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
sfaNlRegress(sfaList, arg, func)A list that contains all information about the handled sfa-structure
Input data, each column a different variable
(T x 1) the function to be fitted nonlinearly
returns a list res with elements
(T x 1) the function fitted by NL-regression
(M x 1) the coefficients for the NL-expanded dimensions