R help - Jun 2010 - Confidence interval calculation for intersection of two quadratic lines

How do I calculate the confidence interval for the value x given by the
intersection of two quadratics (i.e. parabolas)?

I fit two quadratics of the form:

  y = C1 + B1*x + A1*x^2
  y = C2 + B2*x + A2*x^2

to two sets of points N1 and N2.

I test for whether they intersect, if they do then I calculate the roots of:
  0 = (C1 - C2) + (B1 - B2)*x + (A1 - A2)*x^2

to determine where they intersect and choose the proper root.
I have propagated errors through the quadratic equation so that I may calculate
the variance of each root.

Naively I might take the variance of the root, find a Student-t quantile for
degrees-of-freedom = (N1 + N2 - 6), and then calculate
C.I. = root +/- variance * t-quantile 

However I have several reservations about this approach:
(1) Let's say that one quadratic fit devolves to a linear fit (A1 ~ 0)*,
then I have the intersection of a straight line and a quadratic.  (I code this
situation separately with different error propagation formulas)  However
intuitively if the intercept of the straight line varies by some dy up or down I
would expect that the intersection with a parabola varies by a different amount
if the straight line is moved up versus down, e.g. the confidence interval
should be asymmetric.

(2) The variances for the coefficients A1, B1, C1 depend on N1 data points (and
A2,B2,C2 on N2) and therefore intuitively it doesn't seem accurate to use a
Student-t quantile derived from (N1 + N2).  Another way to think of it is if I
constructed a linear model:
  x.1  <- x  * (x <  0)
  x.2  <- x  * (x >= 0)
  x2   <- x^2
  x2.1 <- x2 * (x <  0)
  x2.2 <- x2 * (x >= 0)
  mylm <- lm(y ~ x.1 + x.2 + x2.1 + x2.2) 
some coefficients are dependent on N1 data points (and their noise) and other
coefficients depend on N2 data points (and their noise). (and I don't think
lm would given me an aswer with such bifurcated


Sincerely,


Leif S. Kirschenbaum, Ph.D., PMP, CRE


* In reality I will perform both quadratic and linear fits on N1 and N2, compare
MSEs of the quadratic vs. linear on N1 to determine the best fit for N1 points
and similarly on N2.  The situation is that I am measuring many instances of an
object (~16,000 devices per wafer) whose behavior is not well modelled at room
temperature.  By inspection of ~4,000 plots of device data I see that there is
sometimes a linear dependence and sometimes a dependence with curvature
adequately represented by a quadratic fit.
The zero temperature device behavior is given by ( (y-yoffset)/yscale )^(2/3) +
( (x-xcenter)/xscale )^(2/3) = 1 which is darn hard to fit with linear models
even for perfect devices, which we do not have.  The room temperature device
behavior is so poorly understood that it is only simulated with finite element
models, which does not give me a neat analytic expression such as p^(2/3) +
q^(2/3) = 1, and in any event the plots rarely look anything like x^(2/3)
behavior.

Background:
I am developing a test system in LabVIEW, however I simulate data analysis
offline in R, i.e. acquire raw data on the test system, import raw data into R
and try analysis methods, then port the methods back to the test system.
Therefore use of any R functions more sophisticated than lm will be of limited
use; LabVIEW does not have very sophisticated statistical functions (thank
goodness it includes inverse-t).  I have already developed propagation of
quadratic fit variances and written my own linear fit confidence interval
calculations and now I am extending this to quadratics and to this intersection
problem.