numDerivs: Functions for creating Gradient and Hessian matrices by...

Description Usage Arguments Details Value Note Author(s) Examples

Description

These two functions create Gradient and Hessian matrices by Richardson's central finite difference method of the partial derivatives for any expression.

Usage

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numGrad(expr, envir = .GlobalEnv)
numHess(expr, envir = .GlobalEnv)

Arguments

expr

an expression, such as expression(x/y).

envir

the environment to evaluate in.

Details

Calculates first- and second-order numerical approximation using Richardson's central difference formula:

f'_i(x) \approx \frac{f(x_1, …, x_i + d, …, x_n) - f(x_1, …, x_i - d, …, x_n)}{2d}

f''_i(x) \approx \frac{f(x_1, …, x_i + d, …, x_n) - 2f(x_1, …, x_n) + f(x_1, …, x_i - d, …, x_n)}{d^2}

Value

The numeric Gradient/Hessian matrices.

Note

The two functions are modified versions of the genD function in the 'numDeriv' package, but a bit more easy to handle because they use expressions and the function's x value must not be defined as splitted scalar values x[1], x[2], ... x[n] in the body of the function.

Author(s)

Andrej-Nikolai Spiess

Examples

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## Check for equality of symbolic  
## and numerical derivatives.
EXPR <- expression(2^x + sin(2 * y) - cos(z))
x <- 5
y <- 10
z <- 20

symGRAD <- evalDerivs(makeGrad(EXPR))
numGRAD <- numGrad(EXPR)
all.equal(symGRAD, numGRAD)

symHESS <- evalDerivs(makeHess(EXPR))
numHESS <- numHess(EXPR)
all.equal(symHESS, numHESS)

propagate documentation built on May 7, 2018, 1:03 a.m.