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Table 1 Fit of modeled to observed data for three methods: SPCovR, spls, and SGCCA

From: Obtaining insights from high-dimensional data: sparse principal covariates regression

Method

VAF

\(r(\hat {y},y)^{2}\)

\(r(\hat {y}_{2007},y_{2007})^{2}\)

SPCovR

0.19

0.42

0.79

spls

 

0.99

0.55

SGCCA

0.11

1

0.53

  1. Displayed are the variance accounted for by the components in the block of covariates and the squared correlation between the modeled and observed outcome for the 2008 and 2007 season. The model was constructed using the 2008 data
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BMC Bioinformatics

ISSN: 1471-2105

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