[SciPy-User] calculate predicted values from regression + confidence intervall
Thu Oct 20 15:50:39 CDT 2011
Am 20.10.11 16:12, schrieb email@example.com:
> On Thu, Oct 20, 2011 at 5:11 AM, Christian K. <firstname.lastname@example.org> wrote:
>> <josef.pktd <at> gmail.com> writes:
>>>> f(X,Y) = a1-a2*log(X)+a3/Y (inverse power/Arrhenius model from accelerated
>>>> reliability testing)
>>> your f(X,Y) is still linear in the parameters, a1, a2, a3. So the
>>> linear version still applies.
>> Ok, but then I do not understand how to follow your indications for the
>> prediction interval:
>>>> distributed with mean y = Y = X*beta, and var(y) = X' * cov_beta * X +
>>>> var_u_estimate (dot products for appropriate shapes)
>> X in my case is [X,Y] and cov_beta has a shape of 3x3, since there are 3
>> Sorry for my ignorance on statistics, I really apppreaciate your help.
> I'm attaching a complete example for the linear in parameters case,
> including the comparison with statsmodels.
Ok, I got it, thank you very much. As I understood, this works for OLS
(only?). What about if I get the covariance matrix from a 2D odr/leastsq
fit from scipy.odr ? I noticed, that the covariance matrices differ by a
constant (large) factor.
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