ppi_mean

Simulate and Summarize Projection Pursuit Index (PPI) Values

Projection pursuit is used to find interesting low-dimensional
projections of high-dimensional data by optimizing an index over all
possible projections. The 'spinebil' package contains methods to evaluate
the performance of projection pursuit index functions using tour methods.
A paper describing the methods can be found at <doi:10.1007/s00180-020-00954-8>.

Tina Rashid Jafari

spinebil

Investigating New Projection Pursuit Index Functions

Ursula Laa

Dianne Cook

ppi_mean function

<dl><dt>data</dt>
<dd>A data frame or matrix. Must have at least two columns.</dd>
<dt>index_fun</dt>
<dd>A function taking two numeric vectors (<code>x</code>, <code>y</code>) and returning a scalar index.</dd>
<dt>n_sim</dt>
<dd>Integer. Number of simulations. Default is 100.</dd>
<dt>n_obs</dt>
<dd>Integer. Number of observations to sample in each simulation. Default is 300.</dd></dl>

Arguments

Simulate and Summarize Projection Pursuit Index (PPI) Values — ppi_mean

<dl>

<dt>data</dt>
<dd>A data frame or matrix. Must have at least two columns.</dd>


<dt>index_fun</dt>
<dd>A function taking two numeric vectors (<code>x</code>, <code>y</code>) and returning a scalar index.</dd>


<dt>n_sim</dt>
<dd>Integer. Number of simulations. Default is 100.</dd>


<dt>n_obs</dt>
<dd>Integer. Number of observations to sample in each simulation. Default is 300.</dd>

</dl>

ppi_mean: Simulate and Summarize Projection Pursuit Index (PPI) Values

Description

Usage

Value

Arguments

Examples