gap {NovelDistns} | R Documentation |
Gull Alpha Power Family of distribution
Description
Computes the pdf, cdf, quantile, and random numbers and estimates the parameters of the exponentiated gull alpha power family of distribution specified by the cdf.
F(x,{\Theta}) = \left[\frac{\alpha G(x)}{\alpha^{G(x)}}\right]
where \theta
is the baseline family parameter vector.Here, the baseline G
refers to the cdf of: exponential, rayleigh and weibull.
Usage
rgap(n, dist, param)
qgap(p, dist, param, log.p = FALSE, lower.tail = TRUE)
pgap(data, dist, param, log.p = FALSE, lower.tail = TRUE)
dgap(data, dist, param, log = FALSE)
mlgap(data, dist,starts, method="SANN")
Arguments
n |
number of realizations to be generated. |
p |
quantile value between 0 and 1. |
data |
Vector of observations. |
param |
parameter vector |
log |
If |
log.p |
If |
lower.tail |
If |
dist |
The name of family's pdf including: " |
method |
the method for optimizing the log likelihood function. It can be one of |
starts |
initial values of |
Value
A vector of the same length as
data
, giving the pdf values computed atdata
.A vector of the same length as
data
, giving the cdf values computed atdata
.A vector of the same length as
p
, giving the quantile values computed atp
.A vector of the same length as
n
, giving the random numbers realizations.A sequence of goodness-of-fit statistics such as: Akaike Information Criterion (
AIC
), Consistent Akaike Information Criterion (CAIC
), Bayesian Information Criterion (BIC
), Hannan-Quinn information criterion (HQIC
), Cramer-von Misses statistic (CM
), Anderson Darling statistic (AD
), log-likelihood statistic (log
). The Kolmogorov-Smirnov (KS
) test statistic and correspondingp-value
and the convergence status.
Author(s)
Mutua Kilai, Gichuhi A. Waititu, Wanjoya A. Kibira
References
Muhammad et al (2020) A Gull Alpha Power Weibull distribution with applications to real and simulated data. https://doi.org/10.1371/journal.pone.0233080
Examples
x=runif(10,min=0,max=1)
rgap(10,"exp",c(0.3,0.5))
qgap(0.6,"exp",c(0.3,0.5))
pgap(x,"exp",c(0.3,0.5))
dgap(x,"exp",c(0.3,0.5))
mlgap(x,"exp",c(0.3,0.5))