Pcapro: Difference between revisions
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* '''newdata''' = data to be applied to the existing PCA model | * '''newdata''' = data to be applied to the existing PCA model | ||
* The PCA model, which can be inputs in one of two forms, 1) as a list of input variables | * The PCA model, which can be inputs in one of two forms, 1) as a list of input variables or 2) as a single model structure variable. These two cases are summarized below: | ||
: 1) list of input variables: | : 1) list of input variables: |
Revision as of 14:14, 8 October 2008
Purpose
Project new data onto an existing principal components model.
Synopsis
- [scoresn,resn,tsqn] = pcapro(newdata,loads,ssq,reslm,tsqlm,plots)
- [scoresn,resn,tsqn] = pcapro(newdata,pcamod,plots)
Description
This function applies a previously-determined PCA model to a set of new data newdata. The PCA model can be input in one of two possible forms: 1) as a list of input variables, or 2) as a single model structure variable that had been previously returned by analysis or pca.
Inputs
- newdata = data to be applied to the existing PCA model
- The PCA model, which can be inputs in one of two forms, 1) as a list of input variables or 2) as a single model structure variable. These two cases are summarized below:
- 1) list of input variables:
- * newdata = data to be applied to the existing PCA model, scaled the same as the original data used to construct the model
- * loads = the model loadings
- * ssq = the model variance information
- * reslm = the limit for Q residuals
- * tsqlm = the limit for T2
- * plots = optional variable, which suppresses plotting when set to 0 {default plots ? 1}.
WARNING: Scaling for newdata should be the same as original data used to create the PCA model!
- 2) single model structure:
- * newdata = data to be applied to the existing PCA model,in the units of the original data
- * pcamod = the structure variable that contains the PCA model pcamod
- and an optional variable plots which suppresses the plots when set to 0 {default plots ???}.
NOTE: newdata will be preprocessed in PCAPRO using information stored in pcamod (pcamod.detail.preprocessing).
Outputs
- scoressn = the new scores
- resn = new residuals
- tsqn = new T2 values