Cross validation analysis

LOO cross-validation analysis of F-W QSAR models showed an overall agreement between predicted and experimental pICso for each individual combination of chemical series and protein target. 

SJ Cho, A Tropsha. Cross-validated R2-guided region selection for comparative molecular held analysis A simple method to achieve consistent results. J Med Chem 38 1060-1066, 1995. 

Validation results obtained from factor analysis of Table 31.2, containing the retention times of 23 chalcones in 8 chromatographic methods, after log double-centering and global normalization. The results are used in the Malinowski s f-test and in cross-validation by PRESS. 

The method of cross-validation is based on internal validation, which means that one predicts each element in the data set from the results of an analysis of the remaining ones. This can be done by leaving out each element in turn, which in the case of an nxp table would require nxp analyses. Wold [44] has implemented a scheme for leaving out groups of elements at the same time, which reduces the... 

Most of the supervised pattern recognition procedures permit the carrying out of stepwise selection, i.e. the selection first of the most important feature, then, of the second most important, etc. One way to do this is by prediction using e.g. cross-validation (see next section), i.e. we first select the variable that best classifies objects of known classification but that are not part of the training set, then the variable that most improves the classification already obtained with the first selected variable, etc. The results for the linear discriminant analysis of the EU/HYPER classification of Section 33.2.1 is that with all 5 or 4 variables a selectivity of 91.4% is obtained and for 3 or 2 variables 88.6% [2] as a measure of classification success. Selectivity is used here. It is applied in the sense of Chapter... 

L Stable and S. Wold, Partial least square analysis with cross-validation for the two-class problem a Monte Carlo study. J. Chemometrics, 1 (1987) 185-196. 

Traditional electrophoresis and capillary electrophoresis are competitive techniques as both can be used for the analysis of similar types of samples. On the other hand, whereas HPLC and GC are complementary techniques since they are generally applicable to different sample types, HPLC and CE are more competitive with each other since they are applicable to many of the same types of samples. Yet, they exhibit different selec-tivities and thus are very suitable for cross-validation studies. CE is well suited for analysis of both polar and nonpolar compounds, i.e. water-soluble and water-insoluble compounds. CE may separate compounds that have been traditionally difficult to handle by HPLC (e.g. polar substances, large molecules, limited size samples). 

To construct the reference model, the interpretation system required routine process data collected over a period of several months. Cross-validation was applied to detect and remove outliers. Only data corresponding to normal process operations (that is, when top-grade product is made) were used in the model development. As stated earlier, the system ultimately involved two analysis approaches, both reduced-order models that capture dominant directions of variability in the data. A PLS analysis using two loadings explained about 60% of the variance in the measurements. A subsequent PCA analysis on the residuals showed that five principal components explain 90% of the residual variability. 

The number of latent variables (PLS components) must be determined by some sort of validation technique, e.g., cross-validation [42], The PLS solution will coincide with the corresponding MLR solution when the number of latent variables becomes equal to the number of descriptors used in the analysis. The validation technique, at the same time, also serves the purpose to avoid overfitting of the model. 

The VolSurf method was used to produce molecular descriptors, and PLS discriminant analysis (DA) was applied. The statistical model showed two significant latent variables after cross-validation. The 2D PLS score model offers a discrimination between the permeable and less permeable compounds. When the spectrum color is active (Fig. 17.2), red points refer to high permeability, whereas blue points indicate low permeability. There is a region in the central part of the plot with both red and blue compounds. In this region, and in between the two continuous lines, the permeability prediction is less reliable. The permeability model... 

Claros (1995) released an attractive program, MitoProt. In this program, various sequence features of a potential signal region are reported to assist in the user s decision making. Later, an objective prediction method that combines many sequence features by the discriminant analysis was proposed (Claros and Vincens, 1996). With a cross-validation test, its accuracy was estimated to be 75%. 

A risk with this approach is that if there are a large number of constant non-zero entries in the data matrix, they can act as binary variables and perhaps weight the analysis toward yielding trivial results. When cross validation is used, however, this risk is reduced. 

Altria, K. D., Harden, R. C., Hart, M., Hevizi, J., Hailey, P. A., Makwana, J. V., and Portsmouth, M. J. (1993). Inter-company cross-validation exercise on capillary electrophoresis. I. Chiral analysis of... 

K.H. Esbensen and T.T. Lied, Principles of image cross-validation (ICV) representative segmentation of image data structures, in Techniques and Applications of Hyperspectral Image Analysis, H. Grahn and P. Geladi (eds). Chap. 7. (155-180), John Wiley Sons, Ltd, Chichester, 2007. 

In 2000 two major petrochemical companies installed process NMR systems on the feed streams to steam crackers in their production complexes where they provided feed forward stream characterization to the Spyro reactor models used to optimize the production processes. The analysis was comprised of PLS prediction of n-paraffins, /xo-paraffins, naphthenes, and aromatics calibrated to GC analysis (PINA) with speciation of C4-C10 for each of the hydrocarbon groups. Figure 10.22 shows typical NMR spectral variability for naphtha streams. Table 10.2 shows the PLS calibration performance obtained with cross validation for... 

Unlike test set validation methods, cross-validation methods attempt to validate a model using the calibration data only, without requiring the preparation and analysis of an additional test set of samples. This involves the execution of one or more internal validation procedures (hereby called subvalidations), where each subvalidation involves three steps ... 

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