similar to: Re: initial values for non linear models? {was "Info_Request"}

Hello there! I got some data with x and y values. there are some y == 0. This is a problem for the selfstarting regression model SSlogis. The regression works if I use a non selfstarting model. The formula is the same. But this needs very detailed information of the starting list. I dont have this Information all the time. An easy solution for selfstart model could be to add 1E-100 to the y==0

Hello, I am trying to fit a nlme (non linear mixed effect). I am using the SelfStart function SSlogis. However the data in my hand contains few observations per subject (4 or less), so the nlsList doesn't work... In this case I should fixe initial parameter estimates. I remark that values of initial estimates have a greater effect on the model fit (i.e. loglikelihood, AIC and also on

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

Hello, I am doing several self-starting growth functions for using with nls(). When I list the self-starting functions included in nls library, for instance, SSlogis, there is: > SSlogis function (input, Asym, xmid, scal) ... <environment: 03476D20> attr(,"class") [1] "selfStart" What is this <environment: 03476D20> instruction? By using deriv() and then

I'm wondering whether AIC scores extracted from nls() objects using AIC() are based on the correct number of estimated parameters. Using the example under nls() documentation: > data( DNase ) > DNase1 <- DNase[ DNase$Run == 1, ] > ## using a selfStart model > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: >

Hi everyone, Does the selfstart function SSlogis of the "nlme" library allows the introduction of time varying covariates ? For example how can I interpret the xmid parameter (reperesenting the age at which we reach the half of the asymptote) if I want to explain it by a some time varying covariate? Thanks in adavance, Abderrahim Abderrahim Oulhaj, Phd in Statistics Oxford

Thanks Adaikalavan, however the problem remains. Considering AIC() as applied to the linear model in AIC() help documentation: > data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score: > -2*logLik(lm1) + 2*(length(coef(lm1))+1)

Dear statistics experts, I'm looking for a way to compare the fit of the following three models: LinModel <- lm(y ~ x) LogModel <- nls(y ~ SSlogis(x, Asym, xmid, scal)) PotModel <- nls(y ~ a * x^n, start=list(a=1, n=1)) I am only interested in whether one of these models has substantial advances in explaining the variance of y. So my original idea was simply to compare the adjusted

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 <-

Hello, I'm trying to do some non-linear regression with 2 cell types and 4 tissue type treatments using selfStart models Following Ritz and Streibig (2009), I wrote the following routines: ##Selfstart expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){ exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL } expDecayAndConstantInflowModelInit <- function(mCall, LHS,

I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

Hi, I'm attempting to calculate the 0.25 and 0.97 quantiles for tree height (0-50 meters) against tree age (0-300 years) and I am running into some difficulty with the plotted grafic. I've run the examples in the quantreg help and can get those to work properly and by plugging in my data I can also get the lines plotted on my dataset. Unfortunately I'm running into a problem with 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!! Can anyone help me, i have problems to converge the following data with 5 nonlinears models that i evaluated. Firtly, i send my data (totalsinatipicos) that i just try to fit with the nonlinear models. Next, i have the following script where i called the data as totalsinatipicos. I made selfstarting each nonlinear model. ###Library library(NRAIA) ###Data d<-totalsinatipicos

Oups, I sent the email by error, as I was still writing my reply… Spencer, Le 22-avr.-09 à 03:33, spencerg a écrit : > Is your first model a special case of the second with eta1 = 0? > If yes, what about using 2*log(likelihood ratio) being approximately > chi-square? Yes, the first model is a special case of the second with eta1=0… Could you give me more explanation about

Dear I have a data.frame, and want to fit a constrained non-linear model: data: x y -0.08 20.815 -0.065 19.8128 -0.05 19.1824 -0.03 18.7346 -0.015 18.3129 0.015 18.0269 0.03 18.4715 0.05 18.9517 0.065 19.4184 0.08 20.146 0 18.2947 model: y~exp(a)*(x-m)^4+exp(b)*(x-m)^2+const I try to use nls() and set start=list(a=1,b=1,c=1,m=1), but which always give me a error message that

Dear all, I am trying to define a selfStart function for a non-linear model, which is a log-transformed SSmicmen model with multiplicative errors and so it is required to make them additive: log(y)=log(a)+log(x)-log(1+x/b) Any ideas about how to use the "peeling" method to derive the "initial" argument and get the initial values? Thank you for being always there!:)

hello, I have got some trouble with R functions nlm(), nls() or optim() : I would like to fit 3 parameters which must stay in a precise interval. For exemple with nlm() : fn<-function(p) sum((dN-estdata(p[1],p[2],p[3]))^2) out<-nlm(fn, p=c(4, 17, 5), hessian=TRUE,print.level=2) with estdata() a function which returns value to fit with dN (observed data vactor) My problem is that only

Dear Members of the Help List, Honestly, I feel a little bit stupid - I would like to do something rather simple: fit a non linear model to existing data, to be more precise I wanted to start with simple higher order polynomials. Unfortunately, I do not quite understand the examples in the helpfiles for the nlm, nls and nlsModel commands. Could anyone please provide a simple example to get me

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out