similar to: Error in 1:p : NA/NaN argument when running model comparisons

Subject: nls, step factor 0.000488281 reduced below 'minFactor' of 0.000976563 Hi there, I'm trying to conduct nls regression using roughly the below code: nls1 <- nls(y ~ a*(1-exp(-b*x^c)), start=list(a=a1,b=b1,c=c1)) I checked my start values by plotting the relationship etc. but I kept getting an error message saying maximum iterations exceeded. I have tried changing these

Dear R-users, I am trying to create a model using the NLS function, such that: Y = f(X) + q + e Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a covariate (continous variable) and e is an error term. I know that you can create multiple nonlinear regressions where x is polynomial for example, but is it possible to do this kind of thing when x is a function with unknown

  Hi, I fitted tree growth data with Chapman-Richards growth function using nls. summary(fit.nls) Formula: Parameters: Estimate Std. Error t value Pr Signif. codes: 0 ''***'' 0.001 ''**'' 0.01 ''*'' 0.05 ''.'' 0.1 '' '' 1 Residual standard error: 1.879 on 713 degrees of freedom Algorithm

Hi there, I am looking to compare nonlinear mixed effects models that have different nonlinear functions (different types of growth curve)embedded. Most of the literature I can find focuses on comparing nested models with likelihood ratios and AIC. Is there a way to compare model fits when models are not nested, i.e. when the nonlinear functions are not the same? Many thanks in advance! Lindsay

Good morning gentlemen! How using a weighted model in nls2? Values with the nls are logical since values with nls2 are not. I believe that this discrepancy is due to I did not include the weights argument in nls2. Here's an example: MOISTURE <- c(28.41640, 28.47340, 29.05821, 28.52201, 30.92055, 31.07901, 31.35840, 31.69617, 32.07168, 31.87296, 31.35525, 32.66118, 33.23385,

Dear All, I was trying to call a self-defined function that performs nonlinear regression and gets the corresponding prediction upper limit using nls2 package. However, weird thing happened. When I called the function in the main program, an error message "fitted(nlsmodel): object 'nlsmodel' not found" came up. But when I directly ran the codes inside the function, no error came

Good morning gentlemen! When I use the function nls2, and store it in an object, that object is automatically printed, without the summary or to draw the object. For example. model <- nls2 (...) Number of iterations to convergence: ... Achieved convergence tolerance: ... Nonlinear regression model model: ... ~ ... Date: NULL The B k ... ... ... residual sum-of-squares: ... Number

first, apologies for so many posts yesterday and today. I am wrestling with nls() and nls2(). I have tried to whittle it down to a simple example that still has my problem, yet can be cut-and-pasted into R. here it is: library(nls2) options(digits=12); y= c(0.4334,0.3200,0.5848,0.6214,0.3890,0.5233,0.4753,0.2104,0.3240,0.2827,0.3847,0.5571,0.5432,0.1326,0.3481) x=

Dear R users, Does anybody have a function to calculate logLik or AIC for nlsList objects? After receiving error messages, another user helped me ascertain that this function is not currently written into R. Many thanks Lindsay

Hi there, I have been trying to fit an NLME to my data. My dataset has two category levels - one is a fixed effect (level1) and one is a random effect (level2), though so far I have only experimented with the highest level grouping (fixed, level1), with the following code: mod1 <- nlme(H ~ a*(1-exp(-b*D^c)), data=sizes, fixed=a+b+c~factor(Loc), start=c(a=75,b=0.05,c=0.7)) This returns the

Potential bug: I mistyped weights in the call ('weigths') and it did not produce any error= message. The coefs were exactly the same like without weights, so I was su= spicious and when weights(nls1) gave NULL, I saw my typo. Usually the function will say "Unused arguments", which shows you the error= , but not nls. Regards Stephen [[alternative HTML version deleted]]

Dear Madam or sir, Does anyone know if there is a pre-compiled version of package nls2 for windows, please? Thank you. Souleymane

I wanted to use the nls() module to solve a Problem from Sen & Srivastava (1990, p.209). Here is the (basic) code used to perform the estimation: library(SenSrivastava) library(nls) data(E9.8) # Use Linear Least Square for estimating start values lm.obj <- lm(R.1 ~ I.1 + S.1, data = E9.8) nls1.obj <- nls(R.1 ~ b.0 + b.1*(I.1^a.1-1)/a.1 + b.2*(S.1^a.2-1)/a.2,

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

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

Here is the code I am running: library(nls2) modeltest<- function(A,mu,l,b,thour){ out<-vector(length=length(thour)) for (i in 1:length(thour)) { out[i]<-b+A/(1+exp(4*mu/A*(l-thour[i])+2)) } return(out) } A=1.3 mu=.22 l = 15 b = .07 thour = 1:25 Yvals<-modeltest(A,mu,l,b,thour)-.125+runif(25)/4 st2 <- expand.grid(A = seq(0.1, 1.6,.5), mu = seq(0.01, .41,.1), l=1, b =seq(0,.6,.3))

2007/11/22, Duncan Sands <baldrick at free.fr>: > > Hi, > > > Current llvm-gcc cannot emit llvm intrinsic function like llvm.pow.* and > > llvm.sin.* > > For example: > > > > double foo(double x, double y) { > > return pow(x,y); > > } > > > > will compiled into ll: > > > > define double @foo(double %x, double %y) {

> On Jan 12, 2017, at 12:58 PM, Friedman, Eli via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > On 1/12/2017 9:33 AM, Mehdi Amini via llvm-dev wrote: >>> On Jan 12, 2017, at 5:03 AM, Antoine Pitrou via llvm-dev <llvm-dev at lists.llvm.org> wrote: >>> >>> On Mon, 9 Jan 2017 11:43:17 -0600 >>> Wei Ding via llvm-dev <llvm-dev at

Hi Thanks for your suggestion, I'm trying to install this package in Ubuntu (7.10) but unsuccessfully. Also tried in MacOSX, and no success too. _____ De: Ndoye Souleymane [mailto:ndoye_p@hotmail.com] Enviado el: miércoles, 05 de diciembre de 2007 13:38 Para: bady@univ-lyon1.fr; Florencio González CC: r-help@stat.math.ethz.ch Asunto: RE: [R] confidence intervals for y predicted in

Hi, > Current llvm-gcc cannot emit llvm intrinsic function like llvm.pow.* and > llvm.sin.* > For example: > > double foo(double x, double y) { > return pow(x,y); > } > > will compiled into ll: > > define double @foo(double %x, double %y) { > %tmp3 = tail call double @pow( double %x, double %y ) > ret double %tmp3 > } > > This is not