basisfd: Define a Functional Basis Object


This is the constructor function for objects of the basisfd class. Each function that sets up an object of this class must call this function. This includes functions create.bspline.basis, create.constant.basis, create.fourier.basis, and so forth that set up basis objects of a specific type. Ordinarily, user of the functional data analysis software will not need to call this function directly, but these notes are valuable to understanding what the "slots" or "members" of the basisfd class are.


basisfd(type, rangeval, nbasis, params,
        dropind=vector('list', 0),
        quadvals=vector('list', 0),
        values=vector("list", 0),
        basisvalues=vector('list', 0))



a character string indicating the type of basis. Currently, there are eight possible types:

  1. Bspline, bspline, Bsp, bspb-spline basis

  2. const, con, constantconstant basis

  3. exp, expon, exponen, exponentialexponential basis

  4. Fourier, fourier, Fou, fouFourier basis

  5. mon, monom, monomialmonomial basis

  6. polyg, polygon, polygonalpolygonal basis

  7. power, powpower basis


a vector of length 2 containing the lower and upper boundaries of the range over which the basis is defined


the number of basis functions


a vector of parameter values defining the basis.

If the basis is "fourier", this is a single number indicating the period. That is, the basis functions are periodic on the interval (0,PARAMS) or any translation of it.

If the basis is "bspline", the values are interior points at which the piecewise polynomials join. Note that the number of basis functions NBASIS is equal to the order of the Bspline functions plus the number of interior knots, that is the length of PARAMS. This means that NBASIS must be at least 1 larger than the length of PARAMS.


a vector of integers specifiying the basis functions to be dropped, if any. For example, if it is required that a function be zero at the left boundary, this is achieved by dropping the first basis function, the only one that is nonzero at that point.


a matrix with two columns and a number of rows equal to the number of argument values used to approximate an integral using Simpson's rule. The first column contains these argument values. A minimum of 5 values are required for each inter-knot interval, and that is often enough. These are equally spaced between two adjacent knots. The second column contains the weights used for Simpson's rule. These are proportional to 1, 4, 2, 4, ..., 2, 4, 1.


a list, with entries containing the values of the basis function derivatives starting with 0 and going up to the highest derivative needed. The values correspond to quadrature points in quadvals and it is up to the user to decide whether or not to multiply the derivative values by the square roots of the quadrature weights so as to make numerical integration a simple matrix multiplication. Values are checked against quadvals to ensure the correct number of rows, and against nbasis to ensure the correct number of columns.

values contains values of basis functions and derivatives at quadrature points weighted by square root of quadrature weights. These values are only generated as required, and only if the quadvals is not matrix("numeric",0,0).


a list of lists. This is designed to avoid evaluation of a basis system repeatedly at a set of argument values. Each sublist corresponds to a specific set of argument values, and must have at least two components, which may be named as you wish. The first component in an element of the list vector contains the argument values. The second component is a matrix of values of the basis functions evaluated at the arguments in the first component. Subsequent components, if present, are matrices of values their derivatives up to a maximum derivative order. Whenever function getbasismatrix is called, it checks the first list in each row to see, first, if the number of argument values corresponds to the size of the first dimension, and if this test succeeds, checks that all of the argument values match. This takes time, of course, but is much faster than re-evaluation of the basis system. Even this time can be avoided by direct retrieval of the desired array. For example, you might set up a vector of argument values called "evalargs" along with a matrix of basis function values for these argument values called "basismat". You might want too use tags like "args" and "values", respectively for these. You would then assign them to BASISVALUES with code such as basisobj$basisvalues <- vector("list",1); basisobj$basisvalues[[1]] <- list(args=evalargs, values=basismat).


Previous versions of the 'fda' software used the name basis for this class, and the code in Matlab still does. However, this class name was already used elsewhere in the S languages, and there was a potential for a clash that might produce mysterious and perhaps disastrous consequences.

To check that an object is of this class, use function is.basis.

It is comparatively simple to add new basis types. The code in the following functions needs to be estended to allow for the new type: basisfd, getbasismatrix and getbasispenalty. In addition, a new "create" function should be written for the new type, as well as functions analogous to fourier and fourierpen for evaluating basis functions for basis penalty matrices.

The "create" function names are rather long, and users who mind all that typing might be advised to modify these to versions with shorter names, such as "splbas", "conbas", and etc. However, a principle of good programming practice is to keep the code readable, preferably by somebody other than the programmer.

Normally only developers of new basis types will actually need to use this function, so no examples are provided.


an object of class basisfd, being a list with the following components:


type of basis


acceptable range for the argument


number of bases


a vector of parameter values defining the basis.


input argument dropind


quadrature values ...


a list of basis functions and derivatives


input argument basisvalues


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. (2006), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

See Also

is.basis, is.eqbasis, plot.basisfd, getbasismatrix, getbasispenalty, create.bspline.basis, create.constant.basis, create.exponential.basis, create.fourier.basis, create.monomial.basis, create.polygonal.basis, create.power.basis

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