Neutrosophic Generalized Pareto {ntsDists} | R Documentation |
Density, distribution function, quantile function and random generation for
the neutrosophic generalized pareto distribution with parameters shape
= \alpha_N
and scale
=\beta_N
.
dnsGenPareto(x, shape, scale)
pnsGenPareto(q, shape, scale, lower.tail = TRUE)
qnsGenPareto(p, shape, scale)
rnsGenPareto(n, shape, scale)
x |
a vector or matrix of observations for which the pdf needs to be computed. |
shape |
the shape parameter, which must be a positive interval. |
scale |
the scale parameter, which must be a positive interval. |
q |
a vector or matrix of quantiles for which the cdf needs to be computed. |
lower.tail |
logical; if TRUE (default), probabilities are
|
p |
a vector or matrix of probabilities for which the quantile needs to be computed. |
n |
number of random values to be generated. |
The neutrosophic generalized pareto distribution with parameters \alpha_N
and
\beta_N
has density
f_N(x)=\frac{1}{\beta_N}\left(1+\frac{\alpha_N x_N}{\beta_N} \right)^{-\frac{1}{\alpha_N}-1}
for x \ge 0
, \alpha_N \in (\alpha_L, \alpha_U)
, the shape
parameter which must be a positive interval and
\beta_N \in (\beta_L, \beta_U)
, the scale parameter which
must be a positive interval.
dnsGenPareto
gives the density function
pnsGenPareto
gives the distribution function
qnsGenPareto
gives the quantile function
rnsGenPareto
generates random variables from the neutrosophic generalized pareto distribution.
Eassa, N. I., Zaher, H. M., & El-Magd, N. A. A. (2023). Neutrosophic Generalized Pareto Distribution, Mathematics and Statistics, 11(5), 827–833.
data(remission)
dnsGenPareto(x = remission, shape = c(1.1884, 1.1896), scale = c(7.6658, 7.7796))
pnsGenPareto(q = 20, shape = c(1.1884, 1.1896), scale = c(7.6658, 7.7796))
# Calculate quantiles
qnsGenPareto(p = c(0.25, 0.5, 0.75), shape = c(1.1884, 1.1896), scale = c(7.6658, 7.7796))
# Simulate 10 numbers
rnsGenPareto(n = 10, shape = c(1.1884, 1.1896), scale = c(7.6658, 7.7796))