gmixp {PivotalP} | R Documentation |
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)