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  • PLS_Toolbox FAQ
    Matlab based Eigenvector Research products you can use the following instructions to compile your application including the licensed Eigenvector Research EVRI code If you were not supplied an evrilicense lic file by EVRI create one by copying the license code supplied for your compilation license found on the download tab of your EVRI account into a plain text file named evrilicense lic The file should consist of the license code on a single line of the file For example 12345678 98765432 ab 1234 1234 Copy the evrilicense lic file into one of the folders on your Matlab path This could be either one of the PLS Toolbox folders or your application s folder Add the evrilicense lic file to the Shared Resources list in the Matlab project builder This will assure that the EVRI license gets included in the compiled application Compile your application as usual using Mathworks standard instructions The Matlab dependency logic will automatically include the PLS Toolbox functions in your compiled application See note below regarding blocking certain functions from being included Blocking Unnecessary Functions By default Matlab s compiler automatically identifies all m files which are necessary to run your application and includes all of these

    Original URL path: http://eigenvector.com/faq/index.php?id=139 (2016-04-27)
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  • PLS_Toolbox FAQ
    the Custom method in the Cross Validation window is chosen then the user will be asked to specify a custom cross validation vector that specifies the subsets of objects to be placed in each test subset for each sub validation This custom cross validation vector must be a vector of integers with dimensionality 1 x n where n is the total number of objects in the currently loaded data set The values within this vector must adhere to the following set of rules A value of 2 indicates that the object is placed in every test set never in a model building set A value of 1 indicates that the object is placed in every model building set never in a test set A value of 0 indicates that the object is used for neither model building nor model testing Values of 1 2 3 indicate the test set number for each object for those objects that are used in the cross validation procedure For example for a data set containing 9 objects a custom cross validation array of 1 0 1 1 2 2 2 0 1 will result in two sub validation experiments one using objects 3 4

    Original URL path: http://eigenvector.com/faq/index.php?id=107 (2016-04-27)
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  • PLS_Toolbox FAQ
    44 for a class 1 sample is equal to the probability of measuring a value of 0 44 for a class 0 sample Because the equation above normalizes these probabilities we would say that a sample giving a y value of 0 44 has a 50 chance of being in class 1 or 0 Two more examples there is a small non zero probability of measuring a value of 0 40 for a class 1 sample but a larger probability of measuring 0 40 for a class 0 sample Again normalizing we get 10 and 90 prob of sample being class 1 or class 0 respectively A value of 0 8 however has effectively a zero probability of being observed for a class 0 sample the distribution fit to the class 0 samples has dropped to near zero out this far This means that the probability that a sample giving a y value of 0 8 is in class 1 is essentially 100 Another technical description Given two groups of samples A and B assume we have a PLSDA model which was designed to separate the two groups using a y block where each group A sample is assigned a zero and each group B sample is assigned a one The estimated y values i e y values predicted on the calibration set for each group using that model call them y est A and y est B will have some finite range around zero and one respectively We can fit y est A and y est B using two separate distribution functions one which describes the y values we would expect from the entire population of A samples and one which describes the entire population of B samples For simplicity the algorithm assumes Gaussian distributions of the estimated values This allows us to simply take the standard deviation and mean of y est A and y est B and use those to construct two Gaussian profiles that we assume are close to representing the true profiles of all samples in the populations of A and B note The math up to this point is simply the mean and standard deviation equations the standard equation of a gaussian This allows us to calculate the probability of observing a value of y given a sample from group A P y A dist A 1 sqrt 2 pi std A exp 0 5 y mean A std A 2 where std A and mean A are the standard deviation and mean of group A respectively Repeat this for B to get P y B P y B dist B 1 sqrt 2 pi std B exp 0 5 y mean B std B 2 To calculate the probability for any value of y we assume that a sample for which we ve made a prediction is definitely one of the two groups one should use model residuals and Hotelling s T 2 to eliminate samples which are not safely predicted using the

