dcop {dsfa} | R Documentation |
Probablitiy density function, distribution and random number generation for copulas.
dcop(
W,
delta,
distr_cop = "normal",
rot = 0,
deriv_order = 0,
tri = NULL,
log.p = FALSE
)
pcop(W, delta = 0, distr_cop = "normal", rot = 0, log.p = FALSE)
rcop(n, delta = 0, distr_cop = "normal", rot = 0)
W |
numeric matrix of pseudo observations. Must have two columns. |
delta |
numeric vector of copula parameter |
distr_cop |
string, defines the copula family: |
rot |
integer determining the rotation for Archimedian copulas. Can be |
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). |
n |
positive integer; number of observations. |
A two-dimensional copula C(w_1, w_2, \delta)
is a multivariate cumulative distribution function for which the marginal probability distribution of w_1
and w_1
are uniform on the interval [0,1]
.
The parameter \delta
specifies the copula.
The functions pcop()
and rcop()
are wrapper functions for the pCopula()
and rCopula()
.
dcop
gives the density, pcop
gives the distribution function for a specified copula and rcop
generates random numbers, with given delta
.
dcop()
returns a derivs
object. For more details see trind()
and trind_generator()
.
pcop()
: distribution function for copula.
rcop()
: random number generation for copula.
Schepsmeier U, Stöber J (2014). “Derivatives and Fisher information of bivariate copulas.” Statistical Papers, 55(2), 525–542.
Hofert M, Kojadinovic I, Mächler M, Yan J (2018). Elements of copula modeling with R. Springer.
Other copula:
cop()
,
delta_bounds()
u=0.3; v=0.7; p=0.5
pdf <- dcop(W=cbind(u,v), delta=p, distr_cop="normal")
cdf <- pcop(W=cbind(u,v), delta=p, distr_cop="normal")
r <- rcop(n=100, delta=p, distr_cop="normal")