oneDay_clean

<p>Data from the SM-MBR Bioreactor system over 12 hours. This data
  will be used for testing the mvMonitoring package.</p>

datasets

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

oneDay_clean function

<p>An xts matrix of 75 rows and 35 features recorded over
  2017-01-27 at 00:10 to 2017-01-27 at 12:30.</p>

Format

Real Process Data for Testing — oneDay_clean

Real Process Data for Testing

oneDay_clean: Real Process Data for Testing

Description

Usage

Arguments

Format