[SciPy-User] ancova with optimize.curve_fit

[Peter Tittmann]

 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 


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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