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Table 2 The average numbers of selected variables in the layers for each method of OPLSR, according to \(\hat {\beta }_{OPLSa}\) (denoted by OPLSR a) and \(\hat {\beta }_{OPLSb}\) (denoted by OPLSR b), and FDR; and significance level α, meaning αf for OPLSR b and q-value for FDR, are shown. This shows that OPLSR a found more variables consistently while OPLSR b worked comparably to FDR

From: Feature selection using distributions of orthogonal PLS regression vectors in spectral data

α   0.01   0.05   0.10
   Layer   Layer   Layer
   1 2 3   1 2 3   1 2 3
OPLSR a   24.4 0.837 0.580   25.0 3.79 5.61   25.1 5.32 10.0
OPLSR b   18.1 0.190 0.137   20.8 0.987 1.24   22.9 2.85 3.68
FDR   21.3 0.020 0.000   22.1 0.100 0.027   22.8 0.103 0.010
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