similar to: step halving factor reduced below minimum

Hello,   I am attempting to use nlme to model the response of 19 groups.  I am able to get a reasonable fit of all of the groups to the observed data using an exponential decay model (a*exp-x*b).  The problem is that when I plot the fitted values to residuals, it demonstrates a pattern of increasing variance.   I attempt to model this variance using   Sig3.nlme <- update(Sig2.nlme, weights =

Hi R-users, Could you give me some advice in solving the problem of such error message from gnls and nls? ## begin error message "Problem in gnls(y1 ~ glogit4(b, c, m, t, x), data.frame(x..: Step halving factor reduced below minimum in NLS step " ##and "Problem in nls(y ~ 1/(1 + exp((xmid - x)/scal)), data = x..: step factor reduced below minimum "? Thank you in

In some trial simulation work I need to create batch files that will repeatedly generate pseudoreplicate datasets and then create non- linear mixed effects models using nlme. Inevitably these models sometimes fail to converge but I need the batch file to simply move on to another simulation rather than abort. I am using the try() function as in model<-try((nlme(...))) which handles

Hi all, I'm working with a dataset from 10 treatments, each treatment with 30 subjects, each subject measured 5 times. The plot of the dataset suggests that a 3-parameter logistic could be a reasonable function to describe the data. When I try to fit the model using gnls I got the message 'Step halving factor reduced below minimum in NLS step'. I´m using as the initial values of the

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Hi R-help; In using nlme, I often encounter the following error message: Error in nlme.formula(model = response ~ ......, data = ... : Step halving factor reduced below minimum in PNLS step Resetting the pnlsTol default value does not solve the problem. Any suggestions or workarouds for solving the problem? TIA, Richard

Hello all. I'm doing a simulation study where I will be making use of the 'nlme' package. I want to loosen up the convergence criteria so that I increase the likelihood of convergence (potentially at the cost of obtaining slightly less than ideal results). The parameters in the function nlmeControl() control the convergence criteria. These default values can be modified to make

Hi I have found some postings referring to the fact that one can try and understand why a particular model is failing to solve/converge from the verbose output one can generate when fitting a nonlinear mixed model. I am trying to understand this output and have not been able to find out much: **Iteration 1 LME step: Loglik: -237.4517 , nlm iterations: 22 reStruct parameters: subjectno1

I'm maximising a reasonably complex function using nlme (version 3.1-65, have also tried 3.1-66) and am having trouble with fixed parameter estimates slightly away from the maximum of the log likelihood. I have profiled the log likelihood and it is a parabola but with sum dips. Interestingly changing the parameterisation moves the dips around slightly. Unfortunately the PNLS step is

I  am trying to learn nls using a simple simulation. I assumed that the binomial prob varies linearly as 0.2 + 0.3*x in  x {0,1}, and the objective is to recover the known parameters a=0.2, b=0.3 ..data frame d has 1000 rows... d$x<-runif(0,1)               d$y<-rbinom(1000,1,0.2+0.3*d$x)  table(d$y,cut(d$x,breaks=5));   (-0.000585,0.199] (0.199,0.399] (0.399,0.599] (0.599,0.799]

Hello, I've seen various threads on people reporting: step factor 0.000488281 reduced below `minFactor' of 0.000976563 While I know how to set the minFactor, what I'd like to have happen is for nls to return to me, the last or closest fitted parameters before it errors out. In other words, so I don't get convergence, I'd still like to acquire the values of the parameters

Hello, I am trying to estimate 4 parameters of a non-linear model using nls. My model function is a Fourier integral and is very expensive to calculate. I get the following error: > theta0 <- c(0.045, 1.02*10^(-4), 0.00169, 5.67*10^(-4)) > res <- nls(log(y) ~ log(model(theta,r,t)), data=dataModel, + start=list(theta=theta0), trace=TRUE, + control=nls.control(tol=1e-2))

I am trying to fit the 4-parameter logistic model with SSfpl. The estimation is terminated with a message "step factor 0.000488281 reduced below 'minFactor' of 0.000976562". I tried to change the minFactor with nls.control(minFactor=yy) with no apparent success. A call of nls.control() without arguments always shows the value of 0.000976562. Also the estimation terminates as

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

test/BugPoint/metadata.ll Takes about 6 seconds to run if llvm-symbolizer isn't present 13 seconds with debug info 1m30s with split DWARF/Fission So, couple of things: 1) llvm-symbolizer is /really slow/ on split-dwarf files... 2) Why are stack traces even attempted when using bugpoint? Even if it's just the 6->13s slowdown, that's still halving the speed of bugpoint in the

>>>>> "VW" == Viechtbauer Wolfgang (STAT) <Wolfgang.Viechtbauer at STAT.unimaas.nl> >>>>> on Fri, 26 Sep 2008 18:00:19 +0200 writes: VW> Hi all, I'm trying to fit a marginal (longitudinal) VW> model with an exponential serial correlation function to VW> the Orange tree data set. However, R crashes frequently VW>

Dear R users; I was trying to fit a nonlinear model using gnls (nlme version 3.1-80, R 2.5.0, WinXP) and I got the following error and warning message: Error in gnls(ht ~ a1 * hd * (1 - a2 * exp(-a3 * (dbh/dq2))), data = hdat, : Step halving factor reduced below minimum in NLS step In addition: Warning message: $ operator is deprecated for atomic vectors, returning NULL in:

What sort of performance is expected over GigE, with Samba 3 as the server and XP64 as the client? I havn't been able to find any current benchmarks at all. http://lists.samba.org/archive/samba/2010-June/156708.html talks about the same configuration, and suggests that the protocol should be able to break 100MB/sec. (Unfortunately, the poster disappeared without following up.) Focusing on

Hi All, Is it possible to send output in mono using Icecast? Also, would that mean I would only be sending one channel so effectively halving the bandwidth? Regards, Andy -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.xiph.org/pipermail/icecast/attachments/20090204/81d06606/attachment.htm

This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs