Given the data in arg, expand them nonlinearly in the same way as it was
done in the SFA-object sfaList (expanded dimension M) and search the vector
RCOEF of M constant coefficients, such that the sum of squared residuals
between a given function in time FUNC and the function
R(t) = (v(t) - v0)' * RCOEF, t=1,...,T,
is minimal
Usage
sfaNlRegress(sfaList, arg, func)
Arguments
sfaList
A list that contains all information about the handled sfa-structure
arg
Input data, each column a different variable
func
(T x 1) the function to be fitted nonlinearly
Value
returns a list res with elements
res$R
(T x 1) the function fitted by NL-regression
res$rcoef
(M x 1) the coefficients for the NL-expanded dimensions