search for: minfactor

Dear All, A couple of questions about the nls package. 1. I'm trying to run a nonlinear least squares regression but the routine gives me the following error message: step factor 0.000488281 reduced below `minFactor' of 0.000976563 even though I previously wrote the following command: nls.control(minFactor = 1/4096), which should set the minFactor to a lower level than the default one, 1/1024=0.000976563. Is there any way of setting the new minfactor to a lower level? 2. Is it possible to set some cons...

Dear Friends. I tried to use nls.control() to change the 'minFactor' in nls( ), but it does not seem to work. I used nls( ) function and encountered error message "step factor 0.000488281 reduced below 'minFactor' of 0.000976563". I then tried the following: 1) Put "nls.control(minFactor = 1/(4096*128))" inside the brackets of nls,...

Hello, I've seen various threads on people reporting: step factor 0.000488281 reduced below `minFactor' of 0.000976563 While I know how to set the minFactor, what I'd like to have happen is for nls to return to me, the last or closest fitted parameters before it errors out. In other words, so I don't get convergence, I'd still like to acquire the values of the parameters last tried...

...03127 [7] 4.3939336 -1.4380091 3.2650180 3.5760906 0.2947972 1.0569417 > X [1] 1 0 0 4 3 5 12 10 12 100 100 100 The problem is that I got the next error: Error en nls(Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 I trien to change the minFactor using the control argument inside nls control=nls.control(maxiter=50, tol=1e-5, minFactor = 1/2048 but got a new error message: Error en nls(Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2, : step factor 0.000244141 reduced below...

Hi I want to change a control parameter for an nls () as I am getting an error message "step factor 0.000488281 reduced below 'minFactor' of 0.000976562". Despite all tries, it seems that the control parameter of the nls, does not seem to get handed down to the function itself, or the error message is using a different one. Below system info and an example highlighting the problem. Thanks, Rainer > version...

...ntervals for the parameter estimates confint(VonB) Everything works as it should right up until the confint(VonB) statement. When I ask for the confidence intervals, R goes through the process then stops and gives "Error in prof$getProfile() : step factor 0.000488281 reduced below 'minFactor' of 0.000976562". However, when I use nls.control=(minFactor=0.000001) OR nls.control(minFactor=0.000001), I get the same error. I'm at a loss. I'm not sure what else I can do to have R reduce the step size if it's not nls.control(). Thanks, SR Steven H. Ranney Gradu...

...ssion model to time series data. If I do this: reg.logis = nls(myVar~SSlogis(myTime,Asym,xmid,scal)) I get this error message (translated to English from French): Erreur in nls(y ~ 1/(1 + exp((xmid - x)/scal)), data = xy, start = list(xmid = aux[1], : le pas 0.000488281 became inferior to 'minFactor' of 0.000976562 I then tried to set the 'minFactor' value like this: reg.logis = nls(myVar~SSlogis(myTime,Asym,xmid,scal),control=nls.control(minFactor=0.000488281)) But I have exactly the same error message, so it seems that the control was not changed If I set some values for the...

...16002e-03 6.320454e-04 12.13746 : 2.099813e-02 8.332565e-05 1.216093e-03 6.320456e-04 12.13746 : 2.099927e-02 8.332518e-05 1.216116e-03 6.320457e-04 Error in nls(log(y) ~ log(model(theta, r, t)), data = dataModel, start = list(theta = theta0), : step factor 0.000488281 reduced below `minFactor' of 0.000976563 > The parameters seem to converge well. I don't understand why the optimization does not stop. I tried different nls.controls, to increase the tol, to reduce the minFactor, but it makes no difference. I keep getting the same error. The function "model" also...

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of maxiter and tol in nls.control. I would be happy to hear from anyone who has a...

...non-linear function using nls, but it won't solve. > regresjon<-nls(lcfu~lN0+log10(1-(1-10^(k*t))^m), data=cfu_data, > start=(list(lN0 = 7.6, k = -0.08, m = 2))) Error in nls(lcfu ~ lN0 + log10(1 - (1 - 10^(k * t))^m), data = cfu_data, : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Tried to increase minFactor and number of iteration, but resulted in extremely high number of iterations > regresjon2<-nls(lcfu~lN0+log(1-(1-10^(k*t))^m, base=10), data=cfu_data, > start=(list(lN0 = 7.6, k = -0.08, m = 2)), > control=nls.control(maxiter=50000, minFa...

...r messages: 1) # Example days <- c(163,168,170,175,177,182,185,189,196,203,211,217,224) height <- c(153,161,171,173,176,173,185,192,195,187,195,203,201) dat <- as.data.frame(cbind(days,height)) fit <- nls(y ~ SSweibull(x, Asym, Drop, lrc, pwr), data = dat, trace=T, control=nls.control(minFactor=1/100000)) Error in nls(y ~cbind(1, -exp(-exp(lrc)* x^pwr)), data = xy, algorithm = “plinear”, :                           step factor 0.000488281 reduced below `minFactor` of 0.000976562 I tried to avoid this error by reducing the step factor below the standard minFactor of 1/1024 using the nl...

