Thank you so much for your valuable feedback Berwin.
Have a great day.
Cheers,
Paul
El El dom, 20 de ago. de 2023 a la(s) 10:21 p. m., Berwin A Turlach <
berwin.turl...@gmail.com> escribió:
> G'day Paul,
>
> On Sun, 20 Aug 2023 12:15:08 -0500
> Paul Bernal wrote:
>
> > Any idea on how to
G'day Paul,
On Sun, 20 Aug 2023 12:15:08 -0500
Paul Bernal wrote:
> Any idea on how to proceed in this situation? What could I do?
You are fitting a simple asymptotic model for which nls() can find good
starting values if you use the self starting models (SSxyz()). Well,
Doug (et al.) choose
Thanks a lot Bert, I appreciate your help.
Kind regards,
Paul
El El dom, 20 de ago. de 2023 a la(s) 2:39 p. m., Bert Gunter <
bgunter.4...@gmail.com> escribió:
> Basic algebra and exponentials/logs. I leave those details to you or
> another HelpeR.
>
> -- Bert
>
> On Sun, Aug 20, 2023 at 12:17
Basic algebra and exponentials/logs. I leave those details to you or
another HelpeR.
-- Bert
On Sun, Aug 20, 2023 at 12:17 PM Paul Bernal wrote:
> Dear Bert,
>
> Thank you for your extremely valuable feedback. Now, I just want to
> understand why the signs for those starting values, given the
Dear Bert,
Thank you for your extremely valuable feedback. Now, I just want to
understand why the signs for those starting values, given the following:
> #Fiting intermediate model to get starting values
> intermediatemod <- lm(log(y - .37) ~ x, data=mod14data2_random)
> summary(intermediatemod)
Oh, sorry; I changed signs in the model, fitting
theta0 + theta1*exp(theta2*x)
So for theta0 - theta1*exp(-theta2*x) use theta1= -.exp(-1.8) and theta2 =
+.055 as starting values.
-- Bert
On Sun, Aug 20, 2023 at 11:50 AM Paul Bernal wrote:
> Dear Bert,
>
> Thank you so much for your kind
I haven't looked to see whether you or Bert made an algebraic mistake
in translating the parameters of the log-linear model to their
equivalents for the nonlinear model, but nls() gives me the same answer
as nls() in this case (I called my data 'dd2'):
n1 <- nlxb(y~theta1 -
Dear Bert,
Thank you so much for your kind and valuable feedback. I tried finding the
starting values using the approach you mentioned, then did the following to
fit the nonlinear regression model:
nlregmod2 <- nls(y ~ theta1 - theta2*exp(-theta3*x),
start =
My answer is WAY longer than Bert Gunter's but maybe useful nonetheless.
(Q for John Nash: why does the coef() method for nlmrt objects
return the coefficient vector **invisibly**? That seems confusing!)
Here's what I did:
* as a preliminary step, adjust the formula so that I don't
I got starting values as follows:
Noting that the minimum data value is .38, I fit the linear model log(y -
.37) ~ x to get intercept = -1.8 and slope = -.055. So I used .37,
exp(-1.8) and -.055 as the starting values for theta0, theta1, and theta2
in the nonlinear model. This converged without
Dear friends,
This is the dataset I am currently working with:
>dput(mod14data2_random)
structure(list(index = c(14L, 27L, 37L, 33L, 34L, 16L, 7L, 1L,
39L, 36L, 40L, 19L, 28L, 38L, 32L), y = c(0.44, 0.4, 0.4, 0.4,
0.4, 0.43, 0.46, 0.49, 0.41, 0.41, 0.38, 0.42, 0.41, 0.4, 0.4
), x = c(16, 24, 32,
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