Peter Dalgaard has already given some indications. However, nls() is pretty fragile as a solver in my experience. I'm in the process of putting some more robust (in the computing and not statistical sense) solvers in the nlmrt package on the R-forge project at
https://r-forge.r-project.org/R/?group_id=395 See output below. Best, JN > source("ardila.R", echo=T) > rm(list=ls()) > require(nlmrt) > logis<- expression(a/(1+exp((b-x)/c))) > D(logis, "x") a * (exp((b - x)/c) * (1/c))/(1 + exp((b - x)/c))^2 > myY<-c(5.5199668, 1.5234525, 3.3557000, 6.7211704, 7.4237955, 1.9703127, > 4.3939336, + -1.4380091, 3.2650180, 3.5760906, 0.2947972, 1.0569417) > myX<-c(1, 0, 0, 4, 3, 5, 12, 10, 12, 100, 100, 100) > mydata<-data.frame(X=myX, Y=myY) > ratelogis <- try(nls(Y ~ a*(exp((b-X)/c)*(1/c))/(1 + exp((b-X)/c))^2, + start=list(a = 21.16322, b = 8.83669, c = 2.957765),trace=TRUE, data=myda .... [TRUNCATED] 151.098 : 21.163220 8.836690 2.957765 127.1149 : 103.49326 11.43274 20.29663 111.344 : 833.02390 -45.86244 140.32986 111.3375 : 978.97105 -76.20547 159.90818 111.3374 : 1097.1336 -101.6763 174.2032 111.3227 : 1179.8406 -119.7406 183.3788 Error in nls(Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2, : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 > print(ratelogis) [1] "Error in nls(Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2, : \n step factor 0.000488281 reduced below 'minFactor' of 0.000976562\n" attr(,"class") [1] "try-error" attr(,"condition") <simpleError in nls(Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2, start = list(a = 21.16322, b = 8.83669, c = 2.957765), trace = TRUE, data = mydata): step factor 0.000488281 reduced below 'minFactor' of 0.000976562> > ratelogisn <- nlxb(Y ~ a*(exp((b-X)/c)*(1/c))/(1 + exp((b-X)/c))^2, + start=list(a = 21.16322, b = 8.83669, c = 2.957765),trace=TRUE, data=mydata .... [TRUNCATED] 'data.frame': 12 obs. of 2 variables: $ X: num 1 0 0 4 3 5 12 10 12 100 ... $ Y: num 5.52 1.52 3.36 6.72 7.42 ... NULL formula: Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2 lower:[1] -Inf -Inf -Inf upper:[1] Inf Inf Inf $watch [1] FALSE $phi [1] 1 $lamda [1] 1e-04 $offset [1] 100 $laminc [1] 10 $lamdec [1] 4 $femax [1] 10000 $jemax [1] 5000 Data variable Y : [1] 5.5199668 1.5234525 3.3557000 6.7211704 7.4237955 1.9703127 [7] 4.3939336 -1.4380091 3.2650180 3.5760906 0.2947972 1.0569417 Data variable X : [1] 1 0 0 4 3 5 12 10 12 100 100 100 Start:lamda: 1e-04 SS= 151.098 at a = 21.16322 b = 8.83669 c = 2.957765 1 / 0 gradient projection0 = -113.7506 gangle= -0.2949267 Stepsize= 1 <<lamda: 4e-05 SS= 127.1308 at a = 102.7381 b = 11.32418 c = 20.15418 2 / 1 gradient projection0 = -55.55629 gangle= -0.1594696 Stepsize= 1 lamda: 4e-04 SS= 129.9286 at a = 378.4733 b = -71.39815 c = 50.21802 3 / 2 gradient projection0 = -49.05021 gangle= -0.505497 [snip] lamda: 2814.75 SS= 50.50144 at a = 36.13314 b = 2.572373 c = 1.079811 42 / 25 gradient projection0 = -5.685471e-20 gangle= -0.9954213 Stepsize= 1 lamda: 28147.5 SS= 50.50144 at a = 36.13314 b = 2.572373 c = 1.079811 43 / 25 gradient projection0 = -5.68707e-21 gangle= -0.9954364 Stepsize= 1 lamda: 281475 SS= 50.50144 at a = 36.13314 b = 2.572373 c = 1.079811 44 / 25 gradient projection0 = -5.687227e-22 gangle= -0.995437 Stepsize= 1 No parameter change > print(ratelogisn) $resid [1] -0.3897067 1.0662193 -0.7660282 -1.1606765 0.6222073 0.9203074 [7] -4.3885301 1.4723895 -3.2596145 -3.5760906 -0.2947972 -1.0569417 $jacobian a b c [1,] 1.419821e-01 -2.954633e+00 -4.486669e-01 [2,] 7.167026e-02 -1.992780e+00 2.349023e+00 [3,] 7.167026e-02 -1.992780e+00 2.349023e+00 [4,] 1.538890e-01 2.981895e+00 -1.207117e+00 [5,] 2.226765e-01 1.456449e+00 -6.874521e+00 [6,] 7.999913e-02 2.165639e+00 2.191813e+00 [7,] 1.495441e-04 5.002498e-03 3.867174e-02 [8,] 9.514926e-04 3.177379e-02 1.867210e-01 [9,] 1.495441e-04 5.002498e-03 3.867174e-02 [10,] 6.050186e-40 2.024541e-38 1.806428e-36 [11,] 6.050186e-40 2.024541e-38 1.806428e-36 [12,] 6.050186e-40 2.024541e-38 1.806428e-36 $feval [1] 44 $jeval [1] 25 $coeffs [1] 36.133144 2.572373 1.079811 $ssquares [1] 50.50144 > ratelogis <- try(nls(Y ~ a*(exp((b-X)/c)*(1/c))/(1 + exp((b-X)/c))^2, + start=list(a = 36.133144, b= 2.572373, c=1.079811),trace=TRUE, data=mydat .... [TRUNCATED] 50.50144 : 36.133144 2.572373 1.079811 > print(ratelogis) Nonlinear regression model model: Y ~ a * (exp((b - X)/c) * (1/c))/(1 + exp((b - X)/c))^2 data: mydata a b c 36.133 2.572 1.080 residual sum-of-squares: 50.5 Number of iterations to convergence: 0 Achieved convergence tolerance: 4.818e-07 > On 04/17/2012 06:00 AM, r-help-requ...@r-project.org wrote: > Message: 112 > Date: Mon, 16 Apr 2012 23:23:07 -0500 > From: "Francisco Mora Ardila" <fm...@oikos.unam.mx> > To: r-help@r-project.org > Subject: [R] error using nls with logistic derivative > Message-ID: <20120417035718.m33...@oikos.unam.mx> > Content-Type: text/plain; charset=utf-8 > > 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: > > ratelogis <- nls(Y ~ a*(exp((b-X)/c)*(1/c))/(1 + exp((b-X)/c))^2, > start=list(a = 21.16322, b = 8.83669, c = 2.957765), > ) > > The data is: > >> > Y > [1] 5.5199668 1.5234525 3.3557000 6.7211704 7.4237955 1.9703127 > [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 'minFactor' of 0.000488281 > > So it seems that as I modify minFactor, the step factor reduces also and I > can never > reach a solution. > > Does anybody Know what am I doing wrong? Is there a problem with the formula? > How can I > solve it? I tried some suggestions on R-help related topics but did not work. > > Thanks > > Francisco ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
