create.bspline.basis  R Documentation 
Functional data objects are constructed by specifying a set of basis
functions and a set of coefficients defining a linear combination of
these basis functions. The Bspline basis is used for nonperiodic
functions. Bspline basis functions are polynomial segments jointed
endtoend at at argument values called knots, breaks or join points.
The segments have specifiable smoothness across these breaks. Bspline
basis functions have the advantages of very fast computation and great
flexibility. A polygonal basis generated by
create.polygonal.basis
is essentially a Bspline basis of order
2, degree 1. Monomial and polynomial bases can be obtained as linear
transformations of certain Bspline bases.
create.bspline.basis(rangeval=NULL, nbasis=NULL, norder=4,
breaks=NULL, dropind=NULL, quadvals=NULL, values=NULL,
basisvalues=NULL, names="bspl")
rangeval 
a numeric vector of length 2 defining the interval over which the
functional data object can be evaluated; default value is
If If length(rangeval)>2 and neither NOTE: Nonnumerics are also accepted provided

nbasis 
an integer variable specifying the number of basis functions. This
'nbasis' argument is ignored if nbasis = nbreaks + norder  2, where nbreaks = length(breaks). If nbreaks = nbasis  norder + 2, and breaks = seq(rangevals[1], rangevals[2], nbreaks). 
norder 
an integer specifying the order of bsplines, which is one higher than their degree. The default of 4 gives cubic splines. 
breaks 
a vector specifying the break points defining the bspline.
Also called knots, these are a strictly increasing sequence
of junction points between piecewise polynomial segments.
They must satisfy As for rangeval, must satisfy 
dropind 
a vector of integers specifying 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 
values 
a list containing the basis functions and their derivatives
evaluated at the quadrature points contained in the first
column of 
basisvalues 
a vector of lists, allocated by code such as 
names 
either a character vector of the same length as the number of basis
functions or a single character string to which 
Spline functions are constructed by joining polynomials endtoend at argument values called break points or knots. First, the interval is subdivided into a set of adjoining intervals separated the knots. Then a polynomial of order $m$ (degree $m1$) is defined for each interval. To make the resulting piecewise polynomial smooth, two adjoining polynomials are constrained to have their values and all their derivatives up to order $m2$ match at the point where they join.
Consider as an illustration the very common case where the order is 4 for all polynomials, so that degree of each polynomials is 3. That is, the polynomials are cubic. Then at each break point or knot, the values of adjacent polynomials must match, and so also for their first and second derivatives. Only their third derivatives will differ at the point of junction.
The number of degrees of freedom of a cubic spline function of this nature is calculated as follows. First, for the first interval, there are four degrees of freedom. Then, for each additional interval, the polynomial over that interval now has only one degree of freedom because of the requirement for matching values and derivatives. This means that the number of degrees of freedom is the number of interior knots (that is, not counting the lower and upper limits) plus the order of the polynomials:
nbasis = norder + length(breaks)  2
The consistency of the values of nbasis
, norder
and
breaks
is checked, and an error message results if this
equation is not satisfied.
Bsplines are a set of special spline functions that can be used to construct any such piecewise polynomial by computing the appropriate linear combination. They derive their computational convenience from the fact that any Bspline basis function is nonzero over at most m adjacent intervals. The number of basis functions is given by the rule above for the number of degrees of freedom.
The number of intervals controls the flexibility of the spline; the more knots, the more flexible the resulting spline will be. But the position of the knots also plays a role. Where do we position the knots? There is room for judgment here, but two considerations must be kept in mind: (1) you usually want at least one argument value between two adjacent knots, and (2) there should be more knots where the curve needs to have sharp curvatures such as a sharp peak or valley or an abrupt change of level, but only a few knots are required where the curve is changing very slowly.
This function automatically includes norder
replicates of the
end points rangeval. By contrast, the analogous functions
splineDesign and spline.des in the
splines
package do NOT automatically replicate the end points.
To compare answers, the end knots must be replicated manually when
using splineDesign or spline.des.
a basis object of the type bspline
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.
basisfd
,
create.constant.basis
,
create.exponential.basis
,
create.fourier.basis
,
create.monomial.basis
,
create.polygonal.basis
,
create.power.basis
splineDesign
spline.des
##
## The simplest basis currently available with this function:
##
bspl1.1 < create.bspline.basis(norder=1)
oldpar < par(no.readonly=TRUE)
plot(bspl1.1)
# 1 basis function, order 1 = degree 0 = step function:
# should be the same as above:
b1.1 < create.bspline.basis(0:1, nbasis=1, norder=1, breaks=0:1)
all.equal(bspl1.1, b1.1)
bspl2.2 < create.bspline.basis(norder=2)
plot(bspl2.2)
bspl3.3 < create.bspline.basis(norder=3)
plot(bspl3.3)
bspl4.4 < create.bspline.basis()
plot(bspl4.4)
bspl1.2 < create.bspline.basis(norder=1, breaks=c(0,.5, 1))
plot(bspl1.2)
# 2 bases, order 1 = degree 0 = step functions:
# (1) constant 1 between 0 and 0.5 and 0 otherwise
# (2) constant 1 between 0.5 and 1 and 0 otherwise.
bspl2.3 < create.bspline.basis(norder=2, breaks=c(0,.5, 1))
plot(bspl2.3)
# 3 bases: order 2 = degree 1 = linear
# (1) line from (0,1) down to (0.5, 0), 0 after
# (2) line from (0,0) up to (0.5, 1), then down to (1,0)
# (3) 0 to (0.5, 0) then up to (1,1).
bspl3.4 < create.bspline.basis(norder=3, breaks=c(0,.5, 1))
plot(bspl3.4)
# 4 bases: order 3 = degree 2 = parabolas.
# (1) (x.5)^2 from 0 to .5, 0 after
# (2) 2*(x1)^2 from .5 to 1, and a parabola
# from (0,0 to (.5, .5) to match
# (3 & 4) = complements to (2 & 1).
bSpl4. < create.bspline.basis(c(1,1))
plot(bSpl4.)
# Same as bSpl4.23 but over (1,1) rather than (0,1).
# set up the bspline basis for the lip data, using 23 basis functions,
# order 4 (cubic), and equally spaced knots.
# There will be 23  4 = 19 interior knots at 0.05, ..., 0.95
lipbasis < create.bspline.basis(c(0,1), 23)
plot(lipbasis)
bSpl.growth < create.bspline.basis(growth$age)
# cubic spline (order 4)
bSpl.growth6 < create.bspline.basis(growth$age,norder=6)
# quintic spline (order 6)
##
## Nonnumeric rangeval
##
# Date
July4.1776 < as.Date('17760704')
Apr30.1789 < as.Date('17890430')
AmRev < c(July4.1776, Apr30.1789)
BspRevolution < create.bspline.basis(AmRev)
# POSIXct
July4.1776ct < as.POSIXct1970('17760704')
Apr30.1789ct < as.POSIXct1970('17890430')
AmRev.ct < c(July4.1776ct, Apr30.1789ct)
BspRev.ct < create.bspline.basis(AmRev.ct)
par(oldpar)
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