fda (version 2.2.7)

fda-package: Functional Data Analysis in R

Description

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.

Details

ll{ Package: fda Type: Package Version: 2.2.6 Date: 2011-02-03 License: GPL-2 LazyLoad: yes }

References

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.

Examples

##
## 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