | K. S. Khalil <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.2 |
| Built: | 2024-11-29 13:53:41 UTC |
| Source: | CRAN |
Construct a prediction point for future observations from mixture beta distribution. Generic method is print.
bmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'bmixp' print(x, ...)bmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'bmixp' print(x, ...)
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 |
Prediction of future observations if the data follows a mixture of two Beta distributions
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:
printThe print of a "bmixp" object shows the prediction point(s) for the future observation(s).
O. M. Khaled, K. S. Khalil and M. H. Harby.
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.
PredictionR.
# 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 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)
Construct a prediction point for future observations from mixture gamma distribution. Generic method is print.
gmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'gmixp' print(x, ...)gmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'gmixp' print(x, ...)
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 |
Prediction of future observations if the data follows a mixture of two gamma distributions
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:
printThe print of a "gmixp" object shows the prediction point(s) for the future observation(s).
O. M. Khaled, K. S. Khalil and M. H. Harby.
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.
PredictionR.
# 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 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)
Construct a prediction point for future observations from mixture normal distribution. Generic method is print.
nmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'nmixp' print(x, ...)nmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'nmixp' print(x, ...)
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 |
Prediction of future observations if the data follows a mixture of two normal distributions
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:
printThe print of a "nmixp" object shows the prediction point(s) for the future observation(s).
O. M. Khaled, K. S. Khalil and M. H. Harby.
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.
PredictionR.
# 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 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)
Construct a prediction point for future observations from mixture weibull distribution. Generic method is print.
wmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'wmixp' print(x, ...)wmixp(data, s, n, a ,parameters, conf=0.95) ## S3 method for class 'wmixp' print(x, ...)
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 |
Prediction of future observations if the data follows a mixture of two weibull distributions
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:
printThe print of a "wmixp" object shows the prediction point(s) for the future observation(s).
O. M. Khaled, K. S. Khalil and M. H. Harby.
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.
PredictionR.
# 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)# 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)