Batchmaturity

Purpose

Batch process model and monitoring.

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.

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