If I have a data that I fit with NonlinearModelfit
that fits a data based on two fitting parameters, c1
and c2
.
When I used nlm["ParameterTable"] // Quiet
I get the following table:
If I have an equation such as:
eq = (2.303*((70 + 273.15)^2)*(c1/c2))/1000
Is there any code (as opposed to doing it manually) I can use to calculate the value of eq
with the combined standard deviation based on the standard deviations of c1
and c2
from the table?.
To clarify I would like to get something like: eq = (2.303*((70 + 273.15)^2)*(8.08318/21.1577))/1000=103.604
but also the standard deviation based on the errors of c1
and c2
as to get something like 103.604 +- standard error
Thank you!
EDIT:
For reference eq
comes from:
eq = ((log10q - Log10[qref]) == c1*(Tfp - Tfpref)/(c2 + (Tfp - Tfpref)));
model = Tfp /. Solve[eqn, Tfp][[1]]// FullSimplify;
const = {Tfpref -> 70, qref -> 10/60};
model2 = model /. (const // Rationalize) // FullSimplify;
nlm = NonlinearModelFit[data, {model2, c1 > 5, c2 > 5}, {c1, c2},
log10q];
where everything in eq
is known except the fitting parameters c1
and c2
eq /.nlm["BestFitParameters"]
? $\endgroup$eq
or an estimate of the standard error foreq
? $\endgroup$eq = (2.303*((70 + 273.15)^2)*(8.08318/21.1577))/1000=103.604
but also the standard deviation based on the errors of c1 and c2 as to get something like103.604 +- standard error
$\endgroup$eq /.nlm["BestFitParameters"]
only gives me the value ofeq
(e.g. 103.604) but not its standard error. In other words, it does not give me something like103.604 +- standard error
$\endgroup$