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PLS regression or discriminant analysis, with leave-one-out cross-validation and prediction. 1.0

  Date Added: July 12, 2013  |  Visits: 202

PLS regression or discriminant analysis, with leave-one-out cross-validation and prediction.

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Leave-one-out cross-validation for PLS regression or discriminant analysispls_cv = plscv(x,y,vl,'da')input:x (samples x descriptors) for cross-validationy (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0.vl (1 x 1) number of latent variables to compute in cross-validation'da' (char) to indicate PLS-discriminant analysis (in PLS regression it is no used)output:pls_cv struct with:Ypcv (samples x variables x vl) predicted variables or (samples x classes x vl) predicted classes for cross-validationTcv (samples x vl) x-scores for cross-validation samplesFor PLS-R:RMSEcv (variables x vl) Root Mean Square Error for cross-validationR2cv (variables x vl) Correlation Coefficient for cross-validationFor PLS-DA:Succv (1 x vl) Success (%) of classification for cross-validation----------------------------------------------------------------------------Model for Partial Least Squares regression or discriminant analysispls_model = pls(x,y,vl,'da')input:x (samples x descriptors) for calibrationy (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0vl (1 x 1) number of latent variables to model'da' (char) to indicate PLS-discriminant analysis (in PLS regression it is no used)output:pls_model struct with:Data (struct) X and Y input, and classes (for PLS-DA)VLvar (2 x vl) cumulative variance (%) explained by model for X and Y.Ypc (samples x variables) predicted variables or (samples x classes) predicted classes for calibration samplesT (samples x vl) x-scoresP (descriptors x vl) x-loadingsW (descriptors x vl) x-weightsU (samples x vl) y-scoresQ (variables x vl) y-loadingsB (descriptors x variables) regression vectorsB0 (1 x 1) regression intercept for (mean(y,1))-(mean(x,1))Lo (samples x vl) samples leveragesLv (variables x vl) variables leveragesFor PLS-R:RMSEc (1 x variavles) Root Mean Square Error for calibrationR2c (1 x variavles) Correlation Coefficient for calibrationRMSEc_Yrand (1 x variavles) RMSEc for Y-randomization test (mean of 10 shuffles)R2c_Yrand (1 x variavles) R2c for Y-randomization test (mean of 10 shuffles)For PLS-DA:Succ (1 x 1) Success (%) of classification for calibration samplesSucc_Yrand (1 x 1) Success (%) of classification for Y-randomization test-----------------------------------------------------------------------------Variables or classes prediction using PLS modelpls_pred = plspred(x,model,y)input:x (samples x descriptors) new samples for predictionmodel (struct) with PLS calibration parametersy (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0. (optional for model test)output:pls_pred struct with:Yp (samples x variables) predicted variables or (samples x classes) predicted classes for new samplesTp (samples x vl) x-scores for new samplesFor PLS-R:RMSEp (1 x variavles) Root Mean Square Error for prediction (only if 'y' is supplied)R2p (1 x variavles) Correlation Coefficient for prediction (only if 'y' is supplied)For PLS-DA:Sucp (1 x 1) Success (%) of classification for prediction (only if 'y' is supplied)

Requirements: No special requirements
Platforms: Matlab
Keyword: Explained Intercept Meany Meanx Plsxyvl Variance Vectors Xloadings Xweights Yloadings Yscores
Users rating: 0/10

License: Shareware Size: 10 KB
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