Batchmaturity

From Eigenvector Research Documentation Wiki
Revision as of 14:11, 1 October 2012 by imported>Donal (→‎Purpose)
Jump to navigation Jump to search

Purpose

Batch process model and monitoring, identifying outliers.

Synopsis

model = batchmaturity(x,ncomp_pca,options);
model = batchmaturity(x,y,ncomp_pca,options);
model = batchmaturity(x,y,ncomp_pca,ncomp_reg,options);
pred = batchmaturity(x,model,options);
pred = batchmaturity(x,model);

Description

Batch process model and monitoring.

Inputs

  • x = X-block (2-way array class "double" or "dataset").
  • y = Y-block (vector class "double" or "dataset").
  • ncomp_pca = Number of components to to be calculated in PCA model (positive integer scalar).
  • ncomp_reg = Number of latent variables for regression method.

Outputs

  • model = standard model structure containing the PCA and Regression model (See MODELSTRUCT).
  • pred = prediction structure contains the scores from PCA model for the input test data as pred.t.

Model and pred contain the following fields which relate to score limits and whether samples are within normal ranges or not:

limits : struct with fields:
cl: value used for cl option
bm: (1 x bmlookuppts) bm values for score limits
low: (nPC x bmlookuppts) lower score limit of inliers
cl: (nPC x bmlookuppts) upper score limit of inliers
inlimits : (nsample x nPC) logical indicating if samples are inliers.
t : (nsample x nPC) scores
t_reduced : (nsample x nPC) scores scaled by limits, with limits -> +/- 1
submodelreg : regression model built to predict bm. Only PLS currently.
submodelpca : PCA model used to calculate X-block scores.

Options

options = a structure array with the following fields:

  • regression_method : [ {'pls'} ] A string indicating type of regression method to use. Currently, only 'pls' is supported.
  • preprocessing : { [] } preprocessing structure goes to both PCA and PLS. PLS Y-block preprocessing will always be autoscale.
  • zerooffsety : [ 0 | {1}] transform y resetting to zero per batch
  • stretchy : [ 0 | {1}] transform y to have range=100 per batch
  • cl : [ 0.90 ] Confidence limit (2-sided) for moving limits (defined as 1 - Expected fraction of outliers.)
  • nearestpts : [{25}] number nearby scores used in getting limits
  • nsmooth : [{25}] number points (odd) used in savgol smoothing
  • bmlookuppts : [{1001}] number of equi-spaced points in bm lookup table
  • plots : [ 'none' | 'detailed' | {'final'} ] governs production of plots when model is built. 'final' shows standard scores and loadings plots. 'detailed' gives individual scores plots with limits for all PCs.
  • waitbar : [ 'off' | {'auto'} ] governs display of waitbar when calculating confidence limits ('auto' shows waitbar only when the calculation will take longer than 15 seconds)

See Also

batchfold, batchdigester