similar to: trace in uniroot() ?

Despite my years with R, I didn't know about trace(). Thanks. However, my decades in the minimization and root finding game make me like having a trace that gives some info on the operation, the argument and the current function value. I've usually found glitches are a result of things like >= rather than > in tests etc., and knowing what was done is the quickest way to get there.

I tend to avoid the the trace/verbose arguments for the various root finders and optimizers and instead use the trace function or otherwise modify the function handed to the operator. You can print or plot the arguments or save them. E.g., > trace(ff, print=FALSE, quote(cat("x=", deparse(x), "\n", sep=""))) [1] "ff" > ff0 <- uniroot(ff, c(0, 10))

Le 18/02/2023 ? 21:44, J C Nash a ?crit?: > I wrote first cut at unirootR for Martin M and he revised and put in > Rmpfr. > > The following extends Ben's example, but adds the unirootR with trace > output. > > c1 <- 4469.822 > c2 <- 572.3413 > f <- function(x) { c1/x - c2/(1-x) }; uniroot(f, c(1e-6, 1)) > uniroot(f, c(1e-6, 1)) > library(Rmpfr) >

I wrote first cut at unirootR for Martin M and he revised and put in Rmpfr. The following extends Ben's example, but adds the unirootR with trace output. c1 <- 4469.822 c2 <- 572.3413 f <- function(x) { c1/x - c2/(1-x) }; uniroot(f, c(1e-6, 1)) uniroot(f, c(1e-6, 1)) library(Rmpfr) unirootR(f, c(1e-6, 1), extendInt="no", trace=1) This gives more detail on the iterations,

dear R experts--- I have (many) unidimensional root problems. think loc.of.root <- uniroot( f= function(x,a) log( exp(a) + a) + a, c(.,9e10), a=rnorm(1) ) $root (for some coefficients a, there won't be a solution; for others, it may exceed the domain. implied volatilities in various Black-Scholes formulas and variant formulas are like this, too.) except I don't need 1 root, but a

Dear Prof Azzalini, You have an interesting example of recursive use of uniroot(). [re-cited at the end] However, note that R currently has the same problem as S-plus: Uniroot() doesn't work reliably, recursively. When you found it to be better, you were just lucky. The relevant file in R's source, src/main/optimize.c says /* WARNING : As things stand, these routines should

curiosity---given that vector operations are so much faster than scalar operations, would it make sense to make uniroot vectorized? if I read the uniroot docs correctly, uniroot() calls an external C routine which seems to be a scalar function. that must be slow. I am thinking a vectorized version would be useful for an example such as of <- function(x,a) ( log(x)+x+a ) uniroot( of, c(

I have problems reading a file with more than one row to carry out mathematical calculations I have a a file of the form mu1 mu2 alpha beta Wsigma sigmaA b r 25 15 .05 .05 22 3 .3 .5 30 20 .1 .2 22 .3 .3 .5 I intend to read one row , carry out the calculations and then the next row with which I intend to do the same calculations. I do the following.

Hi all, This is probably a blindingly obvious question: Why does it matter in the uniroot function whether the f() values at the end points that you supply are of the same sign? For example: f <- function(x,y) {y-x^2+1} #this gives a warning uniroot(f,interval=c(-5,5),y=0) Error in uniroot(f, interval=c(-5, 5), y = 0) : f() values at end points not of opposite sign #this doesn't give a

Hi all, I am coding for finding the root of f(x)= phi(x) -alpha where phi(x) is the cumulative density function and alpha is constant . The problem right now is I can't get the "initialX" representing the root out of the while loop when ending , it seems to me it disappear when the loop ends accroding to the error message. I need help . Please suggest the cause or solution to this

Why not define your own functions based on d? e.g. myCumDist <- function(x) { integrate(d, lower=-Inf, upper=x)$value } myQuantile <- function(x) { uniroot(f=function(y) { h(y) - x }, interval=c(-5,5)) } # limits -5,5 should be replaced by your own which might require some fiddling e.g. d <- function(x) { exp(-x^2/2)/(sqrt(2*pi)) } # just an example for you to test with; use your own

Dear All, Apologies for not providing a reproducible example, but if I could, then I would be able to answer myself my question. Essentially, I am trying to fit a very complicated custom probability distribution to some data. Fitdistrplus does in principle everything which I need, but if require me to specify not only the density function d, but also the cumulative p and and inverse cumulative

R-Helpers: I would like to perform sample size calculations for an experiment. As part of this process, I would like to know how various assumptions affect the sample size calculation. For instance, one thing that I would like to know is how the calculated sample size changes as I vary the difference that I would like to detect. I tried the following first, but got the associated error

I am calling the uniroot function from inside another function using these lines (last two lines of the function) : d <- uniroot(k, c(.001, 250), tol=.05) return(d$root) The problem is that on occasion there's a problem with the values I'm passing to uniroot. In those instances uniroot stops and sends a message that it can't calculate the root because f.upper * f.lower is greater

As the original coder (in mid 1970s) of BFGS, CG and Nelder-Mead in optim(), I've been pushing for some time for their deprecation. They aren't "bad", but we have better tools, and they are in CRAN packages. Similarly, I believe other optimization tools in the core (optim::L-BFGS-B, nlm, nlminb) can and should be moved to packages (there are already 2 versions at least of LBFGS

Hi, I replaced my Rtools today as posted at http://www.murdoch-sutherland.com/Rtools/Rtools.exe Trying to build R-devel_2007-12-13.tar.gz without modifying MkRules gives the gfortran command not found error below. I am wondering if gfortran.exe is missing from (recent?) Rtools.exe or I am doing something wrong. Thanks to hints at Duncan's site, I worked around the error by adding

c1 <- 4469.822 c2 <- 572.3413 f <- function(x) { c1/x - c2/(1-x) }; uniroot(f, c(1e-6, 1)) uniroot(f, c(1e-6, 1)) provides a root at -6.00e-05, which is outside of the specified bounds. The default value of the "extendInt" argument to uniroot() is "no", as far as I can see ... $root [1] -6.003516e-05 $f.root [1] -74453981 $iter [1] 1 $init.it [1] NA

> On 6 Feb 2019, at 10:58, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > ..... > --------------------------------------------------------------------------- > > I summarize what has been reported till: > > Failure in these cases > ======== > 1. Kasper K ("Scientific Linux", self compiled R, using Intel's MKL > for BLAS/LAPACK)

Colleagues, I am trying to learn to use uniroot to solve a simple linear equation. I define the function, prove the function and a call to the function works. When I try to use uniroot to solve the equation I get an error message, Error in yfu n(x,10,20) : object 'x' not found. I hope someone can tell we how I can fix the problem

This below is not solvable with uniroot to find "a": fn=function(a){ b=(0.7/a)-a (1/(a+b+1))-0.0025 } uniroot(fn,c(-500,500)) gives "Error in uniroot(fn, c(-500, 500)) : f() values at end points not of opposite sign" I read R-help posts and someone wrote a function: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/92407.html but it is not very precise. Is there any