| numericDeriv | R Documentation |
numericDeriv numerically evaluates the gradient of an expression.
numericDeriv(expr, theta, rho = parent.frame(), dir = 1,
eps = .Machine$double.eps ^ (1/if(central) 3 else 2), central = FALSE)
expr |
|
theta |
|
rho |
|
dir |
numeric vector of directions, typically with values in
|
eps |
a positive number, to be used as unit step size h for
the approximate numerical derivative (f(x+h)-f(x))/h or the
central version, see |
central |
logical indicating if central divided differences should be computed, i.e., (f(x+h) - f(x-h)) / 2h . These are typically more accurate but need more evaluations of f(). |
This is a front end to the C function numeric_deriv, which is
described in Writing R Extensions.
The numeric variables must be of type double and not integer.
The value of eval(expr, envir = rho) plus a matrix
attribute "gradient". The columns of this matrix are
the derivatives of the value with respect to the variables listed in
theta.
Saikat DebRoy saikat@stat.wisc.edu;
tweaks and eps, central options by R Core Team.
myenv <- new.env()
myenv$mean <- 0.
myenv$sd <- 1.
myenv$x <- seq(-3., 3., length.out = 31)
nD <- numericDeriv(quote(pnorm(x, mean, sd)), c("mean", "sd"), myenv)
str(nD)
## Visualize :
require(graphics)
matplot(myenv$x, cbind(c(nD), attr(nD, "gradient")), type="l")
abline(h=0, lty=3)
## "gradient" is close to the true derivatives, you don't see any diff.:
curve( - dnorm(x), col=2, lty=3, lwd=2, add=TRUE)
curve(-x*dnorm(x), col=3, lty=3, lwd=2, add=TRUE)
##
## IGNORE_RDIFF_BEGIN
# shows 1.609e-8 on most platforms
all.equal(attr(nD,"gradient"),
with(myenv, cbind(-dnorm(x), -x*dnorm(x))))
## IGNORE_RDIFF_END
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