rotateScale3D

Render a 3-Dimensional projection matrix given positive or
 negative degree changes in yaw, pitch, and / or roll and increment or
 decrement feature scales.

Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
data generated from a continuous-time multivariate industrial or natural
process. Employ PCA-based dimension reduction to extract linear combinations
of relevant features, reducing computational burdens. For a description of
ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
The multi-state application of ADPCA is from a manuscript under current
revision entitled "Multi-State Multivariate Statistical Process Control" by
Odom, Newhart, Cath, and Hering, and is expected to appear in Q1 of 2018.

Gabriel Odom

mvMonitoring

Multi-State Adaptive Dynamic Principal Component Analysis for
Multivariate Process Monitoring

Melissa Innerst

Ben Barnard

Karen Kazor

Amanda Hering

rotateScale3D function

<dl><dt>rot_angles</dt>
<dd>a list or vector containing the rotation angles in the
order following: yaw, pitch, roll. Defaults to &lt;0,0,0&gt;.</dd>
<dt>scale_factors</dt>
<dd>a list or vector containing the values by which to
multiply each dimension. Defaults to &lt;1,1,1&gt;.</dd></dl>

Arguments

Three-Dimensional Rotation and Scaling Matrix — rotateScale3D

<dl>

<dt>rot_angles</dt>
<dd>a list or vector containing the rotation angles in the
order following: yaw, pitch, roll. Defaults to &lt;0,0,0&gt;.</dd>


<dt>scale_factors</dt>
<dd>a list or vector containing the values by which to
multiply each dimension. Defaults to &lt;1,1,1&gt;.</dd>

</dl>

rotateScale3D: Three-Dimensional Rotation and Scaling Matrix

Description

Usage

Value

Arguments

Details

See Also

Examples