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...I assume are the analogs of maxiter and tol in nls.control. I would be happy to hear from anyone who has an idea on what parameters in gnlsControl to change to get convergence. Cheers Dan Coleman Genentech Inc. > gnlsControl function (maxIter = 50, nlsMaxIter = 7, msMaxIter = 50, minScale = 0.001, tolerance = 1e-06, nlsTol = 0.001, msTol = 1e-07, msScale = lmeScale, returnObject = FALSE, msVerbose = FALSE, apVar = TRUE, .relStep = (.Machine$double.eps)^(1/3), nlmStepMax = 100, opt = c("nlminb", "optim"), optimMethod = "BFGS", mi...

...tly does "Warning message: Step halving factor reduced below minimum in NLS step in: gnls(model = y ~ 5 + ...)" mean? I have tried to address this by specifying "control = list(maxIter = 1000, pnlsMaxIter = 200, msMaxIter = 1000, tolerance = 1e-06, pnlsTol = 1e-04, msTol = 1e-07, minScale = 1e-10, returnObject = TRUE)" in my model calls, but this does not entirely eliminate the problem (I am running gnls( ) 24 separate times on separate data sets). Much thanks in advance, david paul #Constructing Indicator Variables indicator <- paste( "foo$X <- sapply(foo$...

...#39;maxIter', 'pnlsMaxIter', 'msMaxIter', and 'niterEM' by a factor of 20. I don't believe there is any harm in doing this. There are a few parameters, however, in the nlme controls that I'm not quite sure what they mean or do. For example, exactly what is 'minScale?' The help file says that it is "the minimum factor by which to shrink the default step size in an attempt to decrease the sum of squares in the 'PNLS' step." What does it mean to increase the default value? Will increasing this value lead to more convergence because the crit...

...~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs ~ SSlogis(Months, Asym.Int + Asym.Group * Groupdum, : Step halving factor reduced below minimum in NLS step I tried adding ,control=list(minScale=.0001)) But get the same lack-of-convergence message. Any ideas on how to fix this and why gnls is finickier than nls? I use a good starting point about equal to the final param values nls gives. The logistic function does indeed give a decent fit to the data. I need gnls instead of nls because...