Grokbase Groups R r-help May 2005
FAQ
Hi,

There is a nice article by Fornberg and Sloan (Acta Numerica 1994) on
various order accuracy (Taylor-series based) approximations for different
order derivatives. I had coded a couple of these in R for first and second
order derivatives, with truncation errors of orders 2 and 4.

Here is the code and a simple example demonstrating its accuracy for a
sharply oscillating function:

############################################
# A function to compute highly accurate first- and second-order derivatives
# From Fornberg and Sloan (Acta Numerica, 1994, p. 203-267; Table 1, page
213)
deriv <- function(x, fun, h=NULL, order=1, accur=4) {
macheps <- .Machine$double.eps

if (order==1) {
if(is.null(h)) h <- macheps^(1/3)* abs(x)
ifelse (accur==2, w <- c(-1/2,1/2), w <- c(1/12,-2/3, 2/3,-1/12))
ifelse (accur==2, xd <- x + h*c(-1,1), xd <- x + h*c(-2,-1,1,2))
return(sum(w*fun(xd))/h)
}
else if (order==2) {
if(is.null(h)) h <- macheps^(1/4)* abs(x)
ifelse (accur==2, w <- c(1,-2,1), w <- c(-1/12,4/3,-5/2,4/3,-1/12))
ifelse (accur==2, xd <- x + h*c(-1,0,1), xd <- x + h*c(-2,-1,0,1,2))
return(sum(w*fun(xd))/h^2)
}
}
############################################
func1 <- function(x){
sin(10*x) - exp(-x)
}
#############################################
curve(func1,from=0,to=5)
x <- 2.04
# first order derivative
numd1 <- deriv(x,f=func1)
exact <- 10*cos(10*x) + exp(-x)
c(numd1,exact,numd1/exact-1)
[1] 3.335371e-01 3.335371e-01 1.981793e-11
# second order derivative
numd2 <- deriv(x,f=func1,order=2)
exact <- -100*sin(10*x) - exp(-x)
c(numd2,exact,numd2/exact-1)
[1] -1.001093e+02 -1.001093e+02 -2.300948e-11

Hope this is helpful.

Best,
Ravi.

--------------------------------------------------------------------------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvaradhan at jhmi.edu
--------------------------------------------------------------------------
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-
bounces at stat.math.ethz.ch] On Behalf Of Peter Dalgaard
Sent: Thursday, May 05, 2005 8:51 PM
To: Uzuner, Tolga
Cc: 'r-help at stat.math.ethz.ch'; 'Berton Gunter'
Subject: Re: [R] Numerical Derivative / Numerical Differentiation of unkno
wnfunct ion

"Uzuner, Tolga" <tolga.uzuner@csfb.com> writes:
Ah... I searched for half an hour for this function... you know, the
help function in R could really be a lot better...

But wait a minute... looking at this, it appears you have to pass in
an expression. What if it is an unknown function, where you only
have a handle to the function, but you cannot see it's
implementation ? Will this work then ?

-----Original Message-----
From: Berton Gunter [mailto:gunter.berton at gene.com]
Sent: 05 May 2005 23:34
To: 'Uzuner, Tolga'; r-help at stat.math.ethz.ch
Subject: RE: [R] Numerical Derivative / Numerical Differentiation of
unknown funct ion


But...

See ?numericDeriv which already does it via a C call and hence is much
faster (and probably more accurate,too).
The expression passed to numericDeriv can easily be a call to .C or
similar.

Actually, numericDeriv can get you in trouble if the function is not
smooth enough. It basically just calculates (f(a+d)-f(a))/d where d is
on the order of 1e-7 * a for each parameter. Sometimes a larger d and
a higher order approximation is need to avoid getting stuck in the
rough.

(Yes, Bill, I do remember that you wanted an R News Programmer's Niche
item from me on this...)

--
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907

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