similar to: nls newbie: help approximating Weibull distribution

SSweibull() :  problems with step factor and singular gradient Hello I am working with growth data of ~4000 tree seedlings and trying to fit non-linear Weibull growth curves through the data of each plant. Since they differ a lot in their shape, initial parameters cannot be set for all plants. That’s why I use the self-starting function SSweibull(). However, I often got two error messages:

Dear all, this looks pretty much a standard problem, but I couldn't find a satisfying and understandable solution. (A) Given a data frame (or matrix), e.g. x <- data.frame(A=c(1, 2, 2), B=c(4, 5, 5)) and a row of this data frame, e.g. r <- c(2, 5) I need to find one row index i (or all such indices) such that r is at the i-th row in x, that is, the

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,

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

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 there, as part of a 2 x 2 contingency table analysis I would like to estimate conditional probabilities (success rates) in a Bernoulli experiment. In particular I want to test a null hypothesis p <= p0 versus the alternative hypothesis p > p0. As far as I understand the subject, there are UMPU tests for these types of hypotheses. Now I know about R's "binom.test" but the

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

Hello, please excuse me if this is not the appropriate mailing list, but I'll give it a try! I recently switched from using Emacs 20.7.1 to XEmacs 21.4, both on cygwin / MS W2K. On Emacs the ESS 5.1.17 was working well, but now, when calling R from within XEmacs, it first asks for the working directory, then says "R process is not running". The ESS buffer holds the

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,

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: I am new on R, this is the first time I work with it, I am trying to run some R example to get learn of them, but, I have a problem when running the example below: http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/zCO2.html CO2.Qn1 <- CO2[CO2$Plant == "Qn1", ] SSasympOff(CO2.Qn1$conc, 32, -4, 43) # response only Asym <- 32; lrc <- -4; c0 <- 43

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Full_Name: Madeleine Yeh Version: 1.9.1 OS: AIX 5.2 Submission from: (NULL) (151.121.225.1) After compiling R-1.9.1 on AIX 5.2 using the IBM cc compiler, I ran the checks. One of them failed. Here is the output from running the check solo. root@svweb:/fsapps/test/build/R/1.9.1/R-1.9.1/tests/Examples: ># ../../bin/R --vanilla < stats-Ex.R R : Copyright 2004, The R

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

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),

GUYS, I NEED HELP WITH ERROR: library(MASS) > dados<-read.table("mediaRGinverno.txt",header=FALSE) > vento50<-fitdistr(dados[[1]],densfun="weibull") Erro em fitdistr(dados[[1]], densfun = "weibull") : Weibull values must be > 0 WHY RETURN THIS ERROR? WHAT CAN I DO? BEST REGARDS [[alternative HTML version deleted]]

I need your help to make R works on my linux box, runing fedora 11. Few things on the machine and steps I did: 1. uname -a shows: Linux bagvapp 2.6.29.4-167.fc11.i586 #1 SMP Wed May 27 17:14:37 EDT 2009 i686 athlon i386 GNU/Linux 2. it has a gcc (version 4.4) RedHat's build 3. the g77 comes with the box does not work (R's configure tells it could not compile simple fortran code); so

Hello What is the appropriate method to test if a given distribution is a weibull thank you meriema email meriema.belaidouni at int-evry.fr

Hi I have been asked to provide Weibull parameters from a paper using Kaplan Meir survival analysis. This is something I am not familiar with. The survival analysis in R works nicely and is the same as commercial software (only the graphs are superior in R). The Weibull does not and produces an error (see below). Any ideas why this error should occur? My approach may be spurious.