Batchfold: Difference between revisions
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imported>Scott (Created page with "===Purpose=== Transform batch data into dataset for analysis. ===Synopsis=== : bdata = batchfold(method,data,options); : [bdata,model] = batchfold(method,data,option...") |
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===See Also=== | ===See Also=== | ||
[[batchalign]], [[batchdigester]], [[batchmaturity]] | [[batchalign]], [[batchdigester]], [[batchmaturity]], [[bspcgui]] |
Latest revision as of 11:22, 6 September 2017
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.