| dcomper | R Documentation |
Probablitiy density function, distribution, quantile function and random number generation for the composed-error distribution
dcomper(
x,
mu = 0,
sigma_v = 1,
sigma_u = 1,
s = -1,
distr = "normhnorm",
deriv_order = 0,
tri = NULL,
log.p = FALSE
)
pcomper(
q,
mu = 0,
sigma_v = 1,
sigma_u = 1,
s = -1,
distr = "normhnorm",
deriv_order = 0,
tri = NULL,
log.p = FALSE
)
qcomper(
p,
mu = 0,
sigma_v = 1,
sigma_u = 1,
s = -1,
distr = "normhnorm",
log.p = FALSE
)
rcomper(n, mu = 0, sigma_v = 1, sigma_u = 1, s = -1, distr = "normhnorm")
x |
numeric vector of quantiles. |
mu |
numeric vector of |
sigma_v |
numeric vector of |
sigma_u |
numeric vector of |
s |
integer; |
distr |
string; determines the distribution: |
deriv_order |
integer; maximum order of derivative. Available are |
tri |
optional; index matrix for upper triangular, generated by |
log.p |
logical; if TRUE, probabilities p are given as log(p). |
q |
numeric vector of quantiles. |
p |
numeric vector of probabilities. |
n |
positive integer; number of observations. |
This is wrapper function for the normal-halfnormal and normal-exponential distribution. A random variable X follows a composed error distribution if X = V + s \cdot U , where V \sim N(\mu, \sigma_V^2) and U \sim HN(0,\sigma_U^2) or U \sim Exp(\sigma_U^2).
For more details see dnormhnorm and dnormexp. Here, s=-1 for production and s=1 for cost function.
dcomper() gives the density, pcomper() give the distribution function, qcomper() gives the quantile function, and rcomper() generates random numbers, with given parameters.
dcomper() and pcomper() returns a derivs object.
pcomper(): distribution function for the composed-error distribution.
qcomper(): quantile function for the composed-error distribution.
rcomper(): random number generation for the composed-error distribution.
aigner1977formulationdsfa
kumbhakar2015practitionerdsfa
schmidt2020analyticdsfa
gradshteyn2014tabledsfa
azzalini2013skewdsfa
Other distribution:
dcomper_mv(),
dnormexp(),
dnormhnorm()
pdf <- dcomper(x=5, mu=1, sigma_v=2, sigma_u=3, s=-1, distr="normhnorm")
cdf <- pcomper(q=5, mu=1, sigma_v=2, sigma_u=3, s=-1, distr="normhnorm")
q <- qcomper(p=seq(0.1, 0.9, by=0.1), mu=1, sigma_v=2, sigma_u=3, s=-1, distr="normhnorm")
r <- rcomper(n=10, mu=1, sigma_v=2, sigma_u=3, s=-1, distr="normhnorm")
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