...           71            76           102  z <- nls(y ~ rbinom(1000,1,a+b*x),data=d,start= list(a =0.1,b=0.2),trace=T); 374 :  0.1 0.2  361 :  0.1 0.2  350 :  0.1 0.2  Error in nls(y ~ rbinom(1000, 1, a + b * x), data = d, start = list(a = 0.1,  :    step factor 0.000488281 reduced below 'minFactor' of 0.000976562 I have tried plinear, got the same.... Additionaly- why does the parameters not change with the iteration ? [[alternative HTML version deleted]]

...meters using nls. I have come across a common problem that R users have reported when I attempt to fit a particular 3-parameter nonlinear function to my dataset: Error in nls(r ~ tlm(a, N.fix, k, theta), data = tlm.data, start = list(a = a.st, : step factor 0.000488281 reduced below `minFactor' of 0.000976563 Despite modifying minFactor using nls.control, I am unable to counter the apparent singularity in the model fit. I have also tried changing the tolerance and start parameter values to no avail. If anyone can provide a relatively simple solution (perhaps adjusting the gradien...

Dear friends. I use nls() and encounter the following puzzling problem: I have a function f(a,b,c,x), I have a data vector of x and a vectory y of realized value of f. Case1 I tried to estimate c with (a=0.3, b=0.5) fixed: nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5 ^2048),start=list(c=0.5)). The error message is: "number of iterations exceeded maximum of 100000" Case2 I then think maybe the value of a and be are not reasonable. So, I let nls() estimate (a,b,c) altogether: nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5 ^2048...

I am trying to fit the 4-parameter logistic model with SSfpl. The estimation is terminated with a message "step factor 0.000488281 reduced below 'minFactor' of 0.000976562". I tried to change the minFactor with nls.control(minFactor=yy) with no apparent success. A call of nls.control() without arguments always shows the value of 0.000976562. Also the estimation terminates as previously although I tried to use control=nls.control(minFactor=yy...

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...

....2965745 0.9999650 1.0000000 0.003702546 : 0.2965898 0.9999650 1.0000000 0.003702546 : 0.2965898 0.9999650 1.0000000 0.003702546 : 0.2965898 0.9999650 1.0000000 Error in nls(Occupancy ~ 1 - (theta * beta^(2 * Resolution^(1/2)) * delta^Resolution), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Regards, *Evans Ochiaga* *African Institute for Mathematical Sciences* *6 Melrose Road* *Muizenberg, South Africa* *Msc in Mathematical Sciences+27 84 61 69 183 * *"When I cannot understand my Father?s leading, And it seems to be but hard and cruel fate, Still I h...

...d calling nls directly. We are using R 2.9.0 library(stats) wrappernls <- function(modelArgument,dataArgument,startArgument,weightsArgument) { F.mod <-try(nls(formula=modelArgument,data=dataArgument,start=startArgument, weights=weightsArgument, control=nls.control(maxiter = 500, tol = 1e-05, minFactor = 1/102400)),silent=TRUE) return(F.mod) } F<-c(0.4091867,0.4060938,0.4032078,0.4089090,0.4138126,0.4183426,0.4073004,0.4145457,0.4137699, 0.4161127,0.4228770,0.4231176,0.4295189,0.4290417,0.4348761,0.4517475,0.4899147,0.5463731, 0.6273890,0.7458752,0.8960531,1.0280455,1.1753147,1.3122100,1.4370...

...finity produced when evaluating the model when I use plinear algoritm, and run this gamma.asfr1 <- nls(gamma.asfr, data= asfr.aus, start = list(b = 28, a = 1, d= 0.5), trace = TRUE, algorithm="plinear") error: number of iterations exceeded maximum of 50 then i fix the iteration and minFactor even then its can't work gamma.asfr1 <- nls(gamma.asfr, data= asfr.aus, start = list(b = 28, a = 1, d= 0.5), trace = TRUE, algorithm="plinear", nls.control(maxiter=500, minFactor=0.000001)) error: Missing value or an infinity produced when evaluating the model Can any body tell...

...up on this track. Here are a few lines from the command line: > funct3 function(x, h, k, z, a) { (1 - h*(1 - k*(x - z)/a)^(1/k))^(1/h) } > reg24 <-nls(FldFatRate ~ funct3(MeanDepth_m, h,k,z,a), data=data1, + start=list(h = -17, k = .05, z = 22, a = 3.7), trace=TRUE, control=nls.control(minFactor=.00009)) 24.69316 : -17.00 0.05 22.00 3.70 Error in numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model > reg24 <-nls(FldFatRate ~ funct3(MeanDepth_m, h,k,z,a), data=data1, + start=list(h = -18, k = -.00008, z = 24, a = 3), trace=TRUE,...