fdatrain: Learn the training data using functional data analysis.
Description
Use B-spline basis to fit the curves and use functional regression with functional response as the model to learn the data.Usage
fdatrain(data, nbasis = NULL, norder = 6, lambda1 = 1e-07, lambda2 = 1e-05, pen.order = 2)
Arguments
data
Training data. Should be the data after wrap or with certain data.table format.
nbasis
Number of basis functions. Default is the integer of dimension of data over 5.
norder
Number of norder. Default to be 4.
lambda1
Smoothing parameter for curve fitting. Default to be 1e-07
lambda2
Smoothing paramter for functional regression. Default to be 1e-05
pen.order
Order of penalty term. Default to be 2.
Value
- A list of
- betaestlistthe list of coefficients basis
- sigv2fdFunctional object of the variance structure
Details
Use penalized fitting and regression with large number of basis functions to avoid some computational costs.References
Ramsay, James O., and Silverman, Bernard W. (2006), Functional
Data Analysis, 2nd ed., Springer, New York.See Also
create.bspline.basis, fdapred