All I had in mind was that *if* you can set up an optimization over
the parameters such that the prediction for a particular set of
predictors is constrained to come out to a specified target value
(i.e., nonlinear equality constraints), then you can compute profile
confidence intervals on the pred
On 06 Jan 2015, at 07:40 , Rune Haubo wrote:
> On 5 January 2015 at 21:08, Ben Bolker wrote:
>> Roger Coppock cox.net> writes:
>>
>>>
>>> When will "R" implement the "se.fit" option to the
>>> predict.nls() function? Is there some schedule?
>>>
>>
>> I think this is unlikely to happen, e
On 5 January 2015 at 21:08, Ben Bolker wrote:
> Roger Coppock cox.net> writes:
>
>>
>> When will "R" implement the "se.fit" option to the
>> predict.nls() function? Is there some schedule?
>>
>
> I think this is unlikely to happen, ever (sorry). The exact method
> for finding confidence inte
Roger Coppock cox.net> writes:
>
> When will "R" implement the "se.fit" option to the
> predict.nls() function? Is there some schedule?
>
I think this is unlikely to happen, ever (sorry). The exact method
for finding confidence intervals on nonlinear fits would be
to compute likelihood p
When will "R" implement the "se.fit" option to the predict.nls() function? Is
there some schedule?
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