similar to: summary of problem with mCall function.

Hello. Following pages 342-347 of Pinheiro & Bates, I am trying to write a self-starting nonlinear function (a non-rectagular hyperbola) to be used in nonlinear least squares regression (and eventually for a mixed model). When I use the getInitial function for my self-starting function I get the following error message: > getInitial(photo~NRhyperbola(Irr,theta,Am,alpha,Rd),dat) Error

Following on my posting of this morning, concerning a problem that I am having constructing a self-starting function for use with nls (and eventually with nlsList and nlme), the following is the self-starting function called NRhyperbola: > NRhyperbola function (Irr,theta,Am,alpha,Rd) { # Am is the maximum gross photosynthetic rate # Rd is the dark resiration rate (positive value) #

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

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

I am not certain how nlme works so I followed an example from the web ( http://www.menne-biomed.de/gastempt/gastempt1.html). I was able to successfully reproduce the example. However, when I modified my the example to use my data and with my formula, I get a set of errors having to do with the log() function. I get 10 of them (all exactly the same) and there are 10 levels in my factor variable.

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 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 <-

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 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,

Dear all i want to fit the self start model in nls. i have two question. i have a function, (asfr ~ I(((a*b)/c))+ ((c/age)^3/2)+ exp((-b^2)*(c/age)+(age/c)-2) i am wondering how to build the selfstart model. there is lost of example, (i.e. SSgompertz, SSmicman, SSweibull, etc). my question is, how to derive the function of parameters. and also which model to use for get the initials values. In the

Hi, I am not sure this is a bug but I can repeat it, The functions and data are below. I know this is nasty data, and it is very questionable whether a 4pl model is appropriate, but it is data fed to an automated tool and I would have hoped for an error. Does this repeat for anyone else? My details: > version _ platform i686-pc-linux-gnu

Hi All. I'd like to send some control arguments to the nls function when performing a nlsList analysis. I'm fitting a power model to some grouped data and would like to impose lower bounds on the estimates using the "port" algorithm. Obtaining the lower bound constraint works fine with a direct call to nls for a single level of the grouping variable. ?However, the bounds

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 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

Hello, nls library provides 6 self-starting models, among them: SSfp, a four parameters logistic function. Its self-starting procedure involves several steps. One of these steps is: pars <- as.vector(coef(nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xydata, start = list(lscal = 0), algorithm = "plinear"))) which assumes an initial value of lscal equal to 0. If lscal

Hello. I am trying to determine whether I should be using ML or REML methods to estimate a linear mixed model. In the book by Pinheiro & Bates (Mixed-effects models in S and S-PLUS, page 76) they state that one difference between REML and ML is that « LME models with different fixed-effects structures fit using REML cannot be compared on the basis of their restricted likelihoods. In

I am trying to understand the proper way to encode the nesting structure for data in the context of nlme, in the specific case of individuals nested within species for which each individual is unique. I have searched through Pinheiro & Bates and also past postings, but without success. Take the Wafer data set which has 2 levels: Wafer (8 values) and Site nested within Wafer (10 values for

Hello. I am trying to fit a nonlinear mixed model involving 3 parameters. I have successfully made a self-starting function. getInitial() correctly outputs the initial estimates. I can also use the nlsList with this function to get the separate nonlinear fits by group. However, I get an error message when using the nlme function. Here is the relevent code:

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