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...t;- expsum + max(expsum) *.001 * rnorm(100) expsum.df <-data.frame(expsumNoisy) ## estimate decay rates, amplitudes with default Gauss-Newton summary (nls(expsumNoisy ~ getExpmat(k, 1:100) %*% sp, expsum.df, start = list(k = c(.6,.02), sp = c(1,2)), trace=TRUE, control = nls.control(maxiter=20, warnOnly = TRUE))) ## won't work with port summary (nls(expsumNoisy ~ getExpmat(k, 1:100) %*% sp, expsum.df, start = list(k = c(.6,.02), sp = c(1,2)), algorithm = "port", trace=TRUE, control = nls.control(maxiter=20, warnOnly = TRUE)))

...7, 10.740636, 11.246827,17.022761, 21.430386) x2<-c(25.0, 29.3, 33.8, 38.3, 42.8, 47.2, 51.6, 55.8, 60.4, 64.9) dat <- data.frame(x2,y2) nlmod <- nls(y2 ~ ifelse(x2 < xint+(yint/slp), yint, yint + (x2-(xint+(yint/slp)))*slp), data=dat, control=list(minFactor=1e-5,maxiter=500,warnOnly=T), start=list(xint=40.49782, yint=1.013981, slp=0.8547828), na.action=na.omit, trace=T) ##plotting the function plot(dat$x2,dat$y2) segments(x0=0, x1=coef(nlmod)[1]+coef(nlmod)[2]*coef(nlmod)[3], y0=coef(nlmod)[2], y1=coef(nlmod)[2]) segments(x0=coef(nlmo...

Hello everyone, I have come across a bit of an odd problem: I am currently analysing data PCR reaction data of which part is behaving exponential. I would like to fit the exponential part to the following: y0 + alpha * E^t In which Y0 is the groundphase, alpha a fluorescence factor, E the efficiency of the reaction & t is time (in cycles) I can get this to work for most of my reactions,

...6392, 36.39401717, 32.43620195, 44.77442277) x1<-c(24.6, 28.9, 33.2, 37.6, 42.0, 46.4, 50.9, 55.3, 59.8, 64.3) dat <- data.frame(x1,y1) nlmod <- nls(y1 ~ ifelse(x1 < xint+(yint/slp), yint, yint + (x1-(xint+(yint/slp)))*slp), data=dat, control=list(minFactor=1e-5,maxiter=500,warnOnly=T), start=list(xint=39.27464924, yint=0.09795576, slp=2.15061064), na.action=na.omit, trace=T) ##plotting the function plot(dat$x1,dat$y1) segments(x0=0, x1=coef(nlmod)[1]+coef(nlmod)[2]*coef(nlmod)[3], y0=coef(nlmod)[2], y1=coef(nlmod)[2]) segments(x0=coef(nl...

...rnal call (internal to nls) of the chol2inv fails. ============================================ Error in chol2inv(object$m$Rmat()) : element (2, 2) is zero, so the inverse cannot be computed In addition: Warning messages: 1: In nls(myModel.nlm, fData, start = initialValues, control = nls.control(warnOnly = TRUE), : number of iterations exceeded maximum of 50 2: In nls(myModel.nlm, fData, start = initialValues, control = nls.control(warnOnly = TRUE), : singular gradient =========================================================== Any suggestions on how to prevent chol2inv from breaking my si...

...lotfit(r.PTG.V) I tried to use the function plotfit on the result of the following for loop but I got an error message: for (i in 1:length(formList)) { resultList[[i]] <- nls(formList[[i]], data=subset(dati, Fiume=="Laveggio"), start=startList7[[i]], nls.control(maxiter=1000, warnOnly=TRUE), algorithm='port', na.action=na.omit,lower=lowerList7[[i]], upper=upperList7[[i]]) } plotfit(resultList[[1]] "Error in diff(as.numeric(y[ord])) : (list) object cannot be coerced to type 'double'" Could somebody tell me what this error means? how can I fix...

