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Table 2 Linear model fit summary

From: rapmad: Robust analysis of peptide microarray data

  Df Sum Sq Mean Sq F value Pr(>F)
Peptide 12 170916 14243.0 57461.65 < 2.2e-16
Subarray 2 24 12.2 49.18 < 2.2e-16
Needle 15 45 3.0 12.08 < 2.2e-16
Row 234 458 2.0 7.89 < 2.2e-16
Column 75 409 5.5 22.03 < 2.2e-16
Residuals 2218 550 0.2   
  1. Linear model fit summary. The F-values and the corresponding probabilities (Pr(>F)) clearly indicate that all explanatory variables used are highly significant and that each contributes to reducing the variance present in the arrays. The peptide sequence itself shows by far the strongest effect while requiring only twelve degrees of freedom (Df), it shows a sum of squares for its effect (Sum Sq) of 170,916, much stronger than the residuals sum of squares of 550. While not as strong as the peptide effect, the remaining explanatory effects reduce the residuals by 65%. Column and row effects are strongest, but subarray and needle effects require fewer degrees of freedom, resulting in strong mean sum of squares (Mean Sq) values.