Tools ModelRobustness and Release Notes Version 6 7: Difference between pages

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[[TableOfContents|Table of Contents]] | [[Tools_Cross-Validation|Previous]] | [[Tools_CorrelationMap|Next]] | [[Index|Index]]


==Model Robustness Tool==


You use the Model Robustness tool to measure the sensitivity of a regression model to artifacts in new spectroscopic measurements. To open the Model Robustness tool, on the Analysis window, click Tools > Model Robustness, and then click Shifts or Interferences.
Version 6.7 of PLS_Toolbox and Solo was released in March, 2012.


===Shifts===
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].


The Shifts option measures the sensitivity of a regression model to shifts in x-axis data that are caused by instrument instability-that is, if you have an instrument that is not particularly stable or reproducible over time, what is the impact on predictions using the given model? The Shifts plot is a three-dimensional plot that details the RMSEP (Root Mean Squared Error of Prediction) for the model as a function of shift, where shift is described in terms of the number of variables and the Smoothing window.
(back to [[Release Notes PLS Toolbox and Solo]])


''Example of a Shifts plot''
==NEW FEATURES==


[[Image:Tools_ModelRobustness.29.1.1.jpg|417x359px]]
===Design Of Experients (DOE) Tools===
* bla


===Other Method Improvements===
* bla
 
===Preprocessing===
* bla
 
===Import / Export===
* bla
 
===DataSet Editor===
* bla


Consider the figure above, which shows the model robustness for a regression model with an RMSEC (Root Mean Squared Error of Calibration) of approximately 0.5. As shown in this figure:
===Plot Controls===
* Selectable plot themes. [[Image:Figbg_color.gif|thumb|| ]]
* bla


* 1 - Without shift and without smoothing of the variables, the RMSEP indicates that you have test data that is identical to your calibration data and you have performance that is on par for the RMSEC of the model
===[[AnalysisWindow_Layout|Analysis Window]]===
* Show CV results for confusion table when available.
* Improved [[modelcache]] performance and string handling.


* 2 - Shifting a spectrum over by simply one variable increases the RMSEP for the model by almost twelve orders of magnitude, from 0.5 to almost 60.  
===[[WorkspaceBrowser_Layout|Workspace Browser]] ===
* Drag Drop - Enable dragging and dropping of system files onto the Browser window as well as to Analysis window. Enhanced highlighting of drop location.


* 3 - With a combination of shifting and smoothing, the impact on the model is lessened somewhat.
===Command-line Tool Changes===
* bla


===Interferences===


The Interferences option measures the sensitivity of a regression model to the location and width of a new peak in test data-that is, if you have a chemical entity that is present in the test data but that was not reflected in the calibration data, what is the impact on predictions using the given model? The Interferences plot is a three-dimensional plot that details the RMSEP (Root Mean Squared Error of Prediction) for the model as a function of a new peak, where the peak is described in terms of its width and location.
==NEW FUNCTIONS AND FILES==


''Example of an Interferences plot''
*''Full Support for Matlab R2012a''


[[Image:Tools_ModelRobustness.29.1.2.jpg|418x366px]]
===Misc New Functions===
:[[autoexport]] - Exports a DataSet object to a file of the specified format.
:[[classsummary]] - List class and axisscale distributions for a DataSet.
:[[figuretheme]] - Resets a figure background and axes to a specified color.
:[[meantrimmed]] - Trimmed mean.
:[[mediantrimmed]] - Trimmed median.
:[[windowfilter]] - Spectral filtering.


===Design of Analysis Tools===
:[[anovadoe]]        - Function to perform ANOVA for 2^k factorial model X, Y data


===Other Changes===


Consider the figure above, which shows the model robustness for a regression model with an RMSEC (Root Mean Squared Error of Calibration) of approximately 0.5. As shown in this figure, the RMSEP for the model can be impacted in one of three ways:
:[[crossval]] - improved display of classification results, improved integration with model input
 
* 1 - An interferant area where there is virtually no impact on the RMSEP for the model, no matter how wide the interfering peak is.
 
* 2 - An interferant area where there is a slight impact on the RMSEP for the model, but the impact is lessened as the width of the peak increases.
 
* 3 - An interference area where there is a significant impact on the RMSEP for the model, but the impact is lessened as the width of the peak increases.

Revision as of 10:36, 7 March 2012


Version 6.7 of PLS_Toolbox and Solo was released in March, 2012.

For general product information, see PLS_Toolbox Product Page. For information on Solo, see Solo Product Page.

(back to Release Notes PLS Toolbox and Solo)

NEW FEATURES

Design Of Experients (DOE) Tools

  • bla

Other Method Improvements

  • bla

Preprocessing

  • bla

Import / Export

  • bla

DataSet Editor

  • bla

Plot Controls

Analysis Window

  • Show CV results for confusion table when available.
  • Improved modelcache performance and string handling.

Workspace Browser

  • Drag Drop - Enable dragging and dropping of system files onto the Browser window as well as to Analysis window. Enhanced highlighting of drop location.

Command-line Tool Changes

  • bla


NEW FUNCTIONS AND FILES

  • Full Support for Matlab R2012a

Misc New Functions

autoexport - Exports a DataSet object to a file of the specified format.
classsummary - List class and axisscale distributions for a DataSet.
figuretheme - Resets a figure background and axes to a specified color.
meantrimmed - Trimmed mean.
mediantrimmed - Trimmed median.
windowfilter - Spectral filtering.

Design of Analysis Tools

anovadoe - Function to perform ANOVA for 2^k factorial model X, Y data

Other Changes

crossval - improved display of classification results, improved integration with model input