Release Notes Version 8 5 and Faq how are error bars calculated regression model: Difference between pages

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===Issue:===


==Version 8.5==
How are the error bars calculated for a regression model and can they be related to a confidence limit (confidence in the prediction)?
Version 8.5 of PLS_Toolbox and Solo was released in September, 2017.


For general product information, see [http://www.eigenvector.com/software/pls_toolbox.htm PLS_Toolbox Product Page]. For information on Solo, see [http://www.eigenvector.com/software/solo.htm Solo Product Page].
===Possible Solutions:===


(back to [[Release Notes PLS Toolbox and Solo]])
The error bars reported for inverse least squares models (and from the [[Ils_esterror]] function) represent the estimation error for each prediction, see:


==Overview==
Faber, N.M. and Bro, R., Chemomem. and Intell. Syst., 61, 133-149 (2002)


* The primary focus of version 8.5 has been in Calibration Transfer and additional importers.
They can be read as a standard deviation of the estimate. However because the underlying distribution is not clearly known (and is a matter of research), a confidence limit is not reported.  


* PLS_Toolbox/Solo 8.5 has been tested extensively with Matlab 2017b prerelease and should be compatible with the coming release.


* Simplified interface design changes. Various controls have been moved to simplify interfaces. Seldom used settings have been moved to options in several cases (e.g., simplified [[Svm|SVM]] panel).
'''Still having problems? Please contact our helpdesk at [mailto:helpdesk@eigenvector.com helpdesk@eigenvector.com]'''


* The Analysis window's confusion matrix and table toolbar icon now returns classification results for both [http://wiki.eigenvector.com/index.php?title=Sample_Classification_Predictions#Class_Pred_Most_Probable 'most probable'] and [http://wiki.eigenvector.com/index.php?title=Sample_Classification_Predictions#Class_Pred_Strict  'strict'] classifications. In the 'strict' case it also lists the 'strictthreshold' value used.
[[Category:FAQ]]
 
==Calibration Transfer==
* [[MCCTTool | New Model-centric Calibration Transfer]] - Interface to develop instrument specific models with calibration transfer.
 
* [[nlstd| NLSTD]] - Create or apply non-linear instrument transfer models (PLS_Toolbox only).
 
* [[sstcal| SST]] - Spectral Subspace Transformation calibration transfer.
 
* [[Demonstration_Datasets|Corn DSO]] - New calibration transfer demonstration data set added. 80 samples of corn measured on 3 different NIR spectrometers with moisture, oil, protein and starch values for each of the samples is also included.
 
==Importers==
 
* [[shimadzueemreadr]] - Imports Shimadzu EEM formatted text files.
* [[visionairxmlreadr]] - Imports Vision Air formatted XML files (X- & Y-Blocks).
* [[pltreadr]] - Imports Vision Air model files (.plt).
 
* [[aqualogreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
 
* [[jascoeemreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
 
* [[hitachieemreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
 
* [[rawread|RAWREAD]] - Added support for new version format (3 & 4) (See MIA_Toolbox).
 
* [[spgreadr|SPGREADR]] Added new feature, options.spectrumindex now can be an integer, an array of integers (indices, & order doesn't matter), or 'all'. When loading multiple files, options.spectrumindex must be either a single value or 'all'.
 
==Exporters==
 
* [[writeplt]] - Exports EVRI Model structures to Vision Air PLT files.
 
==Other Features and Improvements==
* [[Cooksd| Cook's Distance]] - Calculates Cooks Distance for samples in a regression model.
 
* [[rpls| RPLS]] - A recursive PLS and PCR variable selection algorithm.
 
* [[manhattandist| MANHATTANDIST]] - Calculate Manhattan Distance between rows of a matrix.
 
* [[Confusionmatrix]] and [[Confusiontable]] - Classification results formatting options added. Can now specify use of mostprobable or strict classification rule.
* [[calcdifference]] - Calculate difference between two datasets.
* Added GLS Weighting to model optimizer.

Revision as of 12:07, 5 December 2018

Issue:

How are the error bars calculated for a regression model and can they be related to a confidence limit (confidence in the prediction)?

Possible Solutions:

The error bars reported for inverse least squares models (and from the Ils_esterror function) represent the estimation error for each prediction, see:

Faber, N.M. and Bro, R., Chemomem. and Intell. Syst., 61, 133-149 (2002)

They can be read as a standard deviation of the estimate. However because the underlying distribution is not clearly known (and is a matter of research), a confidence limit is not reported.


Still having problems? Please contact our helpdesk at helpdesk@eigenvector.com