similar to: bootstrapped CI for nonlinear models using nlsBoot from nlstools

Hi all I?ve got a problem using boxcox.nls function in nlrwr packagge. I?m fitting several non linear models to these data: > x [1] 2 1 1 5 4 6 13 11 13 101 101 101 > y [1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853 [6] 6.553951324 14.243274230 14.519899320 15.066473610 21.728809880 [11] 18.553054450 23.722637370 I used nls function with self

Hi all I have fitted a model usinf nls function to these data: > x [1] 1 0 0 4 3 5 12 10 12 100 100 100 > y [1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853 [6] 6.553951324 14.243274230 14.519899320 15.066473610 21.728809880 [11] 18.553054450 23.722637370 The model fitted is: modellogis<-nls(y~SSlogis(x,a,b,c)) It runs OK. Then I calculate

Thanks to the help of people from this forum I was able to bootstrap my data and then apply a model to it. Thanks for all your help. Everything worked out well, but I am having a difficult time getting the new parameter values. I bootstrapped the data 300 times and I want to get the 300 sets of parameter estimates and plot them in Excel. Here is my code:

I apologize if this comes across as confusing. I will try to explain my situation as best I can. I have R bootstrapping my growth data for fish. It's resampling my database of age and length data and then produces several new datasets for me. In this case, it's resampling my data to create three new datasets of age and length data. Here is my code with my original data called

Hi R-users, My question is more methodological one, rather than technical. I have to samples representing residuals based on two measurements techniques (resid1,resid2; n=69). I need to compare two samples, to reject one technique (the worse one), and to keep the one which gave lower residuals (better one). What to look for? What should I analyse? Means, variance, std. deviations? Based on

Hello, I am trying to do a fit a loglikelihood function with Multivariate distribution via copulas with fitMdvc. The problem is that it doesn't recognize that my beta is a vector of km parameter and when I try to run it it say that the length of my initial values is not the same as the parameter. Can somebody guide me where my mistake is. Thanks, Elisa. #################################

Hi, I am using the nls function and it stops because the number of iterations exceeded 50, but i used the nls.control argument to allow for 500 iterations. Do you have any idea why it's not working? fm1 <- nls(npe ~ SSgompertz(npo, Asym, b2, b3), data=f,control=nls.control(maxiter=500)) Error in nls(y ~ exp(-b2 * b3^x), data = xy, algorithm = "plinear", start = c(b2 =

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),

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

Hi everyone, I'm working on a script trying to use foreach %dopar% but without success, so I manage to run the code with foreach %do% and looks like this: The code is part of a MCMC model for projects valuation, returning the most important results (VPN, TIR, EVA, etc.) of the simulation. foreach (simx = NsimT, .combine=cbind, .inorder=FALSE, .verbose=TRUE) %do% { MCPVMPA = MCVAMPA[simx]

Hi I?m trying to fit a nonlinear model to a derivative of the logistic function y = a/(1+exp((b-x)/c)) (this is the parametrization for the SSlogis function with nls) The derivative calculated with D function is: > logis<- expression(a/(1+exp((b-x)/c))) > D(logis, "x") a * (exp((b - x)/c) * (1/c))/(1 + exp((b - x)/c))^2 So I enter this expression in the nls function:

Hi all I?m trying to tests the significance of loadings from a ordination of 46 variables (caategorical, ordinal and nominal). I used dudi.mix from ade4 for the ordination. A years ago Jari Oksanen wrote this script implementing Peres-Neto et al. 2003 (Ecology) bootstraping method: netoboot <- function (x, permutations=1000, ...) { pcnull <- princomp(x, cor = TRUE, ...) res

Dear R users and R friends, I have a little problem... I don't know anymore which package to use if I want to perform a power regression analysis. To be clear, I want to fit a regression model like this: fit <- ....(y ~ a * x ^ b + c) where a, b and c are coefficients of the model. The R Site does not have the answer I want... Thanks in advance and with kind regards, David

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 am using the nls function and it stops because the number of iterations exceeded 50, but i used the nls.control argument to allow for 500 iterations. Do you have any idea why it's not working? fm1 <- nls(npe ~ SSgompertz(npo, Asym, b2, b3), data=f,control=nls.control(maxiter=500)) Thanks for your help, Cheers, Karine.

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

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Hello list, I'm trying to plot nls profiles, but the plot.profile.nls function in R doesn't seem to accept any plot.default variables. Specifically, I'd like to be able to change the x-axis title and the colors to black and white. Has anyone had any luck with this? If not, is there a way to override to plotting colors, perhaps in par()? Thanks, Sam fm1 <- nls(demand ~

Hi, I would like to fit a linear model where some but not all explanators are chosen stepwise - ie I definitely want to include some terms, but others only if they are deemed significant (by AIC or whatever other approach is available). For example if I wanted to definitely include x1 and x2, but only include z1 and z2 if they are significant, something like this: df <-

Hi, I have a problem with fitting a nonlinear mixed-effects model to my data. I was able to fit quite simple exponential model but now I?m trying to fit the following Gompertz model: > gomp<-nlme(paino~b0+b1*exp(-exp(-b2*(daydeg.scale-b3))), data=group1, fixed=b0+b1+b2+b3~1, start=c(b0=150, b1=3000, b2=0.6, b3=2500)) I?m getting the following error: Error in chol((value + t(value))/2) :