search for: selfstarting

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out

Hello, I'm trying to do some non-linear regression with 2 cell types and 4 tissue type treatments using selfStart models Following Ritz and Streibig (2009), I wrote the following routines: ##Selfstart expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){ exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL } expDecayAndConstantInflowModelInit <- function(mCall, LHS,

Hi All, Another nls related problem (for background, I'm migrating a complicated modelling package from S-plus to R). Below I've reduced this to the minimum necessary to demonstrate my problem (I think); the real situation is more complicated. Two similar selfStart functions, ssA and ssB. The 'initial' function for ssB modifies its arguments a little and then calls getInital

Hi, I am a student of Earthquake Engineering, and am new to R. Currently I try to run nonlinear regression analysis by R. My data has three variables: X, Y, and Z. Z is a function of (X, Y). My R script is as below. rm(list=ls()) # read in data Alldata <- read.table("~/Documents/R/Wu_data.dat", header=TRUE) # assign variables Z <- Wu[[1]] # N1,60 X <- Wu[[2]]

Hi Everyone, I'm trying to write selfStart non-linear models for use with nls. In these models some combinations of parameter values are illegal; the function value is undefined. That's OK when calling the function directly [e.g. SSmodel(x, pars...)]; I return an appropriate non-value such as NA or Inf. However, when called from nls [e.g. nls(y~SSmodel(x, pars...), ...)] those

Hi Everyone, I posted this a couple of weeks ago with no responses. My interface (via gmane) seemed a bit flakey at the time, so I'm venturing to repost with some additional information. I'm trying to write selfStart non-linear models for use with nls. In these models some combinations of parameter values are illegal; the function value is undefined. That's OK when calling the

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

hi all; I'm starting in PDC and need some help, i'm using samba for pdc but without ldap, and all time that a user make login, open a notepad with the content: [.ShellClassInfo] LocalizedResourceName=@%SystemRoot%\system32\shell32.dll,-21787 or [DeleteOnCopy] Owner=cesar Personalized=5 PersonalizedName=My documents and all folder have a document Desktop.ini... someone can say me why

Hello, list, I am making a self-starting nonlinear function to model the relation of tree height (H) and diameter (D) in a forest stand. The function I am trying is H=a*exp[b*(D+5.8)^(-c)]. To calculate the initial estimates of the parameters, I linearized the formula by taking logarithms and fixing the parameter c=1. Then I calculated the initial estimates of a and b using lm() on the

Hi Everyone, I'd omitted the non-optional 'parameters' argument to selfStart. Making this change to SSbatch gives the same (successful) result from the two calls to nls. SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate coefficients as mean of each batch xy <- sortedXyData(mCall[["Batch"]],

Hi Everyone, I seek guidance to avoid wasting a lot of time and doing things badly. Several times I've solved my problems, only to find that my solutions were clumsy and not robust. (see "nested" getInitial calls; variable scoping problems: Solved?? http://finzi.psych.upenn.edu/R/Rhelp02a/archive/139943.html for one truly horrible approach). I'm sure that I'm not the

Hi, I am a non-expert user of R. I am essaying the fit of two different functions to my data, but I receive two different error messages. I suppose I have two different problems here... But, of which nature? In the first instance I did try with some different starting values for the parameters, but without success. If anyone could suggest a sensible way to proceed to solve these I would be

Hi Rick Thanks to Dieter Menne I did manage to solve the problem of imposing bounds on the parameter space duirng an nlsList fit. He suggested using optim to optimize the parameters prior to each fit. This worked well for me as I had a customized selfStart function that then optimized the parameters for each individual separately. if you rewrote your selfStart function as: powrDpltInit <-

..... nls in package nls, {i.e. you type library(nls)} has so called self-starting models for a few classes of non-linear *regression* models. I don't know if this helps, since nlm() is not only for regression models. type ?selfStart and apropos("^SS") to get a list of predefined selfstarting models, and then, e.g. ?SSlogis Further you may need to learn more about non-linear regression in general. E.g., Bates, D.M. and Watts, D.G. (1988) Nonlinear Regression Analysis and Its Applications; Wiley. and -- for selfstarting (and more general, see help(gls, package="nlme&...

Hello, nls-users, is it possible to pass additional parameters to the model function that are known and groupwise constant with nlsList? I could not find something like a "keep this fixed" option in the documentation and the code (my fault...?) The current workaround is to break the problem down into groups and use globals to pass the constant parameters, but it is ugly code and

Hi everyone, I thought that for a selfStart function, these two should be exactly equivalent > nls(Aform, DF) > nls(Aform, DF, start=getInitial(Aform, DF)) but in this example that is not the case in R (although it is in S-plus V6.2) ------------------------------ SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate

Hello, I'm trying to adjust a non linear model in which the biological response variable (ratio of germinated fungus spores) is dependent on 2 covariates (temperature and time). The response to temperature is modeled by a kind of beta function with 2 parameters (optimal and maximum temperatures) and the time function is a 2-parameter Weibull. Adjustments with nls or gnls work, but I need to

Hello there! I got some data with x and y values. there are some y == 0. This is a problem for the selfstarting regression model SSlogis. The regression works if I use a non selfstarting model. The formula is the same. But this needs very detailed information of the starting list. I dont have this Information all the time. An easy solution for selfstart model could be to add 1E-100 to the y==0 values. That...

Hello, I am doing several self-starting growth functions for using with nls(). When I list the self-starting functions included in nls library, for instance, SSlogis, there is: > SSlogis function (input, Asym, xmid, scal) ... <environment: 03476D20> attr(,"class") [1] "selfStart" What is this <environment: 03476D20> instruction? By using deriv() and then

Hi, I have great problems with my work in R. I look for to model the growth of fish. I have "Longitudinal data", a serie of repeated measures for each individual. Using the corresponding packages "nlme" in R. I treat to fit to the data different growth functions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I