numDerivLogf: Build Derivative Function for Log f
In docopulae: Optimal Designs for Copula Models

Description Usage Arguments Details Value See Also Examples

numDerivLogf/numDeriv2Logf builds a function that evaluates to the first/second derivative of log(f(y, theta, ...)) with respect to theta[[i]]/theta[[i]] and theta[[j]].

numDerivLogf(f, isLogf = FALSE, logZero = .Machine$double.xmin,
  logInf = .Machine$double.xmax/2, method = "Richardson",
  side = NULL, method.args = list())

numDeriv2Logf(f, isLogf = FALSE, logZero = .Machine$double.xmin,
  logInf = .Machine$double.xmax/2, method = "Richardson",
  method.args = list())

`f`	`function(y, theta, ...)`, where `theta` is a list of parameters. A joint probability density function.
`isLogf`	set to `TRUE` if `f` is already `log(f)`.
`logZero`	the value `log(f)` should return if `f` evaluates to `0`.
`logInf`	the value `log(f)` should return if `f` evaluates to `Inf`.
`method, side, method.args`	see `grad` and `hessian` in package numDeriv.

numDeriv produces NaNs if the log evaluates to (negative) Inf so you may want to specify logZero and logInf.

numDerivLogf passes method, side and method.args directly to numDeriv::grad.

numDeriv2Logf duplicates the internals of numDeriv::hessian to gain speed. The defaults for method.args are list(eps=1e-4, d=0.1, zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2).

numDerivLogf returns function(y, theta, i, ...) which evaluates to the first derivative of log(f(y, theta, ...)) with respect to theta[[i]].

numDeriv2Logf returns function(y, theta, i, j, ...) which evaluates to the second derivative of log(f(y, theta, ...)) with respect to theta[[i]] and theta[[j]].

grad and hessian in package numDeriv, buildf, DerivLogf, fisherI