[SciPy-User] ancova with optimize.curve_fit

Peter Tittmann ptittmann@gmail....
Thu Dec 2 16:59:53 CST 2010


 getDiam is a predictor to get dbh from height. It works with curve_fit to find coefficients a and b given datasetset of known dbh/height pairs. You are right, what I want is dummy variables for each plot. I'll see if I can get that worked out by revising getDiam..


Thanks again





On Thursday, December 2, 2010 at 2:43 PM, josef.pktd@gmail.com wrote:

> On Thu, Dec 2, 2010 at 4:51 PM, Peter Tittmann <ptittmann@gmail.com> wrote:
> 
> >  here is some of the data. Josef, i'm not sure I understand your suggestion.
> >  dbh is the dependent variable and is id (actually a list). The independent
> >  variable is height and the categorical variable to test for covariance with
> >  is plot.
> >  Maybe I'm confused and am trying to do something that cant be done this
> >  way...
> > 
> > 
> 
> I don't understand what your getDiam function is supposed to do. In
> ancova, indHtPlot is 2d (nobs,2) with variables dbh and plot. getdiam
> calculates [a * dbh**b, a * plot**b] a (nobs,2) array, but it should
> produce instead a 1d (nobs,) array.
> Maybe you want to sum the functions with dbh and plot for each
> observation sum([a * dbh**b, a * plot**b], axis=1)
> 
> This should solve the curvefit problem, but if plot is categorical,
> then this wouldn't be correct since it is just treated as metric
> variable. Maybe you want some dummy variables for plot instead. ???
> 
> Josef
> 
> 
> 
> >  Thanks for any further assistance..
> >  Peter
> > 
> >  --
> >  Peter Tittmann
> > 
> > 
> >  On Thursday, December 2, 2010 at 1:33 PM, josef.pktd@gmail.com wrote:
> > 
> >  On Thu, Dec 2, 2010 at 2:55 PM, Peter Tittmann  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.
> > 
> >  Josef
> > 
> > 
> >  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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