archive-com.com » COM » E » EIGENVECTOR.COM Total: 295 Choose link from "Titles, links and description words view": Or switch to
"Titles and links view". |

- PLS_Toolbox FAQ

simple slope a and intercept b form y ax b using the Analysis menu item File Export Model To Regression Vector which uses the PLS Toolbox regcon function regcon REGCON Converts regression model to y ax b form regcon help gives extended help I O a b regcon mod However this function will only operate on models with simple autoscaling or mean centering More advanced preprocessing methods require either the Model Exporter add on product or custom programming to convert into a simple equation format Manually Extracting and Applying To manually extract the regression vector B and the intercept B0 these items would need to be extracted from the model object containing the regression model The following describes these fields and the preprocessing steps needed to be performed The regression vector is stored in the model reg field of the model b model reg B 0 is more subtle In PLS Toolbox the regression vector and intercept are stored in terms of preprocessed data and the intercept is effectively zero Preprocessing information in general can be found in model detail preprocessing 1 for the x block and model detail preprocessing 2 for the y block If you are using ONLY mean centering or autoscaling i e no other preprocessing steps the y block mean is stored in model detail preprocessing 2 1 out 1 This may look like a terribly complicated command but basically it is saying that from preprocessing for the y block 2 pull the first step 1 and return the first stored value out 1 This will be the mean from your y block or means for multi column y If you are using autoscaling there will also be a vector of standard deviations model detail preprocessing 2 1 out 2 Here are several simple situations and

Original URL path: http://eigenvector.com/faq/index.php?id=64 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

function Possible Solutions The conpred1 function was used for converting a PLS model into one regression vector The PLS functions in PLS Toolbox 3 0 report the regression vector directly so there is no need for conpred1 any more You can find it in the reg field of the model structure You may also want to try the regcon function which converts a model into a simple y ax b

Original URL path: http://eigenvector.com/faq/index.php?id=8 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

when using the Mathworks Neural Networks interface Possible Solutions The Mathworks Neural Networks tool uses a function named regression m as part of its functions This function conflicts with a function of the same name in PLS Toolbox Fortunately the regression function in PLS Toolbox is not used it is an outdated way to start the Analysis interface To solve the problem locate the regression m and possible regression p

Original URL path: http://eigenvector.com/faq/index.php?id=161 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

causes of this Preprocess Option Unassigned The most common cause is that you have not set the same preprocessing in the crossval function see the preprocessing option as you are using in the Analysis window You should use the preprocess function to define the same preprocessing you are using in Analysis and pass this into the crossval function via the preprocessing option Preprocessing Outside Cross Validation The next possible cause is that you are doing the preprocessing outside of cross validation Preprocessing should always be done inside of cross validation to help avoid over fitting models Autoscale Preprocess Shortcut Used Version 3 5 2 and earlier The third possible cause is only present when using Version 3 5 2 or earlier of PLS Toolbox and is related to using a preprocessing shortcut in the crossval preprocessing option When using autoscaling in cross validation you may observe different results if you analyze data in the Analysis window compared to the same data analyzed at the command line using the crossval m function This discrepency present through version 3 5 2 of PLS Toolbox is due to a bug in the preprocessing options of crossval m experienced only if you use the

Original URL path: http://eigenvector.com/faq/index.php?id=53 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

on Cross validation splits your data up into n subsets lets say 3 for simplicity Let say you have 12 samples and you re only doing mean centering as your preprocessing again for simplicity Cross validation is going to take your 12 samples and split it into 3 groups 4 samples in each group In each cycle of the cross validation the algorithm leaves out one of those 3 groups 4 samples validation set and does both preprocessing and model building from the remaining 8 samples calibration set Recall that the preprocessing step here is to calculate the mean of the data and subtract it Then it applies the preprocessing and model to the 4 sample validation set and looks at the error and repeats this for each of the 3 sets Here applying the preprocessing is to take the mean calculated from the 8 samples and subtract it from the other 4 samples That last part is the key to why preprocessing BEFORE crossval is bad when preprocessing is done INSIDE cross validation as it should be the mean is calculated from the 8 samples that were left in and subtracted from them and that same 8 sample mean is also subtracted from the 4 samples left out by cross validation However if you mean center BEFORE cross validation the mean is calculated from all 12 samples The result is that even though the rules of cross validation say that the preprocessing mean and model are supposed to be calculated from only the calibration set doing the preprocessing outside of cross validation means all samples are influencing the preprocessing mean With mean centering the effect isn t as bad as it is for something like GLSW or OSC These multivariate filters are far stronger preprocessing methods and operating on

Original URL path: http://eigenvector.com/faq/index.php?id=153 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

change the sign of the loadings returned by SVD Possible Solutions A PCA model is ambiguous with regards to sign flips i e if you have a set of scores T and loads P the model X TP is no more or less valid than the model X T P Further complicating the matter is the fact that MATLAB s SVD routine upon which our PCAENGINE is based sometimes flips

Original URL path: http://eigenvector.com/faq/index.php?id=12 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

If you build a model in Analysis GUI and at the command line using functions like PLS PCR PCA MLR etc you should get identical models assuming you use the same data If you observe differences they are usually caused by not selecting the same preprocessing For most analysis methods Analysis GUI defaults to autoscaling unless you change this default in the Analysis GUI settings To get an identical model

Original URL path: http://eigenvector.com/faq/index.php?id=116 (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

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 example on page 14 of the PLS Toolbox manual loading labels into the DECOMPOSE GUI gives the error Number of rows must number of columns of loaded data no labels loaded Possible Solutions There is a known typo in the manual The line on page

Original URL path: http://eigenvector.com/faq/index.php?id=2 (2016-04-27)

Open archived version from archive