numerical_deriv {mirt} | R Documentation |
Compute numerical derivatives using forward/backward difference, central difference, or Richardson extrapolation.
numerical_deriv(
par,
f,
...,
delta = 1e-05,
gradient = TRUE,
type = "Richardson"
)
par |
a vector of parameters to find partial derivative at |
f |
the objective function being evaluated |
... |
additional arguments to be passed to |
delta |
the term used to perturb the |
gradient |
logical; compute the gradient terms? If FALSE then the Hessian is computed instead |
type |
type of difference to compute. Can be either |
Phil Chalmers rphilip.chalmers@gmail.com
## No test:
f <- function(x) 3*x[1]^3 - 4*x[2]^2
par <- c(3,8)
# grad = 9 * x^2 , -8 * y
(actual <- c(9 * par[1]^2, -8 * par[2]))
## [1] 81 -64
numerical_deriv(par, f, type = 'forward')
## [1] 81.00027 -64.00004
numerical_deriv(par, f, type = 'central')
## [1] 81 -64
numerical_deriv(par, f, type = 'Richardson') # default
## [1] 81 -64
# Hessian = h11 -> 18 * x, h22 -> -8, h12 -> h21 -> 0
(actual <- matrix(c(18 * par[1], 0, 0, -8), 2, 2))
## [,1] [,2]
## [1,] 54 0
## [2,] 0 -8
numerical_deriv(par, f, type = 'forward', gradient = FALSE)
## [,1] [,2]
## [1,] 54.00011 0.000000
## [2,] 0.00000 -7.999574
numerical_deriv(par, f, type = 'central', gradient = FALSE)
## [,1] [,2]
## [1,] 54.00004 0.000000
## [2,] 0.00000 -7.999645
numerical_deriv(par, f, type = 'Richardson', gradient = FALSE) # default
## [,1] [,2]
## [1,] 54 0
## [2,] 0 -8
## End(No test)