search for: nlsr

Dear friends - I have a function for the charge in a fluid (water) buffered with HEPES and otherwise only containing Na and Cl so that [Na] - [Cl] = SID (strong ion difference) goes from -1 mM to 1 mM. With known SID and total HEPES concentration I can calculate accurately the pH if I know 3 pK values for HEPES by finding the single root with uniroot Now, the problem is that there is some

...are useful, but it takes some time to sort them all out and make sense of the problem as a whole. Right now I'm getting ready to go to UseR!, so probably won't have time to spend sorting things out, though I'll have a go if I get time. David: Did you get a crash with the Mac version of nlsr? (You have the latest version I uploaded to CRAN.) I don't have Mac, just Linux and a very ancient Win XP. I suspect the latter is too old to take seriously. I use Win-Builder for package checks. I don't think there's anything seriously wrong with nlsr functions, but there is more to b...

...# Green point is brute-force solution # Red point is optimizer solution for myfun2 ##----------end On Sun, 18 Jun 2017, J C Nash wrote: > 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. > > Notes: > 1) nls...

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. Notes: 1) nlsr has been flagged with a check er...

Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. Cheers, Paul El El s?b, 19 de ago. de 2023 a la(s) 3:35 p. m., J C Nash < profjcnash at gmail.com> escribi?: > Why bother. nlsr can find a solution from very crude start. > > Mixture <- c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23, 20, 6, 13, 21, 3, 18, 15, > 26, 8, 22) > x1 <- c(69.98, 72.5, 77.6, 79.98, 74.98, 80.06, 69.98, 77.34, 69.99, > 67.49, 67.51, 77.63, > 72.5, 67.5, 80.1, 69.99, 72.49, 64....

https://cran.r-project.org/web/views/Optimization.html (Cran's optimization task view -- as always, you should search before posting) In general, nonlinear optimization with nonlinear constraints is hard, and the strategy used here (multiplying by a*b < 1000) may not work -- it introduces a discontinuity into the objective function, so gradient based methods may in particular be

Doesn't nls() expect that the lengths of vectors on both sides of the formula match (if both are supplied)? Perhaps it should check for that. -Bill On Thu, Jan 26, 2023 at 12:17 AM Dave Armstrong <darmst46 at uwo.ca> wrote: > Dear Colleagues, > > I recently answered [this question]() on StackOverflow that identified > what seems to be unusual behaviour with

...te: > Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. > > Cheers, > Paul > > El El s?b, 19 de ago. de 2023 a la(s) 3:35 p.?m., J C Nash <profjcnash at gmail.com <mailto:profjcnash at gmail.com>> escribi?: > > Why bother. nlsr can find a solution from very crude start. > > Mixture <- c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23, 20, 6, 13, 21, 3, 18, 15, 26, 8, 22) > x1 <- c(69.98, 72.5, 77.6, 79.98, 74.98, 80.06, 69.98, 77.34, 69.99, 67.49, 67.51, 77.63, > ? ? ? ? ?72.5, 67.5, 80.1, 69.99, 72.49,...

The derivative table resides in the function D. In S+ that table is extensible because it is written in the S language. R is faster but less flexible, since that table is programmed in C. It would be useful if R provided a mechanism for extending the derivative table, or barring that, provided a broader table. Currently unsupported mathematical functions of one argument include expm1, log1p,

Dear friends, Hope you are all doing well and having a great weekend. I have data that was collected on specific gravity and spectrophotometer analysis for 26 mixtures of NG (nitroglycerine), TA (triacetin), and 2 NDPA (2 - nitrodiphenylamine). In the dataset, x1 = %NG, x2 = %TA, and x3 = %2 NDPA. The response variable is the specific gravity, and the rest of the variables are the predictors.

I have never found the R symbolic differentiation helpful because my functions are typically quite complicated, but was prompted by Steve Ellison's suggestion to try it out in this case: ################# reprex (see reprex package) graphdta <- read.csv( text = "t,c 0,100 40,78 80,59 120,38 160,25 200,21 240,16 280,12 320,10 360,9 400,7 ", header = TRUE ) nd <- c( 100, 250,

...the Jacobian of the model function cannot be inverted and fails with >> the message "Singular gradient". I wish that R could have a more >> reliable built-in nonlinear least squares solver. (I could also be >> holding it wrong.) Meanwhile, we have excellent CRAN packages nlsr and >> minpack.lm: >> >> minpack.lm::nlsLM( >> ? pHobs ~ pHm(SID, pK1, pK2, pK3), >> ? data.frame(pHobs = pHobs, SID = SID), >> ? start = c(pK1 = pK1, pK2 = pK2, pK3 = pK3), >> ? # the following is also needed to avoid MINPACK failing to fit >> ? lowe...

