partial_forder

combine_forder

If the functional data doesn't comfortably fit in memory it is possible to
compute functional ordering by splitting the domain of the data (voxels in
a brain image), using <code>partial_forder</code> on each part and finally
combining the results with <code>combine_forder</code>.

Implementation of global envelopes for a set of general d-dimensional vectors T
in various applications. A 100(1-alpha)% global envelope is a band bounded by two
vectors such that the probability that T falls outside this envelope in any of the d
points is equal to alpha. Global means that the probability is controlled simultaneously
for all the d elements of the vectors. The global envelopes can be used for graphical
Monte Carlo and permutation tests where the test statistic is a multivariate vector or
function (e.g. goodness-of-fit testing for point patterns and random sets, functional
analysis of variance, functional general linear model, n-sample test of correspondence
of distribution functions), for central regions of functional or multivariate data (e.g.
outlier detection, functional boxplot) and for global confidence and prediction bands
(e.g. confidence band in polynomial regression, Bayesian posterior prediction). See
Myllymäki and Mrkvička (2024) <doi:10.18637/jss.v111.i03>,
Myllymäki et al. (2017) <doi:10.1111/rssb.12172>,
Mrkvička and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>,
Mrkvička et al. (2016) <doi:10.1016/j.spasta.2016.04.005>,
Mrkvička et al. (2017) <doi:10.1007/s11222-016-9683-9>,
Mrkvička et al. (2020) <doi:10.14736/kyb-2020-3-0432>,
Mrkvička et al. (2021) <doi:10.1007/s11009-019-09756-y>,
Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>,
Mrkvička et al. (2022) <doi:10.1002/sim.9236>,
Dai et al. (2022) <doi:10.5772/intechopen.100124>,
Dvořák and Mrkvička (2022) <doi:10.1007/s00180-021-01134-y>,
Mrkvička et al. (2023) <doi:10.48550/arXiv.2309.04746>, and
Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.

Mari Myllym<c3><a4>ki

Global Envelopes

Mari Myllymäki

Tomáš Mrkvička

Mikko Kuronen

Jiří Dvořák

Pavel Grabarnik

Ute Hahn

Michael Rost

Henri Seijo

partial_forder function

<dl><dt>curve_set</dt>
<dd>A <code>curve_set</code> object, usually a part of a larger <code>curve_set</code>.
(No missing or infinite values allowed.)</dd>
<dt>measure</dt>
<dd>The measure to use to order the functions from the most extreme to the least extreme
one. Must be one of the following: 'rank', 'erl', 'cont', 'area', 'max', 'int', 'int2'. Default is 'erl'.</dd>
<dt>alternative</dt>
<dd>A character string specifying the alternative hypothesis.
Must be one of the following: "two.sided" (default), "less" or "greater".
The last two options only available for types <code>'rank'</code>, <code>'erl'</code>,
<code>'cont'</code> and <code>'area'</code>.</dd>
<dt>ls</dt>
<dd>List of objects returned by partial_forder</dd></dl>

Arguments

Functional ordering in parts — partial_forder

<dl>

<dt>curve_set</dt>
<dd>A <code>curve_set</code> object, usually a part of a larger <code>curve_set</code>.
(No missing or infinite values allowed.)</dd>


<dt>measure</dt>
<dd>The measure to use to order the functions from the most extreme to the least extreme
one. Must be one of the following: 'rank', 'erl', 'cont', 'area', 'max', 'int', 'int2'. Default is 'erl'.</dd>


<dt>alternative</dt>
<dd>A character string specifying the alternative hypothesis.
Must be one of the following: "two.sided" (default), "less" or "greater".
The last two options only available for types <code>'rank'</code>, <code>'erl'</code>,
<code>'cont'</code> and <code>'area'</code>.</dd>


<dt>ls</dt>
<dd>List of objects returned by partial_forder</dd>

</dl>

Functional ordering in parts

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partial_forder: Functional ordering in parts

Description

Usage

Value

Arguments

See Also

Examples