By Douglas B. Clarkson, Chris Fraley, Charles C. Gu, James O. Ramsey (auth.)
S+Functional facts research is the 1st advertisement item orientated package deal for exploring, modeling, and reading practical info. useful information research (FDA) handles longitudinal facts and treats every one statement as a functionality of time (or different variable). The features are comparable. The aim is to research a pattern of capabilities rather than a pattern of comparable issues.
FDA differs from conventional info analytic innovations in a couple of methods. services should be evaluated at any aspect of their area. Derivatives and integrals, that may offer larger info (e.g. graphical) than the unique facts, are simply computed and utilized in multivariate and different useful analytic methods.
The analyst utilizing S+FDA can deal with irregularly spaced information or info with lacking values. for big quantities of information, operating with a sensible illustration can keep garage. furthermore, S+FDA offers a number of analytic strategies for useful info together with linear types, generalized linear versions, crucial parts, canonical correlation, central differential research, and clustering.
This ebook should be thought of a better half to 2 different hugely acclaimed books related to James Ramsay and Bernard Silverman: sensible info research, moment version (2005) and utilized practical information research (2002). This user's guide additionally offers the documentation for the S+FDA library for SPlus.
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Additional info for S+ Functional Data Analysis: User’s Manual for Windows®
Sample text
For example, to create a product basis consisting of a Fourier and a Bspline univariate basis: > basis1 <- FourierBasis(fDomain=c(0,365), nbasis=11) > basis2 <- bsplineBasis(fDomain=c(0,52), nbasis=20) > basisProd <- fProdBasis(basis1, basis2) The result is an object of class fProdBasis, which contains as two components the univariate bases: > names(basisProd) [1] "basis1" "basis2" Finite Element Bases An S-PLUS constructor function fFinElemBasis creates finite element basis functions for bivariate functional data.
Arithmetic Operators It is also possible to apply the operators “+”, “-”, “*”, “/”, “sqrt”, “^” to the functions (the exponentiation operator, “^” is restricted to constant values). 3. 3: Difference between pinch force function objects created with a B-spline and a polygonal basis. The differences between the two functions are small relative to the observed function values, which have a maximum magnitude near 8. Vectors of Functional Data Objects An object of class fFunction is the functional equivalent of a scalar.
The following section gives more detail on the basis functions available in S+FDA. The description is largely non-mathematical, but is hopefully sufficient to enable you to choose your own basis. The simplest bases are those of class constantBasis. The basis functions in this class are constant (and equal to one) over their entire domain. There is only one argument to the constructor, the domain of the function. 1. 1: Constant basis function over the domain (0,10). 29 Chapter 2 Basis Objects and Operations The constantBasis constructor is surprisingly useful.