similar to: obsolete(?) nls package

Hi,all: I am running a nonlinear regression and there is a problem. There is a data frame: data p s x t 1 875.0 12392.5 11600 0.06967213 2 615.0 12332.5 12000 0.06967213 3 595.0 12332.5 12000 0.06967213 4 592.5 12337.0 12000 0.06967213 5 650.0 12430.0 12000 0.06967213 6 715.0 12477.5 12000 0.06967213 . . . . str(data): 'data.frame': 234 obs. of 4 variables:

Ravi Varadhan and I have been looking at UCMINF to try to identify why it gives occasional (but not reproducible) errors, seemingly on Windows only. There is some suspicion that its use of DBLEPR for finessing the Fortran WRITE() statements may be to blame. While I can find DBLEPR in Venables and Ripley, it doesn't get much mention after about 2000 in the archives, though it is in the R FAQ

Ravi Varadhan and I have been looking at UCMINF to try to identify why it gives occasional (but not reproducible) errors, seemingly on Windows only. There is some suspicion that its use of DBLEPR for finessing the Fortran WRITE() statements may be to blame. While I can find DBLEPR in Venables and Ripley, it doesn't get much mention after about 2000 in the archives, though it is in the R FAQ

The cautions people have given about starting values are worth heeding. That nlxb() does well in many cases is useful, but not foolproof. And John Fox has shown that the problem can be tackled very simply too. Best, JN On 2023-08-19 18:42, Paul Bernal wrote: > Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. > > Cheers, > Paul > > El El

Previously, I've posted queries about this, and thanks to postings and messages in response have recently had some success, to the extent that there is now a package called nlmrt on the R-forge project https://r-forge.r-project.org/R/?group_id=395 for solving nonlinear least squares problems that include small or zero residual problems via a Marquardt method using a call that mirrors the nls()

> On 25 Apr 2015, at 13:11 , Prof J C Nash (U30A) <nashjc at uottawa.ca> wrote: > > Hendrik pointed out it was the parentheses that gave the complaint. > Single quotes and no parentheses seem to satisfy R CMD check. Perhaps > that needs to be in the WRE. Well, it is in ?toTitleCase: ...However, unknown technical terms will be capitalized unless they are single

The following example gives output with a line length of 103 on my system. It is causing a nuisance in creating a vignette. Is there something other than e.g., options(width=60) I need to set? The Sweave FAQ suggests this should work. options(width=60) pastured <- data.frame( time=c(9, 14, 21, 28, 42, 57, 63, 70, 79), yield= c(8.93, 10.8, 18.59, 22.33, 39.35, 56.11, 61.73, 64.62,

I spent more time than I should have debugging a script because I wanted x<-seq(0,100)*0.1 but typed x<-seq(O:100)*0.1 seq(0:100) yields 1 to 101, Clearly my own brain to fingers fumble, but possibly one others may want to avoid it. JN

Hi all, Like a lot of people I noticed that I get different results when I use nls in R compared to the exponential fit in excel. A bit annoying because often the R^2 is higher in excel but when I'm reading the different topics on this forum I kind of understand that using R is better than excel? (I don't really understand how the difference occurs, but I understand that there is a

I've been creating some R tools that manipulate objective functions for optimization. In so doing, I create a character string with R code, and then want to have it in my workspace. Currently -- and this works fine -- I write the code out, then use source() to bring it in again. Example: cstr<-"jack<-function(x){\n cat(\"Silly x:\")\n print(x) \n }\n" write(cstr,

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]

Dear all, I am trying to analyze some non-linear data to which I have fit a curve of the following form: dum <- nls(y~(A + (B*x)/(C+x)), start = list(A=370,B=100,C=23000)) I am wondering if there is any way to determine meaningful quality of fit statistics from the nls function? A summary yields highly significant p-values, but it is my impression that these are questionable at best given

This message is about a curious difference in timing between two ways of computing the same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to be a factor of >1000. The code is below. We would be grateful if anyone can point out any egregious bad practice in our code, or enlighten us on why one approach is so much slower than the other. The problem

I was preparing a fix for a minor glitch in my optimx package and R CMD check gave an error that the title was not in title case. It is A Replacement and Extension of the optim() Function R CMD check suggests the incorrect form A Replacement and Extension of the Optim() Function 'Writing R Extensions' suggests single quotes, i.e., A Replacement and Extension of the 'optim()'

Recently I reported a small bug in optim's SANN method failing to report that it had exceeded the maximum function evaluation limit in the convergence code. This is a small enough matter that I was reluctant to create a full-blown bug report. Indeed in the optimx package Ravi Varadhan and I have been developing on r-forge (under the OptimizeR project) it was a minimal work around to fix

Hi all I have a set of data whose scatter plot shows a very nice power relationship. My problem is when I fit a Power Trend Line in an Excel spreadsheet, I get the model y= 44.23x^2.06 with an R square value of 0.72. Now, if I input the same data into R and use model< -nls(y~ a*x^b , trace=TRUE, data= my_data, start = c(a=40, b=2)) I get a solution with a = 246.29 and b = 1.51. I have tried

I ran the following script. I satisfied the constraint by making a*b a single parameter, which isn't always possible. I also ran nlxb() from nlsr package, and this gives singular values of the Jacobian. In the unconstrained case, the svs are pretty awful, and I wouldn't trust the results as a model, though the minimum is probably OK. The constrained result has a much larger sum of squares.

There are now several R geographic user groups, and a few have mailing lists on the R mailing list system. Thanks to Martin M, there's also a pointer to a page I'm maintaining to list/describe the groups. The page is at http://macnash.telfer.uottawa.ca/RUG.html Contact me if you have a listing. I'm prepared to wikify it if there is sufficient interest. John Nash

The problem is badly scaled -- parameters from very small to very big. nlmrt package manages a bit better, but scaling would likely make it and nls both happier. nlxb insists on having its data in a data frame. Here's my code rm(list=ls()) Laptop_sale <- c(1405, 1863,2027,2669,2938,5275,6595,6943,8621,10905,12420,22400,32380,31600,34900,43163,47838,47592) CuSale <- Laptop_sale time

Not paying close attention to detail, I entered the equivalent of pstr<-c("b1=200", "b2=50", "b3=0.3") when what I wanted was pnum<-c(b1=200, b2=50, b3=0.3) There was a list thread in 2010 that shows how to deal with un-named vectors, but the same lapply solution doesn't seem to work here i.e., pnum<-lapply(pstr, as.numeric) or similar vapply