Description Usage Arguments Details Value Author(s) References See Also Examples
Produce the diagnostic plot based on the fist or second order extended integrated / infimal depths.
1 2 3 
datafA 
A single function whose depth is computed, represented by a

datafB 
Functional dataset with respect to which the depth of 
range 
The common range of the domain where the functions 
d 
Grid size to which all the functional data are transformed. For depth computation,
all functional observations are first transformed into vectors of their functional values of length 
order 
The order of the depth to be used in the plot, for 
method 
The depth that is used in the diagnostic plot. possible values are 
approx 
For 
title 
The title of the diagnostic plot. 
nfun 
For 
plot 
Logical: should the function by plotted? 
Plots a diagnostic plot of pointwise univariate (or bivariate) depths for all possible points (or couples of points) from the domain of the
functional data. From such a plot it is possible to infer into the first order (or second order) properties of a single function x with respect
to the given set of functional data. For order=1
, the integral of the displayed function is the integrated depth of x,
the smallest value of the function is the infimal depth of x.
For order=2
, the bivariate integral of the displayed surface gives the second order extended
integrated depth of x, the infimum of this bivariate function gives the second order infimal depth of x.
For details see Nagy et al. (2016) and depthf.fd1
.
For order=1
two depth values, and two vectors of pointwise depths:
Simpl_FD
the first order integrated depth based on the simplicial depth,
Half_FD
the first order integrated depth based on the halfspace depth,
Simpl_ID
the first order infimal depth based on the simplicial depth,
Half_ID
the first order infimal depth based on the halfspace depth,
PSD
the vector of length d
containing the computed
pointwise univariate simplicial depths used for the computation of Simpl_FD
and Simpl_ID
,
PHD
the vector of length d
containing the computed
pointwise univariate halfspace depths used for the computation of Half_FD
and Half_ID
.
In addition, the first order integrated / infimal depth diagnostic plot of the function A
with respect to
the random sample given by the functions corresponding to the rows of the matrix B
is produced.
For order=2
four depth values, and two matrices of pointwise depths:
Simpl_FD
the second order integrated depth based on the simplicial depth,
Half_FD
the second order integrated depth based on the halfspace depth,
Simpl_ID
the second order infimal depth based on the simplicial depth,
Half_ID
the second order infimal depth based on the halfspace depth,
PSD
the matrix of size d*d
containing the computed
pointwise bivariate simplicial depths used for the computation of Simpl_FD
and Simpl_ID
,
PHD
the matrix of size d*d
containing the computed
pointwise bivariate halfspace depths used for the computation of Half_FD
and Half_ID
.
In addition, the second order integrated / infimal depth diagnostic plot of the function A
with respect to
the random sample given by the functions corresponding to the rows of the matrix B
is produced.
Stanislav Nagy, nagy at karlin.mff.cuni.cz
Nagy, S., Gijbels, I. and Hlubinka, D. (2017). Depthbased recognition of shape outlying functions. Journal of Computational and Graphical Statistics, 26 (4), 883–893.
1 2 3 4  datafA = dataf.population()$dataf[1]
dataf = dataf.population()$dataf[2:20]
shape.fd.analysis(datafA,dataf,order=1)
shape.fd.analysis(datafA,dataf,order=2,approx=0)

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