msnburr2a | R Documentation |
To calculate density function, distribution funcion, quantile function, and build data from random generator function for the MSNBurr distribution.
dmsnburr2a(x, mu = 0, sigma = 1, alpha = 1, log = FALSE)
pmsnburr2a(q, mu = 0, sigma = 1, alpha = 1, lower.tail = TRUE, log.p = FALSE)
qmsnburr2a(p, mu = 0, sigma = 1, alpha = 1, lower.tail = TRUE, log.p = FALSE)
rmsnburr2a(n, mu = 0, sigma = 1, alpha = 1)
x , q |
vector of quantiles. |
mu |
a location parameter. |
sigma |
a scale parameter. |
alpha |
a shape parameter |
log , log.p |
logical; if TRUE, probabilities p are given as log(p), The default value of this parameter is FALSE. |
lower.tail |
logical;if TRUE (default), probabilities are
|
p |
vectors of probabilities. |
n |
number of observations. |
MSNBurr-IIa Distribution
The MSNBurr-IIa distribution with parameters \mu
, \sigma
, and \alpha
has density:
f(x |\mu,\sigma,\alpha)=\frac{\omega}{\sigma}\exp{\left(\omega{\left(\frac{x-\mu}{\sigma}\right)}\right)}{{\left(1+\frac{\exp{\left(\omega{(\frac{x-\mu}{\sigma})}\right)}}{\alpha}\right)}^{-(\alpha+1)}}
where -\infty < x < \infty, -\infty < \mu< \infty, \sigma>0, \alpha>0,
\omega = \frac{1}{\sqrt{2\pi}} {\left(1+\frac{1}{\alpha}\right)^{\alpha+1}}
dmsnburr2a
gives the density, pmsnburr2a
gives the distribution
function, qmsnburr2a
gives the quantile function and rmsnburr2a
generates
random numbers.
Achmad Syahrul Choir and Nur Iriawan
Choir, A. S. (2020). The New Neo-Normal DDistributions and their Properties. Disertation. Institut Teknologi Sepuluh Nopember.
library("neodistr")
dmsnburr2a(7, mu=0, sigma=1, alpha=0.1)
plot(function(x) dmsnburr2a(x, alpha=0.1), -3, 20,
main = "Right Skew MSNBurr-IIa Density ",ylab="density")
p=pmsnburr2a(4, mu=0, sigma=1, alpha=1)
p
q=qmsnburr2a(p, mu=0, sigma=1, alpha=1)
q
qmsnburr2a(0.5, mu=0, sigma=1, alpha=1)
r=rmsnburr2a(10000, mu=0, sigma=1, alpha=0.1)
head(r)
hist(r, xlab = 'MSNBurr random number', ylab = 'Frequency',
main = 'Distribution of MSNBurr-IIa Random Number ')
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