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...have (i.e. when the model is parameterized with p and q R seems to be seeing three parameters, when m is also included R seems to be seeing four). My experience with nls is limited, can someone explain to me why it's doing this? I've included the data set I'm working with (published in Michalakelis et al. 2008) and some example code. ## Assign relevant variables adoption <- c(167000,273000,531000,938000,2056452,3894103,5932090,7963742,9314687,10469060,11393302,11976340) time <- seq(from = 1,to = 12, by = 1) ## Models Bass.Model <- adoption ~ ((p + q)^2/p) * (exp(-(p + q) * time)/((q...

The problem is badly scaled -- parameters from very small to very big. nlmrt package manages a bit better, but scaling would likely make it and nls both happier. nlxb insists on having its data in a data frame. Here's my code rm(list=ls()) Laptop_sale <- c(1405, 1863,2027,2669,2938,5275,6595,6943,8621,10905,12420,22400,32380,31600,34900,43163,47838,47592) CuSale <- Laptop_sale time