On Thu, Jul 12, 2012 at 3:40 PM, Felipe Carrillo <mazatlanmex...@yahoo.com> wrote: > I get a different error now: >> nls(weight ~ cbind(1, exp(gamma*week)), weightData, start = list(gamma= >> 0.2), alg = "plinear") > Error in nls(weight ~ cbind(1, exp(gamma * week)), weightData, start = > list(gamma = 0.2), : > step factor 0.000488281 reduced below 'minFactor' of 0.000976562 > The help file says: ........When start is missing, a very cheap guess for > start is tried (if algorithm != "plinear").
Please give a reproducible example by setting the seed. This reproducible example converges: > set.seed(123) > weight_random <- runif(50,1,24) > weight <- sort(weight_random) > weightData <- data.frame(weight,week=1:50) > nls(weight ~ cbind(1, exp(gamma*week)), weightData, start = list(gamma = > 0.2), alg = "plinear") Nonlinear regression model model: weight ~ cbind(1, exp(gamma * week)) data: weightData gamma .lin1 .lin2 1.136e-03 -3.949e+02 3.962e+02 residual sum-of-squares: 9.17 Number of iterations to convergence: 8 Achieved convergence tolerance: 9.581e-06 as does nls with Gauss Newton: > nls(weight ~ alpha + beta*exp(gamma*week), weightData, start = + c(alpha = 0.0, beta = 1, gamma = 0.2) + ) Nonlinear regression model model: weight ~ alpha + beta * exp(gamma * week) data: weightData alpha beta gamma -3.949e+02 3.961e+02 1.136e-03 residual sum-of-squares: 9.17 Number of iterations to convergence: 48 Achieved convergence tolerance: 2.906e-06 > > So I removed 'plinear' from the call and got the following: > nls(weight ~ cbind(1, exp(gamma*week)), weightData,start = > list(gamma=0.2),trace=TRUE) It cannot be specified as if it were a plinear model but then use Gauss-Newton. See ?nls -- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.com ______________________________________________ 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.