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.
William Dunlap
2011-Mar-14 15:43 UTC
[R] different regression coeffs with different starting point
> -----Original Message----- > From: r-help-bounces at r-project.org > [mailto:r-help-bounces at r-project.org] On Behalf Of Jen > Sent: Monday, March 14, 2011 3:27 AM > To: r-help at r-project.org > Subject: [R] different regression coeffs with different starting point > > 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)When I tried this the second call to tobit gave a warning: > quad_mod2 <- tobit(ldata_out ~ data_in + data_in2, leftlog(15-0.01),init = coef(m2)-0.01) Warning message: In survreg.fit(X, Y, weights, offset, init = init, controlvals control, : Ran out of iterations and did not converge Did you get the warning? If not there is a bug in your system or you are using an old version of some package. If there was a warning, it is misleading to not put the warning in the description of the problem. Responding to the warning, I increased maxiter to 100 and got essentially the same result as in quad_mod (only the iteration number and the call were different). > quad_mod2a <- tobit(ldata_out ~ data_in + data_in2, leftlog(15-0.01),init = coef(m2)-0.01, maxiter=100) > all.equal(quad_mod, quad_mod2a) [1] "Component 5: Mean relative difference: 3.181818" "Component 13: target, current do not match when deparsed" > names(quad_mod)[5] [1] "iter" > quad_mod[[5]] [1] 11 > quad_mod2a[[5]] [1] 46 Bill Dunlap Spotfire, TIBCO Software wdunlap tibco.com> > > -- > 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 at 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. >