...where where crossprod() of the Jacobian of the model function cannot be inverted and fails with the message "Singular gradient". I wish that R could have a more reliable built-in nonlinear least squares solver. (I could also be holding it wrong.) Meanwhile, we have excellent CRAN packages nlsr and minpack.lm: minpack.lm::nlsLM( pHobs ~ pHm(SID, pK1, pK2, pK3), data.frame(pHobs = pHobs, SID = SID), start = c(pK1 = pK1, pK2 = pK2, pK3 = pK3), # the following is also needed to avoid MINPACK failing to fit lower = rep(-1, 3), upper = rep(9, 3) ) # Nonlinear regression model # model:...

...d() > of the Jacobian of the model function cannot be inverted and fails with > the message "Singular gradient". I wish that R could have a more > reliable built-in nonlinear least squares solver. (I could also be > holding it wrong.) Meanwhile, we have excellent CRAN packages nlsr and > minpack.lm: > > minpack.lm::nlsLM( > pHobs ~ pHm(SID, pK1, pK2, pK3), > data.frame(pHobs = pHobs, SID = SID), > start = c(pK1 = pK1, pK2 = pK2, pK3 = pK3), > # the following is also needed to avoid MINPACK failing to fit > lower = rep(-1, 3), upper = rep(9, 3...

..., 16.99, 17.10) t=c(7, 37, 58, 79, 96, 110, 114, 127, 146, 156, 161, 169, 176, 182, 190, 197, 209, 218, 232, 240) lidata <- data.frame(y=y, t=t) #I use the method to fit a curve, but it is different from the real curve, #which can be seen in the figure. linFit <- lm(y ~ cos(t)) library(nlsr) #fullFit <- nls(y ~ A*cos(omega*t+C) + B, #start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.4)) #omega cannot be set to 1, don't know why. fullFit <- nlxb(y ~ A*cos(omega*t+C) + B, data=lidata, start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.04), trace=TRUE) co <-...

> On Apr 6, 2018, at 8:03 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > >> On Apr 6, 2018, at 3:43 AM, g l <gnulinux at gmx.com> wrote: >> >>> Sent: Friday, April 06, 2018 at 5:55 AM >>> From: "David Winsemius" <dwinsemius at comcast.net> >>> >>> >>> Not correct. You already have

...n from the qpcR package and got the following result: > LOF.test(nlregmod3) $pF [1] 0.97686 $pLR [1] 0.77025 Can I conclude from the LOF.test() results that my nonlinear regression model is significant/statistically significant? Where my nonlinear model was fitted as follows: nlregmod3 <- nlsr(formula=y ~ theta1 - theta2*exp(-theta3*x), data = mod14data2_random, start = list(theta1 = 0.37, theta2 = -exp(-1.8), theta3 = 0.05538)) And the data used to fit this model is the following: dput(mod14data2_random) structure(list(index = c(14L, 27L, 37L,...

If you know the period and want to fit phase and amplitude, this is equivalent to fitting a * sin + b * cos > >>> > I don't know how to set the approximate starting values. I'm not sure what you meant by that, but I suspect it's related to phase and amplitude. > >>> > Besides, does the method work for sine curve as well? sin is the same as cos with

I'm trying the different parameters, but don't know what the error is: Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates Thanks for any suggestions. On Tue, Jun 20, 2017 at 7:37 PM, Don Cohen <don-r-help at isis.cs3-inc.com> wrote: > > If you know the period and want to fit phase and amplitude, this is > equivalent to

?tryCatch -- Sent from my phone. Please excuse my brevity. On October 20, 2017 7:37:12 AM PDT, Evangelina Viotto <evangelinaviotto at gmail.com> wrote: >Hello I?m need fitt growth curve with data length-age. I want to >evaluate >which is the function that best predicts my data, to do so I compare >the >Akaikes of different models. I'm now need to evaluate if changing the