create.polynomial.basis: Create a Polynomial Basis
In drtagkim/mcgillfdar: Functional Data Analysis

Description Usage Arguments Details Value See Also Examples

Creates a set of basis functions consisting of powers of the argument shifted by a constant.

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create.polynomial.basis(rangeval=c(0, 1), nbasis=2, ctr=0,
                   dropind=NULL, quadvals=NULL, values=NULL,
                   basisvalues=NULL, names='polynom', axes=NULL)

`rangeval`	a vector of length 2 defining the range.
`nbasis`	the number of basis functions. The default is 2, which defines a basis for straight lines.
`ctr`	this value is used to shift the argument prior to taking its power.
`dropind`	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.
`quadvals`	a matrix with two columns and a number of rows equal to the number of quadrature points for numerical evaluation of the penalty integral. The first column of `quadvals` contains the quadrature points, and the second column the quadrature weights. A minimum of 5 values are required for each inter-knot interval, and that is often enough. For Simpson's rule, these points are equally spaced, and the weights are proportional to 1, 4, 2, 4, ..., 2, 4, 1.
`values`	a list of matrices with one row for each row of `quadvals` and one column for each basis function. The elements of the list correspond to the basis functions and their derivatives evaluated at the quadrature points contained in the first column of `quadvals`.
`basisvalues`	A list of lists, allocated by code such as vector("list",1). This field is designed to avoid evaluation of a basis system repeatedly at a set of argument values. Each list within the vector corresponds to a specific set of argument values, and must have at least two components, which may be tagged as you wish. 'The first component in an element of the list vector contains the argument values. The second component in an element of the list vector contains a matrix of values of the basis functions evaluated at the arguments in the first component. The third and subsequent components, if present, contain 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 names like "args" and "values", respectively for these. You would then assign them to `basisvalues` with code such as the following: basisobj\$basisvalues <- vector("list",1) basisobj\$basisvalues[[1]] <- list(args=evalargs, values=basismat)
`names`	either a character vector of the same length as the number of basis functions or a simple stem used to construct such a vector. For `polynom` bases, this defaults to paste('polynom', 1:nbreaks, sep=”).
`axes`	an optional list used by selected `plot` functions to create custom `axes`. If this `axes` argument is not `NULL`, functions `plot.basisfd`, `plot.fd`, `plot.fdSmooth` `plotfit.fd`, `plotfit.fdSmooth`, and `plot.Lfd` will create axes via `do.call(x$axes[[1]], x$axes[-1])`. The primary example of this uses `list("axesIntervals", ...)`, e.g., with `Fourier` bases to create `CanadianWeather` plots

The only difference between a monomial and a polynomial basis is the use of a shift value. This helps to avoid rounding error when the argument values are a long way from zero.

a basis object with the type polynom.

basisfd, create.basis, create.bspline.basis, create.constant.basis, create.fourier.basis, create.exponential.basis, create.monomial.basis, create.polygonal.basis, create.power.basis

#  Create a polynomial basis over the years in the 20th century
#  and center the basis functions on 1950.
basisobj <- create.polynomial.basis(c(1900, 2000), nbasis=3, ctr=1950)
#  plot the basis
# The following should work but doesn't;  2007.05.01
#plot(basisobj)