Estimation of the First Two Derivatives for Functional Data
Returns the estimated values of derivatives of functional data.
derivatives.est(dataf, range = NULL, d = 101, spar = NULL, deriv = c(0,1))
dataf |
Functional dataset, represented by a |
range |
The common range of the domain where the functions |
d |
Grid size to which all the functional data are transformed. For computation,
all functional observations are first transformed into vectors of their functional values of length |
spar |
If provided, this parameter is passed to functions |
deriv |
A vector composed of |
If the input dataf is a functional random sample of size m,
the function returns a dataf object of nd-dimensional functional data, where
in the elements of the vector-valued functional data represent the estimated values of the
derivatives of dataf. All derivatives are evaluated at an equi-distant grid of d
points in the domain given by range. nd here stands for 1, 2 or 3,
depending on how many derivatives of dataf are
requested to be computed. For the estimation, functions D1ss and D2ss from the package
sfsmisc are utilized.
A multivariate dataf object of the functional values and / or the derivatives of dataf.
The dimensionality of the vector-valued functional data is nd. The arguments of the data are all equal to
an equi-distant grid of d points in the domain given by range. nd is the demanded number
of derivatives at the output, i.e. the length of the vector deriv.
Stanislav Nagy, nagy at karlin.mff.cuni.cz
D1ss in package sfsmisc
D2ss in package sfsmisc
dataf = dataf.population()$dataf derivatives.est(dataf,deriv=c(0,1,2))
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