similar to: .External interface

I just uploaded to CRAN a prototype nls (nonlinear least squares) library for R. It is far from being finished but I am asking for this preliminary version to be installed in the src/contrib/Devel section of CRAN so others can see the use of function closures to emulate the behaviour of objects in languages like Java. It will be a couple of days before the file is installed in CRAN and has a

Dear All, I was trying to call a self-defined function that performs nonlinear regression and gets the corresponding prediction upper limit using nls2 package. However, weird thing happened. When I called the function in the main program, an error message "fitted(nlsmodel): object 'nlsmodel' not found" came up. But when I directly ran the codes inside the function, no error came

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Hi I would like to start by thanking everyone that replied. Thank you for the information, and for the comments about how or why you use it and thankx for the encouragement re my talk.... Please note that I do not claim to have ellicited a reply from all users of R, this is a quick survey not a census. I have tried to break the responces down to the following categories: Where: (hopefully

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Colleagues, I am using "nls" successfully (2.11.1, OS X) but I am having difficulties retrieving part of the output - residual sum of squares. I have assigned the output to FIT: > > FIT > Nonlinear regression model > model: NEWY ~ PMESOR + PAMPLITUDE * cos(2 * pi * (NEWX - POFFSET)/PERIOD) > data: parent.frame() > PMESOR PAMPLITUDE POFFSET >

I got starting values as follows: Noting that the minimum data value is .38, I fit the linear model log(y - .37) ~ x to get intercept = -1.8 and slope = -.055. So I used .37, exp(-1.8) and -.055 as the starting values for theta0, theta1, and theta2 in the nonlinear model. This converged without problems. Cheers, Bert On Sun, Aug 20, 2023 at 10:15?AM Paul Bernal <paulbernal07 at

Estimado Gabriel Trujillo Paucar Del str en el segundo renglón está fitted. ¿Usted pregunta algo como el siguiente ejemplo? fitted(A) O comprendí mal su pregunta. Javier Marcuzzi El 28 de mayo de 2013 13:25, Gabriel Trujillo Paucar < gabriel_enter@hotmail.com> escribió: > Saludos a todos, > > > > Quisiera saber como ocultar algunos componentes de una lista para que

I have data vectors x and y both with 179 observations. I'm trying to fit a nonlinear model with five parameters using nls. The formula is only defined within a range of x-values, it should be zero otherwise, thus my attempted use of ifelse: > df<-data.frame(x,y) >

Dear R-users, I am trying to create a model using the NLS function, such that: Y = f(X) + q + e Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a covariate (continous variable) and e is an error term. I know that you can create multiple nonlinear regressions where x is polynomial for example, but is it possible to do this kind of thing when x is a function with unknown

Dear friends, This is the dataset I am currently working with: >dput(mod14data2_random) structure(list(index = c(14L, 27L, 37L, 33L, 34L, 16L, 7L, 1L, 39L, 36L, 40L, 19L, 28L, 38L, 32L), y = c(0.44, 0.4, 0.4, 0.4, 0.4, 0.43, 0.46, 0.49, 0.41, 0.41, 0.38, 0.42, 0.41, 0.4, 0.4 ), x = c(16, 24, 32, 30, 30, 16, 12, 8, 36, 32, 36, 20, 26, 34, 28)), row.names = c(NA, -15L), class =

I apologize for posting this in essence the second time (no light at the end of the tunnel yet..): is there a way to enforce that "nls" takes both, the data *and* the model definition from the parent environment? the following fragment shows the problem. #======== cut here========== wrapper <- function (choose=0) { x <- seq(0,2*pi,len=100) y <- sin(1.5*x); y <-

Perdón por el goteo. Gabriel, aquí tienes el código de nls: http://svn.r-project.org/R/trunk/src/library/stats/R/nls.R Si buscas "print.nls" verás que corresponde al modo en que se muestra el objeto que retorna la función. En definitiva: Tú cuando creas una función, el objeto que retornas se va a mostrar completamente salvo que exista una función print en la clase a la cual pertenece

Oh, sorry; I changed signs in the model, fitting theta0 + theta1*exp(theta2*x) So for theta0 - theta1*exp(-theta2*x) use theta1= -.exp(-1.8) and theta2 = +.055 as starting values. -- Bert On Sun, Aug 20, 2023 at 11:50?AM Paul Bernal <paulbernal07 at gmail.com> wrote: > Dear Bert, > > Thank you so much for your kind and valuable feedback. I tried finding the > starting

Dear Bert, Thank you so much for your kind and valuable feedback. I tried finding the starting values using the approach you mentioned, then did the following to fit the nonlinear regression model: nlregmod2 <- nls(y ~ theta1 - theta2*exp(-theta3*x), start = list(theta1 = 0.37, theta2 = exp(-1.8), theta3 =

Dear Bert, Thank you for your extremely valuable feedback. Now, I just want to understand why the signs for those starting values, given the following: > #Fiting intermediate model to get starting values > intermediatemod <- lm(log(y - .37) ~ x, data=mod14data2_random) > summary(intermediatemod) Call: lm(formula = log(y - 0.37) ~ x, data = mod14data2_random) Residuals: Min

Basic algebra and exponentials/logs. I leave those details to you or another HelpeR. -- Bert On Sun, Aug 20, 2023 at 12:17?PM Paul Bernal <paulbernal07 at gmail.com> wrote: > Dear Bert, > > Thank you for your extremely valuable feedback. Now, I just want to > understand why the signs for those starting values, given the following: > > #Fiting intermediate model to get

Earlier, I posted a question concerning the syntax of nls() when the nonlinear function within the call is specified outside of nls(). Sundar showed me how to do this. Now I have a slightly more complex problem that is analogous to a broken-stick regression but in a non-linear context. I want my data to be fit to a non-rectangular hyperbola if the value of the independent variable (I) is less

hi everybody, is there a canonical way to get hold of the "trace=TRUE" output from nls, i.e. to copy it to a R variable (or at least to an external log file)? I have only found the possibility to "fix(nlsModel)" (and than the correct copy of that: namespace function ...) within the R-session by modifying the trace() definition within nlsModel. not really good for everyday