fda: smooth.fdPar – R documentation

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smooth.fdPar

Smooth a functional data object using a directly specified roughness penalty

Smooth data already converted to a functional data object, fdobj, using directly specified criteria.

smooth.fdPar(fdobj, Lfdobj=NULL, lambda=1e-4,
             estimate=TRUE, penmat=NULL)

`fdobj`	a functional data object to be smoothed.
`Lfdobj`	either a nonnegative integer or a linear differential operator object. If `NULL`, Lfdobj depends on fdobj[['basis']][['type']]: bspline Lfdobj <- int2Lfd(max(0, norder-2)), where norder = norder(fdobj). fourier Lfdobj = a harmonic acceleration operator: `Lfdobj <- vec2Lfd(c(0,(2*pi/diff(rng))^2,0), rng)` where rng = fdobj[['basis']][['rangeval']]. anything elseLfdobj <- int2Lfd(0)
`lambda`	a nonnegative real number specifying the amount of smoothing to be applied to the estimated functional parameter.
`estimate`	a logical value: if `TRUE`, the functional parameter is estimated, otherwise, it is held fixed.
`penmat`	a roughness penalty matrix. Including this can eliminate the need to compute this matrix over and over again in some types of calculations.

1. fdPar

2. smooth.fd

a functional data object.

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.

#  see smooth.basis

Functional Data Analysis

v5.1.9

GPL (>= 2)

Authors

J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]

Initial release

2020-12-16