Hi all, I have a question about the optimisation methods used in nonlinear regression. I have some data that I would like to fit a tobit regression model to (see code below). It seems that the solution is very sensitive to the initial condition that I give it - is there any option to use a different optimisation process where the solution will be less sensitive to the initial condition? Many thanks in advance, J
require(AER) data_in = c(0,6,12,18,24,30,36,42,48,54,60,66,72,78) data_in2 = data_in^2 data_in3 = data_in^3 data_out = c(139487.00,133333.00,62500.00,58823.00,56338.00,27549.00,4244.00,600.00,112.00,95.00,48.00,31.00,15.00,14.99) ldata_out = log(data_out) m2 <- lm(ldata_out ~ data_in + data_in2) print(m2) quad_mod <- tobit(ldata_out ~ data_in + data_in2, left= log(15-0.01),init = coef(m2)) quad_mod2 <- tobit(ldata_out ~ data_in + data_in2, left= log(15-0.01),init = coef(m2)-0.01) print(quad_mod) print(quad_mod2) -- View this message in context: http://r.789695.n4.nabble.com/different-regression-coeffs-with-different-starting-point-tp3353536p3353536.html Sent from the R help mailing list archive at Nabble.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.
