Evaluate the error sum of squares, its gradient and its hessian for the fit of
surprisal curves to binned psychometric data. The function value is optimized
by function smooth.surp in package TestGardener.
surp.fit(x, surpList)A named list object for the returned objects with these names:
The error sum of squares associated with parameter value x.
A column vector containing gradient of the error sum of squares.
A square matrix of hessian values.
The parameter vector, which is the vectorized form of the K by M-1 coefficient matrix for the functional data object.
A named list object containing objects essential to evaluating the fitting
criterion. See smooth.surp.R for the composition of this list.
Juan Li and James Ramsay
Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.
smooth.surp