View source: R/getbasismatrix.R
getbasismatrix | R Documentation |
Evaluate a set of basis functions or their derivatives at a set of argument values.
getbasismatrix(evalarg, basisobj, nderiv=0, returnMatrix=FALSE)
evalarg |
a vector of arguments values. |
basisobj |
a basis object. |
nderiv |
a nonnegative integer specifying the derivative to be evaluated. |
returnMatrix |
logical: If TRUE, a two-dimensional is returned using a special class from the Matrix package. |
a matrix of basis function or derivative values. Rows correspond to argument values and columns to basis functions.
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.
eval.fd
##
## Minimal example: a B-spline of order 1, i.e., a step function
## with 0 interior knots:
##
bspl1.1 <- create.bspline.basis(norder=1, breaks=0:1)
m <- getbasismatrix(seq(0, 1, .2), bspl1.1)
# check
m. <- matrix(rep(1, 6), 6,
dimnames=list(NULL, 'bspl') )
all.equal(m, m.)
##
## Date and POSIXct
##
# Date
July4.1776 <- as.Date('1776-07-04')
Apr30.1789 <- as.Date('1789-04-30')
AmRev <- c(July4.1776, Apr30.1789)
BspRevolution <- create.bspline.basis(AmRev)
AmRevYears <- as.numeric(seq(July4.1776, Apr30.1789, length.out=14))
AmRevMatrix <- getbasismatrix(AmRevYears, BspRevolution)
matplot(AmRevYears, AmRevMatrix, type='b')
# POSIXct
AmRev.ct <- as.POSIXct1970(c('1776-07-04', '1789-04-30'))
BspRev.ct <- create.bspline.basis(AmRev.ct)
AmRevYrs.ct <- as.numeric(seq(AmRev.ct[1], AmRev.ct[2], length.out=14))
AmRevMat.ct <- getbasismatrix(AmRevYrs.ct, BspRev.ct)
matplot(AmRevYrs.ct, AmRevMat.ct, type='b')
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