search for: cusale

.... 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 <- seq_along(Laptop_sale) Bass.Model <- Laptop_sale ~ m * ((p + q)^2/p) * (exp(-(p + q) * time)/((q / p) * exp(-(p + q) * time) + 1)^2) Bass.Fit <- nls(formula = Bass.Model, start = c(p = 0.03, q = 0.4, m =max(CuSale)), trace = TRUE) Bass.Fit library(nlmrt) xdata&lt...