dcomperr {dsfa}R Documentation

Composed error term distribution

Description

Probablitiy density, distribution, quantile function and random number generation for the composed error term distribution.

Usage

dcomperr(
  x = 0,
  mu = 0,
  sigma_v = 1,
  par_u = 1,
  s = -1,
  family = "normhnorm",
  deriv = 0,
  tri = NULL,
  log.p = FALSE,
  check = TRUE
)

pcomperr(
  q,
  mu = 0,
  sigma_v = 1,
  par_u = 1,
  s = -1,
  family = "normhnorm",
  deriv = 0,
  tri = NULL,
  lower.tail = TRUE,
  log.p = FALSE,
  check = TRUE
)

qcomperr(
  p,
  mu = 0,
  sigma_v = 1,
  par_u = 1,
  s = -1,
  family = "normhnorm",
  lower.tail = TRUE,
  log.p = FALSE,
  check = TRUE
)

rcomperr(
  n,
  mu = 0,
  sigma_v = 1,
  par_u = 1,
  s = -1,
  family = "normhnorm",
  check = TRUE
)

Arguments

x

vector of quantiles.

mu

vector of \mu

sigma_v

vector of \sigma_V. Must be positive.

par_u

vector of parameter of the (in)efficiency term. Must be positive.

s

s=-1 for production and s=1 for cost function.

family

normhnorm for normal-halfnormal and normexp for normal-exponential distribution.

deriv

derivative of order deriv of the log density. Available are 0,2 and 4.

tri

optional, index arrays for upper triangular matrices, generated by trind.generator() and supplied to chainrule().

log.p

logical; if TRUE, probabilities p are given as log(p).

check

logical; if TRUE, check inputs.

q

vector of quantiles.

lower.tail

logical; if TRUE (default), probabilities are P[X \le x] otherwise, P[X > x].

p

vector of probabilities.

n

number of observations.

Details

This is wrapper function for the normal-halfnormal and normal-exponential distribution. A random variable \mathcal{E} follows a composed error distribution if \mathcal{E} = V + s \cdot U , where V \sim N(\mu, \sigma_V^2) and U \sim HN(0,\sigma_U^2) or U \sim Exp(0,\sigma_U^2). For more details see dnormhnorm and dnormexp. Here, s=-1 for production and s=1 for cost function.

Value

dcomperr gives the density, pcomperr gives the distribution function, qcomperr gives the quantile function, and rcomperr generates random numbers, with given parameters. If the derivatives are calculated these are provided as the attributes gradient, hessian, l3 and l4 of the output of the density.

Functions

References

Examples

pdf <- dcomperr(x=seq(-3, 3, by=0.1), mu=1, sigma_v=2, par_u=3, s=-1, family="normhnorm")
cdf <- pcomperr(q=seq(-3, 3, by=0.1), mu=1, sigma_v=2, par_u=3, s=-1, family="normhnorm")
q <- qcomperr(p=seq(0.1, 0.9, by=0.1), mu=1, sigma_v=2, par_u=3, s=-1, family="normhnorm")
r <- rcomperr(n=100, mu=1, sigma_v=2, par_u=3, s=-1, family="normhnorm")


[Package dsfa version 1.0.1 Index]