similar to: SSweibull() : problems with step factor and singular gradient

Hi folks, I tried to retain the Weibull distribution using the `nls' function and proceeding along the lines of the example provided in the `SSweibull' help (at least I thought so): t <- (1:200)/100 v <- pweibull(t, shape=3, scale=1) df <- data.frame(Time=t, Value=v) Asym <- 1.0; Drop <- 1.0; lrc <- 0; pwr <- 1 df.estimate <- nls(Value ~ SSweibull(Time,

Hello I?m need fitt growth curve with data length-age. I want to evaluate which is the function that best predicts my data, to do so I compare the Akaikes of different models. I'm now need to evaluate if changing the initial values changes the parameters and which do not allow to estimate the model. To do this I use the function nls(); and I randomize the initial values (real positive number).

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

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

This data are kilojoules of energy that are consumed in starving fish over a time period (Days). The KJ reach a lower asymptote and level off and I would like to use a non-linear plot to show this leveling off. The data are noisy and the sample sizes not the largest. I have tried selfstarting weibull curves and tried the following, both end with errors. Days<-c(12, 12, 12, 12, 22, 22, 22,

Dear list members, I am working with a multilevel growth curve, that in its simplest form goes like follows: Yit = Ai + Bi t + eit (the error term is assumed to follow an AR(1) autorregressive process) One major topic in my research is the convergence in the values of Y over time. Thus, I am interested in the relationship between the random effects for the intercept and the slope, and I

I ran into a problem trying to make a plot from a file that's read using source. Basically, I have the following code in a file "plot.R" : library(nlme) data(Loblolly) fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), data = Loblolly, fixed = Asym + R0 + lrc ~ 1, random = Asym ~ 1, start = c(Asym = 103, R0 = -8.5, lrc = -3.3))

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

Hello, How do I specify the formula and random effects without a startup object ? I thought it would be a mixture of nls and lme. after trying very hard, I ask for help on using nlme. Can someone hint me to some examples? I constructed a try using the example from nls: #variables are density, conc and Run #all works fine with nls DNase1 <- subset(DNase, Run == 1 ) fm2DNase1 <- nls(

Hola a todos Les consulto por un problema con xtable y nlme, tomando un ejemplo del manual de nlme para obtener los resultados en latex utilizando xtable, se puede utilizar el siguiente código, pero hay un problema y causa error. library(nlme) library(xtable) fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), data = Loblolly, fixed = Asym + R0 + lrc ~ 1,

Dear all I encountered strange behaviour of augPred with virtually the same data First I made groupedData object. > mar.g<-groupedData(rutilizace~doba|int, data=mar) When I perform nlme on complete dataset I get an error with augPred > fit<-nlsList(rutilizace~SSasymp(doba, Asym, R0, lrc), data=mar.g) Warning message: c("1 error caught in nls(y ~ cbind(1 - exp(-exp(lrc) * x),

Dear all I try to refine my nlme models and with partial success. The model is refined and fitted (using Pinheiro/Bates book as a tutorial) but when I try to plot plot(augPred(fit4)) I obtain Error in predict.nlme(object, value[1:(nrow(value)/nL), , drop = FALSE], : Levels (0,3.5],(3.5,5],(5,7],(7,Inf] not allowed for vykon.fac > Is it due to the fact that I have unbalanced

Hi Well, yes I tried it about two weeks ago but my post did not get through as it still awaits moderator approval. I could check which column is offending but actually it is only minor nuisance, I can live with selection of columns before fitting a model. What seems to me strange is that both full dataset and only selected colums gave me identical fit results but only one works within augPred.

Dear Bert, Thank you so much for your kind and valuable feedback. I tried finding the starting values using the approach you mentioned, then did the following to fit the nonlinear regression model: nlregmod2 <- nls(y ~ theta1 - theta2*exp(-theta3*x), start = list(theta1 = 0.37, theta2 = exp(-1.8), theta3 =

Hi there are some predefined self-start functions, like SSmicmen, SSbiexp, SSasymp, SSasympOff, SSasympOrig, SSgompertz, SSflp, SSlogis, SSweibull, Quadratic, Qubic, SSexp (nlrwr) Btw, do you know graphic examples for this functions? The SSexpDecay (exponential decay) for y = (y0 - plateau)*exp(-k*x) + plateau from

Better posted on r-sig-mixed-models , no? Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 23, 2017 at 5:17 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > Dear all > > I encountered strange

Am Dienstag, 25. April 2017, 08:50:34 schrieb Dirk Eddelbuettel: > On 25 April 2017 at 14:58, G?ran Brostr?m wrote: > | hello, > | > | I just installed R-3.4.0 from scratch: > | > | $ sudo apt install r-base > | > | but when I try > | > | > library(survival, lib.loc = "/usr/lib/R/library") > | > fit <- coxph(Surv(exit, event) ~ x, data =

Hi, I'm trying to teach myself R, and by the way, re-learning statistics using Crawley's "Statistics: an introduction using R". I've reached the regression chapter, and when it deals with non-linear regresion using the nls library I face the following problem: I follow the steps--- >deer<-read.table("c:\\temp\\jaws.txt",header=T) ---data available at

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

> On Aug 23, 2017, at 8:08 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > Well, yes I tried it about two weeks ago but my post did not get through as it still awaits moderator approval. It got through just fine. It appeared on Aug 15. It just didn't get any replies. As I read your original question in this thread, it was not clear to me that you had