Simcasub: Difference between revisions
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imported>Lyle (Created page with " == Purpose == simcasub calculates a single SIMCA Sub-model. == Synopsis == :model = simcasub(x,modelclasses,''ncomp'',''options''); %identifies model (calibration) ==...") |
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== Purpose == | ===Purpose=== | ||
simcasub calculates a single SIMCA Sub-model. | simcasub calculates a single SIMCA Sub-model. | ||
== Synopsis == | ===Synopsis=== | ||
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:model = simcasub(x,modelclasses,''ncomp'',''options''); %identifies model (calibration) | :model = simcasub(x,modelclasses,''ncomp'',''options''); %identifies model (calibration) | ||
== Description == | ===Description=== | ||
simcasub calculates a single SIMCA sub-model for inclusion into a SIMCA model. A SIMCA sub-model is a PCA model built only on samples of a given class (or classes) as identified in a DataSet object's class{1} field. Inputs are identical to PCA except a second input (modelclasses) follows the x-block specifying which class or classes on which the sub-model should be built. (modelclasses) is a scalar or a vector of classes. | simcasub calculates a single SIMCA sub-model for inclusion into a SIMCA model. A SIMCA sub-model is a PCA model built only on samples of a given class (or classes) as identified in a DataSet object's class{1} field. Inputs are identical to PCA except a second input (modelclasses) follows the x-block specifying which class or classes on which the sub-model should be built. (modelclasses) is a scalar or a vector of classes. | ||
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NOTE: simcasub is not the usual route to perform SIMCA predictions - use PCA to do predictions from a SIMCA sub-model or SIMCA to do predictions from a SIMCA model. | NOTE: simcasub is not the usual route to perform SIMCA predictions - use PCA to do predictions from a SIMCA sub-model or SIMCA to do predictions from a SIMCA model. | ||
== Input == | ====Input==== | ||
* '''x''' = ''M ''x ''N'' matrix of class "dataset" where class information is extracted from x.class{1,1} and labels from x.label{1,1}. | * '''x''' = ''M ''x ''N'' matrix of class "dataset" where class information is extracted from x.class{1,1} and labels from x.label{1,1}. | ||
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* '''modelclasses''' = ''M ''x 1 vector of class identifiers where each element is an integer identifying the class number of the corresponding sample. | * '''modelclasses''' = ''M ''x 1 vector of class identifiers where each element is an integer identifying the class number of the corresponding sample. | ||
== Optional Inputs == | ====Optional Inputs==== | ||
* '''''ncomp''''' = integer, number of PCs to use in each model. This is rarely known ''a'' ''priori''. When ncomp=[] {default} the user is querried for number of PCs for each class. | * '''''ncomp''''' = integer, number of PCs to use in each model. This is rarely known ''a'' ''priori''. When ncomp=[] {default} the user is querried for number of PCs for each class. | ||
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*'''''options''''' = a structure array. This is the same as PCA. | *'''''options''''' = a structure array. This is the same as PCA. | ||
== Output == | ====Output==== | ||
* '''model''' = a PCA model | * '''model''' = a PCA model | ||
== See Also == | ===See Also=== | ||
[[pca]], [[plsda]], [[simca]] | [[pca]], [[plsda]], [[simca]] | ||
Latest revision as of 06:08, 17 September 2018
Purpose
simcasub calculates a single SIMCA Sub-model.
Synopsis
- model = simcasub(x,modelclasses,ncomp,options); %identifies model (calibration)
Description
simcasub calculates a single SIMCA sub-model for inclusion into a SIMCA model. A SIMCA sub-model is a PCA model built only on samples of a given class (or classes) as identified in a DataSet object's class{1} field. Inputs are identical to PCA except a second input (modelclasses) follows the x-block specifying which class or classes on which the sub-model should be built. (modelclasses) is a scalar or a vector of classes.
NOTE: simcasub is not the usual route to perform SIMCA predictions - use PCA to do predictions from a SIMCA sub-model or SIMCA to do predictions from a SIMCA model.
Input
- x = M x N matrix of class "dataset" where class information is extracted from x.class{1,1} and labels from x.label{1,1}.
- modelclasses = M x 1 vector of class identifiers where each element is an integer identifying the class number of the corresponding sample.
Optional Inputs
- ncomp = integer, number of PCs to use in each model. This is rarely known a priori. When ncomp=[] {default} the user is querried for number of PCs for each class.
- options = a structure array. This is the same as PCA.
Output
- model = a PCA model