[R] different regression coeffs with different starting point

[Jen]

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)


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