similar to: nls.control: increasing number of iterations

Hello, I''m trying to fit a linear mixed effects model of the form: lme(y ~ x * Sex * Year, random=x|subject) where Sex and Year are factors with two and three levels respectively. I want to compare the fixed effects for each level to the overall mean, but the default in R is to compare to the first level. This can be changed by adding the term -1 to the righthand side of the model

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

Hi, Sorry if this is an obvious one, but is there a simple way to modify the lm function to test whether a slope coefficient is significantly different from 1 instead of different from 0? Thanks, Martin Martin Biuw SEA MAMMAL RESEARCH UNIT Gatty Marine Laboratory School of Environmental and Evolutionary Biology University of St Andrews Fife, Scotland KY16 8LB UK phone +44 (0)1334 462630 fax

Howdee, My question appears at #6 below: 1. I want to model the growth of each of a large number of individuals using a 4-parameter logistic growth curve. 2. nlme does not converge with the random structure that I want to use. 3. nlsList does not converge for some individuals. 4. I decided to go around nlsList using: t(sapply(split(data, list(data$id)), function(subd){coef(nls(mass ~

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

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, Once again, I posted a message without a subject line. Sorry.... here is the question again. Is there a simple way to modify the circ.mean function in the CircStats package to include a vector of weights to obtain a weighted average angle? Thanks! Martin -- Martin Biuw Sea Mammal Research Unit Gatty Marine Laboratory, University of St Andrews St Andrews, Fife KY16 8PA Scotland Ph:

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, Is there a function in R to calculate the nth root, similar to the MATLAB function NTHROOT()? Thanks, Martin Biuw [[alternative HTML version deleted]]

Dear R-helpers, I'm using R 2.5.0 under Windows and am trying to use nlsList from nlme 3.1-80 with the selfstart function for the four parametric logistic function. My first test went well, but now I'm trying to do some more sophisticated things and it does not work anymore. I simulate my data from a five parametric logistic function like this:

Mmmh, seems as if I really should change my options as I seem to keep sending off empty bug-reports ;-/ Sorry guys. Here is the content that should have been in the last e-mail: `R CMD check nlme' fails on my machine. The final output in nlme-Ex.Rout is: > library(nlme) > data(Soybean) > fm1 <- nlme(weight ~ SSlogis(Time, Asym, xmid, scal), data = Soybean, + fixed =

G'Day, I'm a little confused why the c function has the followng effect on classes - is this a feature ? My workround [ class(cc) <- c("POSIXt", "POSIXct") ] seems to do the job. Many thanks Bernie McConnell "R version 1.3.1, 2001-08-31" on NT > aa <- as.POSIXct("2001-09-23") > bb <- as.POSIXct("2001-09-24") > cc

Dear R users I'm a new user of R and I have a basic question about the 4-parameter logistic model. According to the information from Pinheiro & Bates the model is: y(x)=theta1+(theta2-theta1)/(1+exp((theta3-x)/theta4)) == y(x)=A+(B-A)/(1+exp((xmid-input)/scal)) from the graph in page 518 of the book of the same authors (mixed models in S) theta 1 corresponds to the horizontal asymptote

Dear all Please help me with correct syntax of predict.nlme. I would like to predict from nlme object for new data. I used predict(fit.nlme6, data=newdata) but I have always got fitted values, no matter how I changed newdata. I have > summary(fit.nlme6) Nonlinear mixed-effects model fit by maximum likelihood Model: konverze ~ SSfpl(tepl, A, B, xmid, scal) Data: limity.gr AIC

Hi R help, I am trying to determine how nls() generates a function based on the self-starting SSlogis and what the formula for the function would be. I've scoured the help site, and other literature to try and figure this out but I still am unsure if I am correct in what I am coming up with. ************************************************************************** dat <-

Hi, I am trying to use F test or Chi-square test to test if 2 5-parameter (A, B, xmid, scal and H) logistic curves are parallel based on residual sum of squares. What's usually done is to first fit the 2 curves using a constraint (or global) model where all parameters are kept the same except for "xmid"; then fit 2 independent curves using unconstraint models where all 5 parameters

G'Day again, I am attempting to read a large MSAccess file into R, but get memory problems. With the first 100 rows of the table ("Macca99") things, as shown below, are fine and the resulting object is 33,780 bytes. But when I read the entire table ("MaccaDiv99") which is 218,000 rows R falls over with the message: Rgui.exe - Application error The instruction at

Dear all I am struggling to fit data which form something like CDF by lognorm. Here are my data: proc <- c(0.9, 0.84, 0.5, 0.16, 0.1) size <- c(0.144, 0.172, 0.272, 0.481, 0.583) plot(size, proc, xlim=c(0,1), ylim=c(0,1)) fit<-nls(proc~SSfpl(size, 1, 0, xmid, scal), start=list(xmid=0.2, scal=.1)) lines(seq(0,1,.01), predict(fit, newdata=data.frame(sito=seq(0,1,.01))), col=2) I tried

Dear R-help subscribers Would you tell me how to apply SSfpl with binary data as below? Unfortunately, there is not the EXAMPLE in help(SSfpl) for binary data but for quantitative data(Chick). V1: dose V2: log-transformed dose V3: response (rate) V1 V2 V3 1 0.775 -0.2548922 0.1666667 2 5.000 1.6094379 0.8148148 3 10.000 2.3025851 0.5000000 4 20.000 2.9957323

How about proc <- c(0.9, 0.84, 0.5, 0.16, 0.1) size <- c(0.144, 0.172, 0.272, 0.481, 0.583) plot(size, proc, xlim=c(0,1), ylim=c(0,1)) fit<-nls(proc~plnorm(size, log(xmid), sdlog, lower=FALSE), start=list(xmid=0.2, sdlog=.1)) summary(fit) lines(fitted(fit)~size) -pd > On 10 Jul 2017, at 16:27 , PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Dear all > > I am