Neutrosophic Normal {ntsDists} | R Documentation |
Density, distribution function, quantile function and random
generation for the neutrosophic generalized exponential
distribution with parameters mean
= \mu_N
and standard deviation
sd
= \sigma_N
.
dnsNorm(x, mean, sd)
pnsNorm(q, mean, sd, lower.tail = TRUE)
qnsNorm(p, mean, sd)
rnsNorm(n, mean, sd)
x |
a vector or matrix of observations for which the pdf needs to be computed. |
mean |
the mean, which must be an interval. |
sd |
the standard deviations that must be positive. |
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 normal distribution with parameters mean
\mu_N
and standard deviation \sigma_N
has density function
f_N(x) = \frac{1}{\sigma_N \sqrt{2 \pi}} \exp\{\left(\frac{\left(X-\mu_N\right)^2}{2 \sigma_N^2}\right)
}
for \mu_N \in (\mu_L, \mu_U)
, the mean which must be an interval, and
\sigma_N \in (\sigma_L, \sigma_U)
, the standard deviation which must
also be a positive interval, and -\infty < x < \infty
.
dnsNorm
gives the density function
pnsNorm
gives the distribution function
qnsNorm
gives the quantile function
rnsNorm
generates random variables from the neutrosophic normal distribution.
Patro, S. and Smarandache, F. (2016). The Neutrosophic Statistical Distribution, More Problems, More Solutions. Infinite Study.
data(balls)
dnsNorm(x = balls, mean = c(72.14087, 72.94087), sd = c(37.44544, 37.29067))
pnsNorm(q = 5, mean = c(72.14087, 72.94087), sd = c(37.44544, 37.29067))
# Calculate quantiles
qnsNorm(p = c(0.25, 0.5, 0.75), mean = c(9.1196, 9.2453), sd = c(10.1397, 10.4577))
# Simulate 10 values
rnsNorm(n = 10, mean = c(4.141, 4.180), sd = c(0.513, 0.521))