similar to: The 'data' argument and scoping in nls

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'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 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,

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

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()

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

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

I'm using nls to fit a variety of different models. Here I use SSgompertz as an example. I want the ability to fix one (or more) of the coefficients that would normally be optimised (e.g. fix b3=0.8). Examples; based on and using data from example(SSgompertz) #--------------------- # vanilla call to nls, no coefficients fixed, works fine nls(density ~ SSgompertz(log(conc), Asym, b2, b3),

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

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 <-

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) #

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 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

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

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

I'm trying to fit a Gompertz sigmoid as follows: x <- c(15, 16, 17, 18, 19) # arbitrary example data here; y <- c(0.1, 1.8, 2.2, 2.6, 2.9) # actual data is similar gm <- nls(y ~ a+b*exp(-exp(-c*(x-d))), start=c(a=?, b=?, c=?, d=?)) I have been unable to properly set the starting value '?'s. All of my guesses yield either a "singular gradient" error if they

I am trying to fit a curve to a cumulative mortality curve (logistic) where y is the cumulative proportion of mortalities, and t is the time in hours (see below). Asym. at 0 and 1 > y [1] 0.00000000 0.04853859 0.08303777 0.15201970 0.40995074 0.46444992 0.62862069 0.95885057 1.00000000 [10] 1.00000000 1.00000000 > t [1] 0 13 20 24 37 42 48 61 72 86 90 I tried to find starting values for

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, I've made a selfStart function for use with gnls and the following piece of code works nicely: check1 <- gnls(y ~ spot.shape.fct(xcord, ycord, background, spotintensity, rho, sigma, delta, mux, muy), start=getInitial(y ~ spot.shape.fct(xcord, ycord, background, spotintensity, rho,