...+ PAMPLITUDE * cos(2 * pi * (NEWX - POFFSET)/PERIOD), start = list(PMESOR = MESOR, PAMPLITUDE = AMPLITUDE, ... > $ dataClasses: Named chr "numeric" > ..- attr(*, "names")= chr "NEWX" > $ control :List of 2 > ..$ maxiter : num 1000 > ..$ warnOnly: logi TRUE > - attr(*, "class")= chr "nls" However, none of these elements appears to contain the residual sum of squares. Help! Thanks. Dennis Dennis Fisher MD P < (The "P Less Than" Company) Phone: 1-866-PLessThan (1-866-753-7784) Fax: 1-866-PLessT...

...68e-05, 0.412938194338572, 3.32626693693668e-05, 0.0441665508678859, 2.86654588531843, 0.412938194338572, 2.86654588531843, 473.324480704723) nls(formula = wcorr ~ (Y0 + a * exp(m1 * -dist/100)), start = pa1, control = list(maxiter = 1000, tol = 1e-05, minFactor = 0.0009765625, printEval = FALSE, warnOnly = TRUE), algorithm = "default", trace = FALSE) list(isConv = TRUE, finIter = 7, finTol = 4.21396379219722e-06, stopCode = 0, stopMessage = "converged") list(maxiter = 1000, warnOnly = TRUE) NULL c(-0.00018212632256334, 0.166967461792613, 10.8045839935834, 0.00028860788...

...ta$noisy[1:4]), X=0) # inital fit of nls fit.rk4 <- nls(noisy ~ rk4(xstart.est, time, minmod, c(p1=p1, p2=p2, p3=p3))[,2], data=sim.data, start=list(p1=0.025, p2=0.02, p3=0.00004), trace=F, control=list(tol=1e-4, minFactor=1/1024/1024, warnOnly=T), weight=(1/(G*0.015))*Weight ) # define deviance function to optimize DevFunc <- function(p4) { fit.rk4 <- nls(noisy ~ rk4(c(G=p4, X=0), time, minmod, c(p1=p1, p2=p2, p3=p3))[,2], data=sim.data, start=list(p1=su...

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

..., upper=c(10e7, 100, 300,100, 300 ), control=list(trace=FALSE)) par2 <- fm6$optim$bestmem names(par2) <- c("c", "A", "n1", "B", "n2") fit2 <- nls(wcorr ~ (c + A*exp(n1 * -dist) + B*exp(n2 * -dist)), start=par2,control =list(maxiter=1000, warnOnly=TRUE)) However, I keep getting the following error - Error in numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model OR In nls(wcorr ~ (c + A * exp(n1 * -dist) + B * exp(n2 * -dist)), : singular gradient Can anyone suggest how to r...

...2.7019079 1.04921802 6 3.0679585 1.60745502 9 0.9436973 -0.31875876 10 1.2201133 -0.21467016 11 1.6470043 0.06069784 12 2.2090048 0.54032947 13 2.6864857 1.04921802 14 3.0673523 1.60745502 new.cont=nls.control(maxiter = 10000, tol = 1e-05, minFactor = 1e-08, printEval = FALSE, warnOnly = FALSE) b0.A=.9*min(DAT$y) b1.A=1.1*max(DAT$y)-b0.A b2.A=-1*mean(DAT$x) b3.A=1 b0.A b1.A b2.A b3.A nls.mdl.A=nls(y~b0 + b1/(1+exp(-b2-b3*x)),data=DAT,start = list(b0=b0.A, b1=b1.A, b2=b2.A, b3=b3.A), lower=-10, upper=10, algorithm="port",trace=T,control=new.cont) ################...

...ontrol = structure(list(maxiter = 50, tol = 1e-05, ... $ dataClasses: Named chr "numeric" ..- attr(*, "names")= chr "Samples" $ control :List of 5 ..$ maxiter : num 50 ..$ tol : num 1e-05 ..$ minFactor: num 0.000977 ..$ printEval: logi FALSE ..$ warnOnly : logi FALSE - attr(*, "class")= chr "nls" Como se observa muchos de estos elmentos no son mostrados en el resultado de la funcion a menos que se use la funcion "str" , quisiera saber como hacer esto, ocultar algunos elementos de una lista producida por una fun...

