search for: nlrob

I have a question regarding robust nonlinear regression with nlrob. I would like to place lower bounds on the parameters, but when I call nlrob with limits it returns the following error: "Error in psi(resid/Scale, ...) : unused argument(s) (lower = list(Asym = 1, mid = 1, scal = 1))" After consulting the documentation I noticed that upper and lower...

Dear all, I am not sure if this mail is for R-help or should be sent to R-devel instead, and therefore post to both. While using nlrob from package 'robustbase', I ran into the following problem: For psi-functions that can become zero (e.g. psi.bisquare), weights in the internal call to nls can become zero. Example: d <- data.frame(x=1:5,y=c(2,3,5,10,9)) d.nlrob <- nlrob(y ~ k*x,start=list(k=1),data=d,psi=p...

Hi all, I'm trying to fit a nonlinear regression by "nlrob": model3=nlrob(y~a1*x^a2,data=transient,psi=psi.bisquare, start=list(a1=0.02,a2=0.7),maxit=1000) However an error message keeps popping up saying that the function psi.bisquare doesn't exist. I also tried psi.huber, which is supposed to be the default for nlrob: model3=nlrob(y~a1*x^a2...

Dear all, ? I implemented a non-linear model using nls(...) and it works just fine. I now tried to run the same model using nlrob(...) which basically does the same but uses a more robust estimation procedure. My problem: I cannot seem to get nlrob(...) running. Irrespective of how I try to call the function, I always get the error message "Error in is.null(x) : 'x' is missing". I do not use the variable &qu...

I have a nonlinear model estimation problem with ~50,000 data records and a simple 3 parameter model (logistic type - please don't tell me that there are linear methods for such a problem). I run nls with constraints once to get a good initial parameter guess, then try to run nlrob to get improved estimates. The model is well-behaved for the parameters that come from nls - no huge values, NAs or infinities for the values of the independent variables. But nlrob fails immediately (on the first pass) with the error message > pxe2 <- nlrob(dpx ~ peFnc(tradeSide, tradeSz,...

What am I failing to understand here? The script below works fine if the dataset being used is DNase1 <- DNase[ DNase$Run == 1, ] per the example given in help(nlrob). Obviously, I am trying to understand how to use nls and nlrob to fit curves to data using R. #package=DAAG attach(codling) plot(pobs~dose) #next command returns 'step factor reduced below min factor error' m.nls <- nls( pobs ~ a/(1 + exp(( b - log(dose) )/c ) ),...

I'm working with 3 different data sets and applying this non-linear regression formula to each of them. nls(Y ~ (upper)/(1+10^(X-LOGEC50)), data=std_no_outliers, start=list(upper=max(std_no_outliers$Y),LOGEC50=-8.5)) Previously, all of the regressions were calculated in Prism, but I'd like to be able to automate the calculation process in a script, which is why I'm trying to move to

...t;us-ascii" I have a nonlinear model estimation problem with ~50,000 data records and a simple 3 parameter model (logistic type - please don't tell me that there are linear methods for such a problem). I run nls with constraints once to get a good initial parameter guess, then try to run nlrob to get improved estimates. The model is well-behaved for the parameters that come from nls - no huge values, NAs or infinities for the values of the independent variables. But nlrob fails immediately (on the first pass) with the error message > pxe2 <- nlrob(dpx ~ peFnc(tradeSide, tradeSz,...

...e: [R] Nonlinear Regression Parameter Shared Across Multiple Data Sets Message-ID: <AANLkTik0LhLW7EV4W2pmeO8zQdEyRmovbn7Lqm-zm9Oq at mail.gmail.com> Content-Type: text/plain As an addendum to my question, I'm attempting to apply the solution to the robust non-linear regression function nlrob from the robustbase package, and it doesn't work in that situation. I'm getting allRobustFit <- nlrob(Y ~ (upper)/(1+10^(X-LOGEC50[dset])), data=all ,start=list(upper=max(all$Y),LOGEC50=c(-8.5,-8.5,-8.5))) Error in nls(formula, data = data, start = start, algorithm = algorithm, : par...