[SciPy-User] ancova with optimize.curve_fit

josef.pktd@gmai... josef.pktd@gmai...
Thu Dec 2 15:33:51 CST 2010

On Thu, Dec 2, 2010 at 2:55 PM, Peter Tittmann <ptittmann@gmail.com> wrote:
> Greetings,
> Im attempting to conduct analysis of covariance (ANCOVA) using a non-linear
> regression with curve_fit in optimize. The doc string states:
> "xdata : An N-length sequence or an (k,N)-shaped array for functions with k
> predictors. The independent variable where the data is measured."
> I am hoping that this means that if I pass the independent variable and a
> categorical variable, the resulting covariance matrix will reflect the
> variability in the equation coefficients among the categorical variables.
> 1. Is tis the case?
> 2. If so, i'm having a problem with the input array for xdata. The following
> extracts data from a relational database (thats the sql). the eqCoeff()
> function works fine, however when I add a second dimension to the xdata in
> th ancova() function (indHtPlot), curve fit produces an error which seems to
> be related to the structure of my input array. I've tried column_stack and
> vstack to form the arrays. Any assistance would be gratefully received.
> import birdseye_db as db
> import numpy as np
> from scipy.optimize import curve_fit
> def getDiam(ht, a, b):
>     dbh = a * ht**b
>     return dbh

if ht is 2dimensional, then dbh is also two dimensional (n,k). there
should be a reduce, e.g. sum in here so that the return is 1d.


> def eqCoeff():
>     '''estimates coefficients a and b in dbh= a* h**b using all trees where
> height was measured'''
>     species=[i[0].strip(' ') for i in db.query('select distinct species from
> plots')]
>     res3d=db.query('select dbh, height, species from plots where ht_code=1')
>     indHt=[i[1] for i in res3d]
>     depDbh=[i[0] for i in res3d]
>     estimated_params, err_est = curve_fit(getDiam, indHt, depDbh)
>     return estimated_params, err_est
> def ancova():
>     res=db.query('select dbh, height, plot, species from plots where
> ht_code=1')
>     indHtPlot= np.column_stack(([i[1] for i in res],[i[2] for i in res] ))
>     depDbh=[i[0] for i in res]
>     estimated_params, err_est = curve_fit(getDiam, indHtPlot, depDbh)
>     return estimated_params, err_est
> Thanks in advance
> --
> Peter Tittmann
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