This function evaluates the derivative of a given parametric bivariate copula density with respect to its parameter(s) or one of its arguments.
BiCopDeriv(u1, u2, family, par, par2 = 0, deriv = "par", log = FALSE,
obj = NULL, check.pars = TRUE)
numeric vectors of equal length with values in [0,1].
integer; single number or vector of size length(u1)
;
defines the bivariate copula family:
0
= independence copula
1
= Gaussian copula
2
= Student t copula (t-copula)
3
= Clayton copula
4
= Gumbel copula
5
= Frank copula
6
= Joe copula
13
= rotated Clayton copula (180 degrees; ``survival Clayton'')
14
= rotated Gumbel copula (180 degrees; ``survival Gumbel'')
16
= rotated Joe copula (180 degrees; ``survival Joe'')
23
= rotated Clayton copula (90 degrees)
24
= rotated Gumbel copula (90 degrees)
26
= rotated Joe copula (90 degrees)
33
= rotated Clayton copula (270 degrees)
34
= rotated Gumbel copula (270 degrees)
36
= rotated Joe copula (270 degrees)
numeric; single number or vector of size length(u1)
;
copula parameter.
integer; single number or vector of size length(u1)
;
second parameter for the t-Copula; default is par2 = 0
, should be an
positive integer for the Students's t copula family = 2
.
Derivative argument
"par"
= derivative with respect to the first parameter (default)
"par2"
= derivative with respect to the second parameter
(only available for the t-copula)
"u1"
= derivative with respect to the first argument u1
"u2"
= derivative with respect to the second argument u2
Logical; if TRUE
than the derivative of the log-likelihood
is returned (default: log = FALSE
; only available for the derivatives
with respect to the parameter(s) (deriv = "par"
or deriv =
"par2"
)).
BiCop
object containing the family and parameter
specification.
logical; default is TRUE
; if FALSE
, checks
for family/parameter-consistency are omitted (should only be used with
care).
A numeric vector of the bivariate copula derivative
of the copula family
with parameter(s) par
, par2
with respect to deriv
,
evaluated at u1
and u2
.
If the family and parameter specification is stored in a BiCop
object obj
, the alternative version
BiCopDeriv(u1, u2, obj, deriv = "par", log = FALSE)
can be used.
Schepsmeier, U. and J. Stoeber (2014). Derivatives and Fisher information of bivariate copulas. Statistical Papers, 55 (2), 525-542. http://link.springer.com/article/10.1007/s00362-013-0498-x.
RVineGrad
, RVineHessian
,
BiCopDeriv2
, BiCopHfuncDeriv
,
BiCop
# NOT RUN {
## simulate from a bivariate Student-t copula
set.seed(123)
cop <- BiCop(family = 2, par = -0.7, par2 = 4)
simdata <- BiCopSim(100, cop)
## derivative of the bivariate t-copula with respect to the first parameter
u1 <- simdata[,1]
u2 <- simdata[,2]
BiCopDeriv(u1, u2, cop, deriv = "par")
## estimate a Student-t copula for the simulated data
cop <- BiCopEst(u1, u2, family = 2)
## and evaluate its derivative w.r.t. the second argument u2
BiCopDeriv(u1, u2, cop, deriv = "u2")
# }
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