ISSN:
0886-9383
Keywords:
Partial least squares
;
Monte Carlo methods
;
Cross validation
;
Chemistry
;
Analytical Chemistry and Spectroscopy
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
Notes:
Partial least squares (PLS) regression is a commonly used statistical technique for performing multivariate calibration, especially in situations where there are more variables than samples. Choosing the number of factors to include in a model is a decision that all users of PLS must make, but is complicated by the large number of empirical tests available. In most instances predictive ability is the most desired property of a PLS model and so interest has centred on making this choice based on an internal validation process. A popular approach is the calculation of a cross-validated r2 to gauge how much variance in the dependent variable can be explained from leave-one-out predictions. Using Monte Carlo simulations for different sizes of data set, the influence of chance effects on the cross-validation process is investigated. The results are presented as tables of critical values which are compared against the values of cross-validated r2 obtained from the user's own data set. This gives a formal test for predictive ability of a PLS model with a given number of dimensions.
Additional Material:
5 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/cem.1180070407