    Original URL path: http://eigenvector.com/faq/index.php?id=38 (2016-04-27)
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  • PLS_Toolbox FAQ
    8 GB or more of memory can be purchased very inexpensively Such systems can easily run analyses of hundreds of millions of elements without having to take memory saving precautions outlined below Suggestion 2 If you have only a 32 bit system have EVERYTHING else closed except Matlab or Solo and follow the following guidelines note several of these mention images as this type of data is one of the biggest challenges obviously the image specific suggestions can be ignored if you aren t analyzing image data Use care when using graphical user interfaces GUIs Whether using Solo where you can only use GUIs or using Matlab and PLS Toolbox and choosing to use the GUIs you need to be aware that the GUI gives you less control over memory requirements Here s some general hints for GUI use Import your data DIRECTLY into the Analysis interface OR make sure you delete it from the base workspace or Image Manager window if you used that after moving it into Anaylsis This will assure you do not have multiple copies of the raw data in memory Preprocess your data in advance and use the Preprocess Save Preprocessed data menu option to save the data to a disk file Then load that preprocessed data from the disk file as X and set the preprocessing to none Use Hard Delete Excluded from the DataSet Editor s edit menu after excluding variables you do NOT want to include in your model For image data use the Crop tool in the Image Manager before analyzing the data to drop spatial regions you don t want to analyze see note above about closing the Image Manager before doing the analysis To make the above choices less cumbersome set the Model Cache s maxdatasize option to inf

    Original URL path: http://eigenvector.com/faq/index.php?id=98 (2016-04-27)
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  • PLS_Toolbox FAQ
    Browse Documentation Wiki Browse EigenGuide Videos Search for Keyword s Issue I keep getting an error with Plotgui Unrecognized Property or Mode Keyword functionname What should I do Possible Solutions This is a versioning problem with the plotgui m file in PLS Toolbox 3 5 4 It contains a line of code meant for version 4 0 of PLS Toolbox To fix the problem install the following patch by deleting

    Original URL path: http://eigenvector.com/faq/index.php?id=70 (2016-04-27)
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  • PLS_Toolbox FAQ
    data are all negative A nonnegative solution would probably be something which had some loading vectors that were all zero For mathematical reasons this can not be allowed because the rank goes down The algorithm when struggling therefore tries to make the model as nonnegative as possible without reaching a situation where one whole loading vector is all zero This can happen when you use too many components or have

    Original URL path: http://eigenvector.com/faq/index.php?id=124 (2016-04-27)
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  • PLS_Toolbox FAQ
    EigenGuide Videos Search for Keyword s Issue Some of the items in my plots and figures don t appear correctly or are cutoff by the figure Possible Solutions This problem can occur when a figure plot is first rendered when a figure is moused over and or when a toolbar is added removed The problem appears to be with Matlab and the MS Windows system Display Properties To recreate the behavior with toolbar add remove figure set 1 toolbar figure drawnow set 1 toolbar none drawnow set 1 toolbar figure drawnow set 1 toolbar none drawnow set 1 toolbar figure drawnow set 1 toolbar none drawnow Executing the above commands cause the figure to slightly grow or shrink depending on the state of your display settings To fix the problem try variations of the steps below 1 Start Matlab 2 Open Windows Display Properties right click on your desktop and select properties 3 Under the Appearance tab change the Windows and buttons drop down menu from its current setting to the next e g Windows XP style to Windows Classic style 4 Click Apply 5 Change the Windows and buttons selection back to its original setting 6 Click Apply and

    Original URL path: http://eigenvector.com/faq/index.php?id=48 (2016-04-27)
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  • PLS_Toolbox FAQ
    Online Courses Resources Contact Us Search Site Search for FAQ Frequently Asked Questions Browse FAQ Browse Documentation Wiki Browse EigenGuide Videos Search for Keyword s Issue The toolbar buttons in the Analysis figure don t show up correctly after I update to Matlab R2006a What can I do Possible Solutions This is a problem with the new versioning terminology used in Matlab 2006a To correct the problem replace your existing

    Original URL path: http://eigenvector.com/faq/index.php?id=71 (2016-04-27)
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