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- Spectra Editor Function for MATLAB - Eigenvector Research, Inc.

file nir data was loaded into the MATLAB workspace as shown in the upper figure The file contains 30 NIR spectra of pseudo gasoline samples measured on two spectrometers spec1 and spec2 along with concentration information of several analytes conc and a wavelength axis lamda The specedit function is called with one of the sets of spectra and the wavelength axis as arguments This produces a figure window with push buttons for activating the cursor so that one may select and deselect regions of the spectra note that only the mean spectra is displayed Selected regions are indicated by the small red dots The edited spectra and wavelength axis indices can be saved to the workspace using dialog boxes generated by the svdlgpls function that are activated by the push buttons The zoompls pushbuttons in the lower right of the figure can be used to zoom in on portions of the plot for better accuracy when selecting spectral features Example of Command Window when using SPECEDIT Example of Figure Produced by SPECEDIT Requirements for running SPECEDIT MATLAB 5 0 zoompls m and svdlgpls m functions No other toolboxes required Must be a registered PLS Toolbox user Developed by Neal B

Original URL path: http://eigenvector.com/MATLAB/SpecEdit.html (2016-04-27)

Open archived version from archive - Variance Captured Function - Eigenvector Research, Inc.

varcap m calculates the percent variance of each original variable in a PCA model and outputs the results graphically and to the workspace The figure below shows an example of the graphical output Here the function was used on a 5 PC model of the NIR spectra in the nir data mat file in the PLS Toolbox The PCA model was built on mean centered spectra The function varcap was called from the command line vc varcap mcx loads lamda where mcx was the mean centered spectra loads are the loadings vectors from a PCA model and lamda was the axis for plotting against in this case the wavelength The output is the matrix of variance captured vc and the plot below The plot shows the cumulative amount of variance captured for each variable with each PC shown as a different color It can be seen for instance that the model captures almost all of the variation in the data in the 1200 1300 nm range but less than half around 880 nm The second PC light blue is very important for describing variation at 1220 nm while the fifth PC reddish brown mostly describes variance around 880 nm Requirements

Original URL path: http://eigenvector.com/MATLAB/VarCap.html (2016-04-27)

Open archived version from archive - Missing Data Functions - Eigenvector Research, Inc.

data sets with missing values Each of the functions uses a flag to indicate the location of missing data They can be used with the PLS Toolbox function for PCA with missing data mdpca m prior to using it Requirements for running missing data scaling functions MATLAB 5 0 No other toolboxes required Must be a registered PLS Toolbox user Developed by Barry M Wise Eigenvector Research Inc bmw eigenvector

Original URL path: http://eigenvector.com/MATLAB/MissDatFuns.html (2016-04-27)

Open archived version from archive - Normalizing Function - Eigenvector Research, Inc.

Online Courses Resources Contact Us Search Site Search for Function for Normalizing Sample Vectors to Unit Length The function normaliz m can be used to normalize samples rows to vectors of unit length The function prints out an error message if any samples samples of zero norm are found Requirements for running normalize MATLAB 4 2 or 5 0 No other toolboxes required Developed by Barry M Wise Eigenvector Research

Original URL path: http://eigenvector.com/MATLAB/Normalize.html (2016-04-27)

Open archived version from archive - Standardization for Non-Square Systems - Eigenvector Research, Inc.

used to predict a single channel on the standard instrument The function will pick the channels such that the channel to be predicted in the standard instrument is as close as possible to being in the center of the window of channels in the instrument to be standardized If the window width is set to zero a variant of the direct method of standardization will be used The stdize m

Original URL path: http://eigenvector.com/MATLAB/StdGenNS.html (2016-04-27)

Open archived version from archive - Figures of Merit Code - Eigenvector Research, Inc.

recent article by Lorber Faber and Kowalski Anal Chem Vol 69 No 8 April 15 1997 The inputs are the preprocessed usually centered and scaled spectral data the preprocessed analyte data and the PCR or PLS approximation to x Rhat Generally Rhat is found by multiplying the scores by the loadings from PLS or PCR using the number of LVs or PCs in the corresponding calibration model The outputs are the matrix of net analyte signals for each of the spectra the norm of the net analyte signal for each sample nnas the matrix of sensitivities for each sample sens the vector of selectivities for each sample sel and the noise filtered estimate of the net analyte signal nfnas which is just the multiple of the regression vector that best fits the nas For example given the 7 LV PLS model formed using the PLS function in the PLS Toolbox reg ssq p q w t u b pls x y 7 Rhat t p nas nnas sens sel nfnas figmerit x y Rhat Note that in later releases of PLS Toolbox the figmerit function is included and the I O is different Please see the figmerit documentation page for

Original URL path: http://eigenvector.com/MATLAB/FigMerit.html (2016-04-27)

Open archived version from archive - Regression Model Conversion to y = Ax + b

convert regression models to the y ax b form for use with unsclaed data required for many control systems Normally PLS and PCR models are stored with the regression vector as it is applied to the scaled data The regcon m function can convert models developed by the PLS Toolbox functions modlmker m or modlgui m or from the individual pieces of the model such as the regression vector x

Original URL path: http://eigenvector.com/MATLAB/regcon.html (2016-04-27)

Open archived version from archive - PLS_Toolbox FAQ

FAQ Browse Documentation Wiki Browse EigenGuide Videos Search for Keyword s Issue How do I concatenate multiple files into a single DataSet Possible Solutions If you want to combine two or more files of data into a single DataSet for analysis follow the directions shown in these two EigenGuide movies The first movie assumes you have not yet imported the data into Solo or PLS Toolbox and shows you how

Original URL path: http://eigenvector.com/faq/index.php?id=130 (2016-04-27)

Open archived version from archive