Description
Usage
Arguments
Value
Uses parallelMap
to parallelize the computation of the distance
matrix. This is done by dividing the data into batches and computing
the distance matrix for each batch.
For details on distance computation see computeDistMat
.
 (x, y = , method = "Euclidean", batches = 1L,
)

x 
[matrix ]
matrix containing the functional observations as rows.

y 
[matrix ]
see x . The default NULL uses y = x .

method 
[character(1) ]
character string describing the distance function to be used. For a full list
execute metricChoices() .
Euclidean equals Lp with p = 2 . This is the default.
Lp, Minkowski the distance for an Lpspace, takes p as
an additional argument in ... .
Manhattan equals Lp with p = 1 .
supremum, max, maximum equals Lp with p = Inf .
The supremal pointwise difference between the curves.
and ... all other available measures for dist .
shortEuclidean Euclidean distance on a limited part of the domain.
Additional arguments dmin and dmax can be specified in
... , giving
the position of the first and the last point to use of an evenly spaced
sequence from 0 to 1 of length length(grid) .
The default values are dmin = o and dmax = 1 ,
which results in the Euclidean distance on the entire domain.
mean the absolute similarity of the overall mean values of
the observations.
relAreas the difference of the relation of two areas on parts
of the domain given by dmin1 to dmax1 and dmin2 to
dmax2 . They are defined analogously to dmin and dmax
and take the same default values.
jump the similarity of jump heights at points t1 and t2 ,
i.e. x[t1 * length(x)]  x[t2 * length(x)] for every functional observation x .
The points t1 and t2 are the positions in an evenly spaced sequence
from 0 to 1 of length length(grid) for which to compare the
jump height. The default values are t1 = 0 and t2 = 1 .
globMax the difference of the curves global maxima.
globMin the difference of the curves global minima.
points the mean absolute differences at certain observation
points .poi , also called "points of impact". These are specified as
a vector .poi of arbitrary length with values between 0
and 1 , encoding the the index of the points of observations.
The default value is .poi = seq(0, 1, length.out = length(grid)) , which results in the Manhattan
distance.
custom.metric your own semimetric will be used. Specify your
own distance function in the argument custom.metric .
amplitudeDistance,phaseDistance The amplitude distance or
phase distance as described in
Srivastava, A. and E. P. Klassen (2016). Functional and Shape Data Analysis. Springer.
FisherRao, elasticMetric the elastic distance of the square
root velocity of the curves as described in Srivastava and Klassen (2016).
This equates to the Fisher Rao metric.
elasticDistance weighted mean of the amplitude and the phase
distance using the implementation in elastic.distance .
Additional arguments are the numeric the penalization parameters a,b,c
for the amplitude distance (a^2 ) and the phase distance (b^2 ).
The default values are a = 1/2, b = 1 .
Alternatively c denotes the ratio of 2*a and b .
lambda is the additional penalization parameter for the warping
allowed before calculating the elastic distance. The default is 1.
rucrdtw, rucred Dynamic Time Warping Distance and Euclidean Distance
from package rucrdtw . Implemented in BoerschSupan (2016) and
originally described in Rakthanmanon et al. (2012).

batches 
[integer(1) ]
Number of roughly equalsized batches to split data into. The distance computation is then carried out
for each batch.

... 
additional parameters to the (semi)metrics.

a matrix of dimensions nrow(x)
by nrow(y)
containing the
distances of the functional observations contained in x
and y
,
if y
is specified. Otherwise a matrix containing the distances of all
functional observations within x
to each other.
classiFunc documentation built on May 2, 2019, 2:04 a.m.