partialOrder

Obtain a partial permutation that rearranges the smallest (largest) elements
of a vector into ascending (descending) order.

utilities

Robust methods for high-dimensional data, in particular linear
model selection techniques based on least angle regression and sparse
regression. Specifically, the package implements robust least angle
regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>),
(robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016;
<doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares
regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).

Andreas Alfons

robustHD

Robust Methods for High-Dimensional Data

Dirk Eddelbuettel

partialOrder function

<dl><dt>x</dt>
<dd>a numeric vector of which to find the order of the smallest or
largest elements.</dd>
<dt>h</dt>
<dd>an integer specifying how many (smallest or largest) elements to
order.</dd>
<dt>decreasing</dt>
<dd>a logical indicating whether the sort order should be
increasing (<code>FALSE</code>; the default) or decreasing (<code>TRUE</code>).</dd></dl>

Arguments

Author

Find partial order of smallest or largest values — partialOrder

<dl>

<dt>x</dt>
<dd>a numeric vector of which to find the order of the smallest or
largest elements.</dd>


<dt>h</dt>
<dd>an integer specifying how many (smallest or largest) elements to
order.</dd>


<dt>decreasing</dt>
<dd>a logical indicating whether the sort order should be
increasing (<code>FALSE</code>; the default) or decreasing (<code>TRUE</code>).</dd>

</dl>

partialOrder: Find partial order of smallest or largest values

Description

Usage

Value

Arguments

Author

See Also

Examples