Dear all,
I found the solution to my question on internet:
https://www.r-bloggers.com/introducing-propagate/
The ‘propagate’ package on CRAN can do this It has one single purpose:
propagation of uncertainties (“error propagation”).
predictNLS: The propagate function is used to calculate the pro
Unless there is good reason to do otherwise, you should cc the list to
allow others to provide perhaps better responses or to correct my
possible errors. I have done so here.
If your "parameter" is fixed in the modeling it cannot contribute to
the uncertainty of estimation of the remaining model p
Vicente:
You have not received a reply. I think it is because your post appears
to reveal a profound lack of understanding about how empirical
modeling works: the uncertainty in parameter estimates derives from
the uncertainty in the data (via the modeling process, of course). You
cannot set them
Dear all,
I would like to introduce an input parameter with an associated standard
error to perform a fitting using the nls function (or any similar function):
parameter1 = 9.00 +/- 0.20 (parameter 1 has a value of 9.00 and standard
error of 0.20)
fittingResults <- nls(y ~ function(xdata, ydata
4 matches
Mail list logo
