A TEST OF SIGNIFICANCE FOR PARTIAL LEAST SQUARES REGRESSION   总被引：1，自引：0，他引：1  

IAN N. WAKELING  JEFF J. MORRIS  AFRC Institute of Food Research  Earley Gate  Whiteknights Ro  Reading RG EF  U.K.Zeneca Pharmaceuticals  Mereside  Alderley Park  Macclesfiel  Cheshire SK TG  U.K. 《地理学报(英文版)》1993,(4)

Partial least squares (PLS) regression is a commonly used statistical technique for performingmultivariate calibration, especially in situations where there are more variables than samples. Choosingthe number of factors to include in a model is a decision that all users of PLS must make, but iscomplicated by the large number of empirical tests available. In most instances predictive ability is themost desired property of a PLS model and so interest has centred on making this choice based on aninternal validation process. A popular approach is the calculation of a cross-validated r~2 to gauge howmuch variance in the dependent variable can be explained from leave-one-out predictions. Using MonteCarlo simulations for different sizes of data set, the influence of chance effects on the cross-validationprocess is investigated. The results are presented as tables of critical values which are compared againstthe values of cross-validated r~2 obtained from the user's own data set. This gives a formal test forpredictive ability of a PLS model with a given number of dimensions.  相似文献