Colleagues, I am fitting an Emax model using nls. The code is: START <- list(EMAX=INITEMAX, EFFECT=INITEFFECT, C50=INITC50) CONTROL <- list(maxiter=1000, warnOnly=T) #FORMULA <- as.formula(YVAR ~ EMAX - EFFECT * XVAR^GAMMA / (XVAR^GAMMA + C50^GAMMA)) ## alternate version of formula FORMULA <- as.formula(YVAR ~ EMAX - EFFECT / (1 + (C50/XVAR)^GAMMA)) FIT <- nls(FORMULA, start=START, control=CONTROL, trace=T) If GAMMA equals 10-80, nls conv...

Hi, All: What tools exist for diagnosing singular gradient problems with 'nls'? Consider the following toy example: DF1 <- data.frame(y=1:9, one=rep(1,9)) nlsToyProblem <- nls(y~(a+2*b)*one, DF1, start=list(a=1, b=1), control=nls.control(warnOnly=TRUE)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates This example is obviously stupid, but other singular gradient problems are not so obvious. If we transfer this problem to 'optim', we can get diagnostics from an...

...)*sin(theta0) ) * exp( kappa*cos(theta)*cos(theta0) ) } nls(dens ~ FisherAvgdPdf(theta,theta0,kappa), data = data.frame( theta=acos(h$mids), dens=h$density ), start=c( theta0=0.5, kappa=4.0 ), algorithm="port", lower=c(0,0), upper=c(acos(xlims[1]),500), control=list(warnOnly=TRUE) ) ##------------------------------------------------------------------ On one machine, nls converges, and on the other it doesn't. Any ideas why, and which is "right"? I can't see anything in R News that could be relevant. The different R versions (and computers)...

...ontrol = structure(list(maxiter = 50, tol = 1e-05, ... $ dataClasses: Named chr "numeric" ..- attr(*, "names")= chr "Samples" $ control :List of 5 ..$ maxiter : num 50 ..$ tol : num 1e-05 ..$ minFactor: num 0.000977 ..$ printEval: logi FALSE ..$ warnOnly : logi FALSE - attr(*, "class")= chr "nls" Como se observa muchos de estos elmentos no son mostrados en el resultado de la funcion a menos que se use la funcion "str" , quisiera saber como hacer esto, ocultar algunos elementos de una lista producida por una func...

...control = structure(list(maxiter = 50, tol = 1e-05, ... $ dataClasses: Named chr "numeric" ..- attr(*, "names")= chr "Samples" $ control :List of 5 ..$ maxiter : num 50 ..$ tol : num 1e-05 ..$ minFactor: num 0.000977 ..$ printEval: logi FALSE ..$ warnOnly : logi FALSE - attr(*, "class")= chr "nls" Como se observa muchos de estos elmentos no son mostrados en el resultado de la funcion a menos que se use la funcion "str" , quisiera saber como hacer esto, ocultar algunos elementos de una lista producida por una funci...

I am running a 3-parameter nonlinear fit using the default Gauss-Newton method of nls. initialValues.L = list(b=4,d=0.04,t=180); fit.nls.L = nls( myModel.nlm , fData.L, start = initialValues.L, control = nls.control(warnOnly = TRUE), trace=T ); summary.nls.L = summary(fit.nls.L); I run the same analysis in SAS proc NLIN. proc nlin data=apples outest=a; parms b=4 d=.04 t=180; model Y = b/(1+exp(-d*(X-t))); output out=b p=yhat r=yresid ; run; proc print data=a; run; A summary of the outputs: - Same coefficien...

....95065902 0.002264977 : 0.14378271 6.94339837 -0.01278772 31.95059050 If I make one parameter further away from optimum, I get this: > rate.nls <- nls(Dev ~ exp(rho * (T)) - exp(rho*Tmax - (Tmax - T)/del)+ + lam , data = xx, trace = TRUE, + nls.control(warnOnly = TRUE), + start = c(rho = 0.15, del = 7, lam = 1.02, Tmax = 30)) 59.80968 : 0.15 7.00 1.02 30.00 4.69849 : 0.14673457 6.83443060 -0.01417782 29.94392535 0.06973771 : 0.14787046 6.75099517 -0.01281668 28.88853959 0.0119916 : 0.1460709 6.8333430 -0.0128098 30.160004...