Description Details Author(s) References Examples

Functions and data sets companion to Ramsay, J. O.; Hooker, Giles; and Graves, Spencer (2010) Functional Data Analysis with R and Matlab, plus Ramsay, J. O., and Silverman, B. W. (2006) Functional Data Analysis, 2nd ed. and (2002) Applied Functional Data Analysis (Springer). This includes finite bases approximations (such as splines and Fourier series) to functions fit to data smoothing on the integral of the squared deviations from an arbitrary differential operator.

Package: | fda |

Type: | Package |

Version: | 2.2.6 |

Date: | 2011-02-03 |

License: | GPL-2 |

LazyLoad: | yes |

J. O. Ramsay, <[email protected]>, Hadley Wickham <[email protected]>, Spencer Graves <[email protected]>, Giles Hooker <[email protected]>

Maintainer: J. O. Ramsay <[email protected]>

Ramsay, J. O.; Hooker, Giles; and Graves, Spencer (2010)
*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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ```
##
## As noted in the Preface to Ramsay, Hooker and Graves (p. v),
## the fda package includes scripts to reproduce all but one of the
## figures in the book.
##
## These figures can be found and run as follows:
##
## Not run:
scriptsDir <- system.file('scripts', package='fda')
Rscripts <- dir(scriptsDir, full.names=TRUE, pattern='R$')
fdarm <- grep('fdarm', Rscripts, value=TRUE)
chapters <- length(fdarm)
# NOTE: If R fails in any of these scripts,
# this for loop will not end normally,
# and the abnormal termination will be displayed:
for(ch in 1:chapters){
cat('Running', fdarm[ch], '\n')
invisible(source(fdarm[ch]))
}
## End(Not run)
##
## Simple smoothing
##
girlGrowthSm <- with(growth, smooth.basisPar(argvals=age, y=hgtf, lambda=0.1))
plot(girlGrowthSm$fd, xlab="age", ylab="height (cm)",
main="Girls in Berkeley Growth Study" )
plot(deriv(girlGrowthSm$fd), xlab="age", ylab="growth rate (cm / year)",
main="Girls in Berkeley Growth Study" )
plot(deriv(girlGrowthSm$fd, 2), xlab="age",
ylab="growth acceleration (cm / year^2)",
main="Girls in Berkeley Growth Study" )
##
## Simple basis
##
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.
fd1.2 <- Data2fd(0:1, basisobj=bspl1.2)
op <- par(mfrow=c(2,1))
plot(bspl1.2, main='bases')
plot(fd1.2, main='fit')
par(op)
# A step function: 0 to time=0.5, then 1 after
``` |

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