expfit(vx, vy, [vg]) Returns a vector
containing three coefficients for an exponential curve of the form A · exp(b·x) + C that best approximates the data in vx and vy. The expfit function
employs the Levenberg-Marquardt method for minimization.
Arguments:
vx and vy are vectors of real data
values of the same length, corresponding to the x-
and y-values in the data set. The x-values must be
≥ 0. There must be at least 3 data points.
vg (optional) is
a three-element vector of real guess values for the parameters A,
b, and C in the exponential
equation. If this argument is not used, then expfit generates a
guess fitting a line to the logs of vy.
Notes:
To further analyze your data, or determine the suitability of a linear regression,
you may wish to apply other statistics
functions for data analysis.
If you wish to do an exponential fit that differs from the form above, use
genfit.
If you wish to perform a power fit on data that has negative x-values, you
must shift your data to the positive axis. Exponential fits on negative-valued
x data can produce an imaginary-valued fitting function.