Batchfold

Batchfold

Purpose

Transform batch data into dataset for analysis.

Synopsis

bdata = batchfold(method,data,options);

[bdata,model] = batchfold(method,data,options);

bdata = batchfold(data,model);

Description

Based on 'method' type, fold/unfold data into suitable dataset for analysis. Data is separated both by batch (high-level experiments) and also optionally by step number (sub-divisions of batch indicating processing segments or other division of batches). Identification of batch and step for each sample must be in .class field. Assumes incoming data is a two-way matrix consisting of samples by variables.ngs.

Inputs

• method = Method type from table below.
• data = Dataset object, 2D samples by variables with all batch and step information in the .class field.

Outputs

• bdata = DataSet Object suitable for loading into 'analysis' interface for given 'method'.
• model = Standard model structure containing the batchfold model (See MODELSTRUCT). NOTE: Care must be taken to assure fields designated in the calibration set also exist in test set or application of model will fail.

Options

options = a structure array with the following fields:

• plots: [ {'none'} | 'final' ] governs plotting of results, and
• order: positive integer for polynomial order {default = 1}.

• batch_source : [{'class'}|'label'|'axisscale'] Field name of source for batch info. Use 'variable' if selecting a column of data.
• batch_set : ['BSPC Batch'] Identifies set to use for identifying sample batches. Either a set name (string) or class set number of set to use.
• batch_locate : {'index'|{'gap'}|'backstep'} How to use variable or axisscale to define steps.

index - boundry at straight index (1 1 1 2 2 3 3 3).

gap - boundry at gaps in data (1 2 3 4 7 8 9 20 21 22).

backstep - boundry at resets (1 2 3 1 2 3 4 1 2).

NOTE: At this point gap and backstep use the same algorithm.

• step_source : ['class'] Field name of source for step info. Use 'variable' if selecting a column of data. If empty then no steps is assumed.
• step_set : ['BSPC Step'] Identifies set to use for identifying sample steps. Either a setname (string) or class set number of set to use.
• step_selection_classes : [] Step numbers (as defined by the step_set) to include in analysis. Empty implies all values are steps. NOTE: This is not an index into the .class field but the actual numeric class values.
• batch_align_options : [struct] Options for 'batchalign' function. See batchalign for more information.
• alignment_batch_class : Numeric class of batch to use as reference for alignment or vector of target.
• alignment_variable_index : Index of variable (columns) in batch to use for alignment.
• summary : {} Type of summary statistics to calculate for each variable and step (as a cell array of stings). This is only used for spca and sparafac methods.

mean - Mean

std - Standard Deviation

min - Minimum

max- Maximum

range - Range

slope - Slope

length - Length (of step)

percentile - 10 25 50 75 90 percentile.

• data_only : [{0} | 1 | 2] Only return data:

0 - Run entire function

1 - Make classes for data.

2 - Make classes and align data.

See Also

batchalign, batchdigester, batchmaturity